MEDIA IMAGERY AND POLITICAL CHOICE: HOW VISUAL CUES INFLUENCE THE CITIZEN NEWS DIET
A DISSERTATION SUBMITTED TO THE DEPARTMENT OF COMMUNICATION AND THE COMMITTEE OF GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
Laura Ann Granka May 2010
© 2011 by Laura Ann Granka. All Rights Reserved. Re-distributed by Stanford University under license with the author.
This work is licensed under a Creative Commons Attribution- Noncommercial 3.0 United States License. http://creativecommons.org/licenses/by-nc/3.0/us/
This dissertation is online at: http://purl.stanford.edu/mk309wn1650
ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.
Shanto Iyengar, Primary Adviser
I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.
James Fishkin
I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.
Simon Jackman
I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.
Clifford Nass
Approved for the Stanford University Committee on Graduate Studies. Patricia J. Gumport, Vice Provost Graduate Education
This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file in University Archives.
iii iv ABSTRACT
Consumption of current affairs information now takes place on a range of different devices—web browsers, mobile phones, and tablets, to name a few beyond traditional analog media—and media owners have responded by offering news content in a variety of formats beyond mere text display. It is common for news interfaces to include graphics, animated slideshows, and other visual features alongside news content. Yet, relatively little research has assessed how these novel formats affect news audiences, specifically with regard to news reading and selection behaviors. The research reported herein examines which factors influence (i) an individual’s preferences for hard or soft news, (ii) an individual’s preferences for specific news sources, and (iii) how stable or susceptible to change these preferences are over time.
STUDY 1: SELECTIVE EXPOSURE TO HARD AND SOFT NEWS The first level of analysis is to determine which individual characteristics are key predictors of consuming substantive news content, and which qualities are more likely to lead to soft news consumption. While this question is not new, this study provides several unique contributions. The first contribution addresses the dispute about whether self-report data (e.g., surveys) provides an accurate measurement of actual news consumption. Through an online experiment and survey with 1,000 participants, this study contrasts self-report and actual behavior, finding that reports of news exposure have a limited relationship to behavioral outcomes. The research adds a second layer of analysis to determine whether external factors – such as the presentation and display of news content – can also influence the type of news individuals choose. Specifically, it appears that a graphical news display, with photographs and images, encourages more soft news selection than does a text- only display. This effect is compounded for individuals with lower education levels, likely due to the extra cognitive burden associated with reading text-only content. Other individual characteristics, such as affective political polarization are associated with increased hard news selection; however, this effect holds only for males.
v Finally, this study approximates the stability of news preferences over time— specifically, throughout the duration of a news reading session. While self-reported interest in current affairs is associated with hard news selection early in the experiment, participants become somewhat more likely to seek out the opposite news type as the experiment progresses. While news preferences are not inert, there is still a clear gap between those preferring hard news and those preferring soft news. This chapter concludes with implications for news displays and how we might attempt to close the widening political knowledge gap in American society.
STUDY 2: POLITICAL POLARIZATION AND NEWS SOURCE PREFERENCES The second study evaluated the extent to which news consumers exercise preferences for content that is perceived to be in ideological agreement. Study 2 was conducted within the same online survey and experiment framework as Study 1, yet extended beyond the type of news selected to evaluate which factors affect the news source selected. News sources were clustered into right, left, and neutral leaning sources, in accordance with previous research and the results of a pre-experiment survey, where participants rated their perceived fairness of news organizations. Results reveal that political orientation does affect the news sources that participants select – notably in the first two trials of the experiment. Further, preferences for a specific source are quite stable over time—as the experiment progressed, individuals rarely deviated from their original sources, and when they did it was more likely to be a politically neutral source rather than the opposite ideology. Furthermore, the graphical display of news, which offers a prominent depiction of the news organization brand, led to more repeat selections of the same source. These findings offer additional support to the argument that the ideological division between media outlets may be growing increasingly stronger. Finally, it is important to note that loyalty towards a specific news provider may in fact be stronger than the influence of ideology: when liberals or conservatives chose an ideologically opposite news source, they showed strong allegiance to this source throughout the experiment, and were just as likely to repeatedly select the source as someone whose ideology matched that of the news provider. Thus, while partisan
vi selectivity towards news choices may in fact be a prevalent phenomenon, it is likely much more nuanced than previously suspected.
STUDY 3: BEHAVIOR EFFECTS OF NEWS DISPLAY: EYETRACKING STUDY The results of Studies 1 and 2 indicate that changing the visual display of news has a significant effect on both the news source and news type selected. Therefore, the third study engages eyetracking to critically evaluate how these unique information formats are viewed. Specifically, this chapter analyzes whether news readers allocate more attention to certain elements of a news story when presented in a graphical format, and whether the level of cognitive processing differs between the text-only and graphical displays. Results reveal clear differences in the processing of text and graphical news displays. Specifically, fixation duration—a measure of interest and cognitive processing—is shorter for soft news topics and when news is presented graphically. The graphical condition also facilitates more rapid decision-making, with less time spent per page. Saccade length, a measure of the overall pattern of page viewing, is significantly longer in the graphical condition, indicating a less linear viewing sequence. Furthermore, the amount of time spent reading a specific news story is highly influenced by page location in both conditions: unsurprisingly, more attention is given to news stories on the top of the page. The eyetracking data provides additional evidence to explain the divergent news selection behaviors witnessed in the text-only and graphical news formats.
vii viii ACKNOWLEDGMENTS I extend deep gratitude and appreciation to the many people who offered invaluable advice, insight, discussion, and support throughout my PhD. In particular:
My committee members and dissertation chair: Shanto Iyengar, Clifford Nass, James Fishkin, Simon Jackman, and Douglas Rivers. Shanto Iyengar, my advisor, has consistently challenged me to think more critically and deeply about the issues herein. James Fishkin has offered insight and wisdom throughout. Simon Jackman has provided me with invaluable statistical and analytical knowledge. Clifford Nass has been supportive since my original enrollment in 2004, offering advice on methods, data analysis, and scholarship generally. Douglas Rivers, my orals defense chair, laid the foundations for my statistical understanding.
My colleagues and mentors at Google who encouraged and supported my goal of finishing my PhD: especially John Boyd and Patrick Larvie.
Colleagues, faculty, and staff at Stanford: especially Fred Turner, whose high standards made me take that extra step, Jeremy Bailenson for career discussion and support, Solomon Messing for insightful discussion, and Susie Ementon, for continued patience, support, and guidance.
Those who generously offered statistics and data analysis advice: Julie Granka for her infinite knowledge and patience, Noah Simon, Kieron Wesson, David Mease, Kyu Hahn, Rehan Khan.
The online survey and experiment were created with much help from John Walker, Sean Westwood, Sam Luks, and Jason Cowden. Frederic Wolens provided eyetracking data assistance.
I am indebted to the priceless patience and support of family and friends: especially my parents, Edward and Rachele, without whom this would not have been possible, and Julie Granka and Kieron Wesson for endless encouragement and discussion.
Thank you all very much.
ix TABLE OF CONTENTS
Introduction
Chapter 1. Introduction: News media and public life 1 1.1 News media and political knowledge 1 1.2 Evolving news media environment 11 1.3 Audience choice and news consumption 19 1.4 Heuristics used in news selection 23
Research Results: Online news preferences
Chapter 2. Testing selective exposure: Who attends to hard news? 33
2.1 Introduction 34 2.2 Methods 34 2.3 Survey results 39 2.4 Descriptive experiment results 47 47 Experiment completion 50 News category selections 55 2.5 Analysis: Hard and soft news selection 57 2.6 Results 57 Trial 1 news category selection 60 Trials 2-4 news selections 67 2.7 Discussion: Hard news & selective exposure 2.8 Future work 71
Chapter 3. Political Polarization: News Source and Partisan Cues 75
3.1 Descriptive results 76 3.2 Clustering ideologically similar sources 80 3.3 Analysis: Source selections 84 Source loyalty 84 3.4 Analysis: Source preferences 92 3.5 Results 93 Trial 1 source selections 93 Trials 2-4 source selections 96 3.6 Discussion 102 3.7 Future work 104
x Chapter 4. Eyetracking Study: Behavioral Processing of Online News 107
4.1 Introduction 109 Visual processing 110 Measuring eye movements 112 4.2 Methods 114 Stimulus materials 114 Data caputre 116 4.3 Analysis 117 4.4 Results 119 Time to click 120 Influence of page location 126 Order of page viewing 130 Fixation Duration 132 Scanning and saccade behaviors 135 Source recall 139 4.5 Discussion 139
Afterward
Chapter 5. Future Directions for Political Communication Research 143
5.1 News design: effects of text and graphical display 143 5.2 Initial news selections 147 5.3 Subsequent selection behaviors 150 5.4 Effects of political polarization 152 5.5 Afterward 154
Appendices
Appendix A: Survey used in online panel study 157 Appendix B: Survey items used in eyetracking study 163 Appendix C: News story headlines 165 Appendix D: Stimulus materials used 167 Appendix E: Source-prediction analysis (Chapter 3 supplement) 181 Appendix F: News category choices: analysis of Opinion category 197
Works Cited 201
xi LIST OF TABLES
Introduction
Chapter 2. Testing selective exposure: Who attends to hard news?
Table 2.1 N participants using each media type as primary news source 40 Table 2.2 N participants visiting at least one news website at x frequency 40 Table 2.3 N participants watching TV news program at x frequency 41 Table 2.4 N participants reporting levels of discussion /interest in politics 41 Table 2.5 N participants reporting specified partisanship and ideology 42 Table 2.6 Gender and political identification 43 Table 2.7 % of respondents rating the other party negatively, by trait 46 Table 2.8 Partisan attributes and attitudes 46 Table 2.9 Experiment completion: N participants completing stages 47 Table 2.10 Experiment completion: Logistic regression results 49 Table 2.11 N participants selecting news category, by trial 50 Table 2.12 N participants selecting a news category 50 Table 2.13 N participants making repeat news category selections 51 Table 2.14 N participants selecting news category pairs in Trials 1 and 2 52 Table 2.15 N participants selecting clustered news categories, Trials 1-4 54 Table 2.16 Chi-square tests of independence between adjacent trials 54 Table 2.17 Hard news selection in Trial 1: Logistic regression results 59 Table 2.18 Hard news selection in Trials 2-4: Logistic mixed model results 61
Chapter 3. Political Polarization: News Source and Partisan Cues
Table 3.1 N selections to news source, by visual treatment condition 77 Table 3.2.a N selections to news source, by news category 78 Table 3.2.b N selections to a news source, by hard and soft news 79 Table 3.2.c N selections to a news source, by hard and soft news, excl. opinion 79 Table 3.3 N clicks to each of the six page locations 79 Table 3.4 Self-reported party identification and ideological affiliation 82 Table 3.5 Linear regression: Perceived fairness of right and left news sources 83 Table 3.6a Source selections in trial 1 and 2, aggregate text and graphical 85 Table 3.6b Source selections in trial 2 and 3, aggregate text and graphical 85 Table 3.6c Source selections in trial 3 and 4, aggregate text and graphical 85
xii Table 3.7 Chi-square test of independence of news source, adjacent trials 86 Table 3.8 Accuracy of source as a predictor of subsequent source selections 87 Table 3.9 Accuracy of clustered source as predictor of subsequent selections 87 Table 3.10 Repeat selections of clustered news sources 88 Table 3.11 N Selections to ideologically similar news sources, adjacent trials 88 Table 3.12 Logistic regression results: characteristics of repeat source selections 90 Table 3.13 Ideology and source selection: All trials 92 Table 3.14 Ideology and source selection: Trial 1 only 92 Table 3.15 Source selections in Trial 1: Ordered logistic regression 95 Table 3.15 Source selections in Trials 2-4: Ordered logistic regression 97 Chapter 4. Eyetracking Study: Behavioral Processing of Online News
Table 4.1 Factors affecting time to click: linear mixed model 123 Table 4.2 Total time spent in location, in milliseconds 127 Table 4.3 Time spent in page location: linear mixed model results 128 Table 4.4 Minimum fixation per page region, by visual treatment 130 Table 4.5 Fixation duration: Linear mixed model results 134 Table 4.6 Saccade length during attention onset and later viewing 136 Table 4.7 Fixation duration during attention onset and later viewing 136 Table 4.8 Saccade length: linear mixed model results 138 Table 4.9 Average number of sources recalled, by experiment condition 120
xiii LIST OF FIGURES
Chapter 2. Testing selective exposure: Who attends to hard news?
Figure 2.1 Category selection screen 35 Figure 2.2 Sample text condition layout 36 Figure 2.3 Sample graphical condition layout 36 Figure 2.4 Sample graphical condition source template, with sample story 37 Figure 2.5a Histogram of affective political polarization scores 44 Figure 2.5b Affective Political polarization, by party and gender 45 Figure 2.6 Effect of ith trial on hard news selection 63 Figure 2.7a Marginal effects of gender and polarization on hard news selection 64 Figure 2.7b Gender, polarization, and hard news: partial effects probability 64 Figure 2.8a Marginal effects of education and visual display on news selection 65 Figure 2.8b Education, visual display, and hard news: partial effects probability 65 Figure 2.9a Marginal effects of trial and previous choice on news selection 66 Figure 2.9b Trial, previous choice, and news selection: partial effects probability 66 Chapter 3. Political Polarization: News Source and Partisan Cues
Figure 3.1 Mean perceived fairness of news organizations, by ideology 80 Figure 3.2 Mean perceived fairness of news organizations, by party id 81 Figure 3.3 Interaction between education and visual treatment on repeat source 91 Figure 3.4 Interaction of ideology and prior choice on repeat source selection 100 Figure 3.3 Interaction of trial number and prior choice on repeat source selection 101 Chapter 4. Eyetracking Study: Behavioral Processing of Online News
Figure 4.1 All plotted fixations in text condition 119 Figure 4.2 All plotted fixations in graphical condition 120 Figure 4.3 Time to click: interaction of graphical display and soft news interest 124 Figure 4.4 Time to click: partial effects plot of main effects 124 Figure 4.5 Time to click by participant and trial 125 Figure 4.6 Total story time: interaction between graphical display and location 129 Figure 4.7 Average first fixation to page region, by visual condition 131 Figure 4.8 Fixation duration: partial effects of visual condition and trial 135 Figure 4.9 Saccade length: by experiment condition and trial 137 Figure 4A.1 Average time to click by participant, graphical condition 141 Figure 4A.2 Average time to click by participant, text condition 141
xiv CHAPTER 1
INTRODUCTION: NEWS MEDIA AND PUBLIC LIFE
I. NEWS MEDIA AND POLITICAL KNOWLEDGE A healthy democracy demands that citizenry make informed electoral decisions by investing time and attention to public affairs. In practice, however, many have argued that it is simply more rational for individuals to remain unaware, or only peripherally aware, of current affairs, as the direct impact on day-to-day life is minimal. As we do find, some Americans are heavily invested in politics, taking the time to consume news content and make an informed vote, and others not at all. This dissertation first identifies the individual characteristics most strongly associated with consuming substantive news content, and then goes a step further, to assess whether these preferences may differ based on the format and visual aesthetic of the news content. Additionally, the studies reported herein address whether news consumption and source preferences are further driven by political and ideological positions. This introduction addresses (i) the benefit of news consumption for a healthy democracy, (ii) the ability (or inability) of news organizations to produce the hard news that citizens so require, and (iii) what individual, environmental, and media-specific factors are likely to encourage the consumption of hard news and/ or ideologically- slanted content.
THE RATIONALITY OF NEWS CONSUMPTION Essential to a healthy, well-functioning democracy is that citizenry make informed electoral decisions by investing time and attention to public affairs – and by extension, the news media. In practice, however, many have argued that it is simply more rational for individuals to remain unaware, or only peripherally aware, of current affairs, as the direct impact on day-to-day life is minimal. Much of this insight (e.g., Downs, 1957; Delli Karpini & Keeter, 1996; Popkin, 1994; Zaller, 2003) has reasoned
1 that despite what Democratic ideals might suggest, there may be little or no benefit in encouraging the electorate to routinely attend to the news. Downs’ economic theory of political action in a democracy (1957) assesses the costs and benefits of citizen awareness in the news: “The marginal cost of a ‘bit’ of information is the return foregone by devoting scarce resources – particularly time – to getting and using it.” The benefit of having additional information is to increase the likelihood of an individual voting “correctly,” or maximizing their citizenship (see Lau & Redlawsk, 1997). Yet, based on the dynamics within a given society and democracy, Downs reasons, “Consequently, it is rational for every individual to minimize his investment in political information, in spite of the fact that most citizens might benefit substantially if the whole electorate were well informed.” Downs was one of the first scholars to suggest that while an idealized democracy would contain a well-informed and engaged electorate, the rational expectation may be for most individuals tune out of politics. Other scholars have built upon the practical advantage of low levels of political information, and have instead suggested that a reliance on shortcuts may instead be helpful for sensible political decision-making (Delli Carpini, 1999l; Lupia, 1994; Popkin, 1994). Shortcuts, also termed heuristics, are “easily obtained and used forms of information that serve as "second-best" substitutes for harder-to-obtain kinds of data,” (Popkin, 1994, p. 44, emphasis added). As Lupia (1994) and Popkin (1994) discuss, shortcuts are not necessarily a bad thing, and may in fact guide poorly-informed individuals towards better decision-making. Commonly accepted voting heuristics include such things as interpersonal influence (e.g., from a prospective voters’ peers or social network), party identification and party image, candidate characteristics – including personality / “likeability”, incumbency, and demographics (age, race, ethnicity), and real-world conditions, such as the current economic status. Given the widespread use of information shortcuts instead of fully-fledged news processing—and the theorized irrationality of news consumption—much research has addressed the consequences of attending to, or ignoring, news information when stakes are high. Baum and Kernell (1999) established a cost-benefit model for the “net utility” of watching a presidential address or debate, drawing specific comparisons between cable and non-cable subscribers. Baum and Kernell employed a P (probability of importance)
2 x B (benefit) – C (cost) model to approximate the relative importance of the presidential message, along with the associated benefits and cost of watching. Their findings showed that age and education affected this equation, as well as a cable subscription: cable viewers were between seven and nine percentage points less likely to watch the presidential debates. Zaller (2003) further speculated that it is most important for individuals to tune into news only in a high-stakes environment. In so doing, Zaller suggested a provocative “burglar alarm” theory of news reporting. While inherently geared towards news dissemination, the theory also encompasses the perceived costs and benefits of consuming news from the audience perspective. Zaller’s supposition is that perhaps the average citizen does not need to be up to date on all of the daily news events, but could instead perhaps be warned of controversial and important topics via “burglar alarms.” In making his argument, Zaller largely extends the work of Michael Schudson (1998), tracing the historical roots of news reporting while making judgments about what citizens want and need to know about public current affairs. Zaller’s description of the “burglar alarm” standard for news reporting is meant to parallel the analogy of a police patrols and burglar alarms, meaning: it is less important for citizens to be actively patrolling and looking out for stories in the news, but rather, citizens should be made aware of situations that are aberrations. While sensible upon first analysis, Bennett (2003) illustrated how Zaller’s model in fact describes the existing deficiencies in modern-day news reporting, but that it is subsequently ill-equipped to handle sensationalist coverage. Despite this, Zaller does contend that perhaps there are certain times when it is more important for an individual to be attentive to news, and other times when lower awareness may be excusable. Finally, tuning into soft news may not be such a bad thing, as Prior (2005), Baum (2002, 2003) and others conclude. However sporadic the factual knowledge gain may be from soft news, research indicates that it does in fact provide some educational value, notably for promoting awareness of political issues. Thus, soft news may be better than no news at all.
THE RATIONALITY OF NEWS PRODUCTION
3 Demand for soft and hard news. Debating the rationality and cost of consuming hard news is one matter, yet it is inherently founded in the assumption that news organizations are indeed producing the hard news that ought to be consumed. In fact, much recent evidence suggests otherwise: that the amount of substantive hard news coverage is shrinking relative to softer news formats (e.g., entertainment, sports, lifestyle) (see Iyengar & McGrady, 2008; Patterson, 2000). While the distinction between hard and soft news is somewhat nebulous, it is traditionally agreed that soft news comprises the content that is more sensational, entertainment-focused, personality-centric, and less time-sensitive (see Patterson, 2000). Given the market- based model of the United States media, and its dependence on profit-driven news, news organizations appear to be finding it increasingly difficult to justify hard news coverage due to the costs it might incur (both reporting costs and potential loss of audience costs). While news organizations may be of the mind that consumer and advertisers alike prefer soft news, there is some evidence that suggests otherwise – that consumers do want and expect substantive news content (Patterson, 2000). Further, the audience for soft news programs, as defined as television talk shows such as Oprah or E! Tonight, is still relatively small (Prior, 2003, 2005, 2007), and no more popular than hard news. Instead, consumers are more likely making the distinction between news and entertainment. First to market. In addition to the drive for greater market-share, the new media environment and reliance on the Internet makes it increasingly difficult for traditional news organizations to compete and invest appropriate time and resources to covering a story. One only needs to look back to Matt Drudge and his breaking of the Clinton and Monica Lewinsky scandal to realize the lowered journalistic standards of citizen reporting makes it especially difficult for traditional news agencies to compete with “breaking” a story (see Kalb, 1998 for more details; Williams & Delli Carpini, 2004). With greater audience choice in the online environment, news agencies are looking for ways to better distinguish themselves from their increased number of competitors. In looking at modern-day news reporting inaccuracies, it appears that vying for audience attention has unfortunately been translated to diminished quality and substance of news, in favor of being first to market with a story. Simply put, given the expense of in-depth
4 news reporting, and the profit-centric nature of US news organizations, the goal of differentiation on a content-level has not created higher quality news content, but has instead emphasized the rapid-fire production of “news.” News organizations feel more pressure to be the first-to-market with a story, as they know that another organization will invariably cover it before the 6pm newscast. This sense of urgency and desire to keep pace with the competition has often manifested in erroneous or careless reporting – such as inaccurately calling returns in the 2000 presidential and other recent elections, or NPR’s and Giffords scadal (Shepard, 2011). Lowering of news quality. To reiterate, traditional news organizations are feeling the competition from bloggers and other citizen journalists, who rarely face ramifications or consequences from inaccurate reporting. The Drudge Report notoriously pioneered citizen journalism and blogging by breaking news stories, particularly controversial or sensational ones. A significant by-product of blogging culture is its deviation from traditional journalistic standards – such as foregoing fact- checking and crediting sources. Bloggers and other news organizations are able to engage in this departure from traditional journalistic standards for online reporting because its production cost is minimal. Not surprisingly, many of the professional journalist standards – such as fact- checking and source-verification – are absent in the blogosphere. Kalb (1998) and Zaller (1999) both warned of the deterioration of news quality due to the increased competition between news organizations. Of particular interest is that at the time of his writing (in 1999), Zaller only considered three primary sources of news: television, radio, and newspaper. While the media environment has grown exponentially to encompass Web content, the fact that Zaller acknowledged serious threats to news quality based on the competition in 1999 is clearly telling. Indeed in the Internet age, Zaller’s definition of competition – “the extent to which two or more news providers offer the same kind of news product to the same audience in the same format at the same time” – is no doubt ever more a concern. Baum and Kernell (1999) address how the growing cable audience has both shrunk attention to presidential coverage, as well as discouraged traditional networks from airing the presidential speeches. They found that throughout the 1980s and 1990s,
5 broadcast networks became more selective about airing presidential addresses – over time, networks have aired fewer broadcasts, particularly during the prime 9pm timeslot. Trends like this are discouraging, showing yet again, that television networks are driving much of their broadcast decisions on the potential for profit rather than the potential for healthy civic engagement. Editorialized Reporting. Another way that news organizations are appealing to the mass public is by delivering appeals that are intended for a core audience demographic. As Kalb (1998) writes, “Journalists have become too big for their britches… Merely reporting fact is no longer enough for them. Now they must analyze and interpret.” This is typically achieved by offering more editorialized content, and in particular, story selection and commentary that is in line with the political viewpoints of their intended audience. Fox News and MSNBC have perhaps achieved notoriety for their success in this style of reporting—Fox News tends to appeal to a conservative base, as well the “tea party” movement. This editorialized strategy reinforces intramural solidarity, leveraging notions of social capital (Putnam, 1995), and draws on intrinsic desires to be a member of a selected community. Media outlets are growing in size and output, and while the Internet has increased the volume of available news, it is unlikely to have stripped individuals of the desire to consume news. Still, this increased choice is most likely to encourage audiences to rely on shortcuts or heuristics in their news selection processes (see Popkin, 1994), which could then result in selective exposure and media polarization.
ATTENTION TO NEWS AND THE POLITICAL KNOWLEDGE GAP All of this is meant to show that even in analog media environments, a number of factors simultaneously affect an individual’s determination to consume the news. Before the adoption of the Internet, scholars were already addressing the rationality of news consumption, and digital technology does not inherently change this paradigm of thought. An individual’s choice to consume news content is not solely dependent upon the volume of available news and entertainment outlets. Instead, audience motivations are shaped by a number of other factors, as described below.
6 Access to technology and political media. The distinction between the technological haves and have-nots is referred to as the knowledge gap: those who have access to the technology, and those who do not (Tichenor, Donahue & Olien, 1970). The technological knowledge gap was typically addressed in terms of socioeconomic barriers (lower socio-economic statuses cannot readily afford broadband connections, nor have the education levels to fully comprehend the information), geographic barriers (some nations simply don’t have ubiquitous access to the Internet), or language barriers (much of the existing online content is in English). As of late, the knowledge gap has been used to refer not only to differences in technology access, but to differences in the specific information that people are obtaining. Of particular concern is how the Internet may be encouraging the avoidance of political and current affairs information. It is well documented that gaps in political knowledge already exist (e.g., Delli Karpini, 1999; Dow, 2008; Druckman, 2005; Jerit, Barabas, & Bolson, 2006; Luskin, 1990; Mendez & Osborn, 2009; Mondak & Anderson, 2004; Morris & Forgette, 2007; Price & Zaller, 1993; Prior, 2009a; Prior, 2009b). Most studies agree that the individuals with the lowest levels of political knowledge include females, individuals with lower education levels, African Americans, and the young. Individuals with higher socio-economic status, and especially those with greater levels of existing political knowledge –within which to integrate new political information – are also more naturally predisposed to retain new political knowledge (Price & Zaller, 1993). Gender and political knowledge. Similarly, much research has concluded that men are simply able to learn more and retain more knowledge about politics than women, even when controlling for education levels and socio-economic status. Clear inequities of political knowledge exist throughout strata of the electorate, and the gender difference has been one of the most curious differences. Attempts to reconcile this gap have found that women are more likely than men to report ‘don’t know’ answers for political knowledge questions (Mondak & Anderson, 2004), whereas men are more likely to guess. While it was theorized that controlling for this behavior could somewhat diminish the difference, evidence still persists that females report having less political knowledge. Other studies have evaluated the extent to which this gap persists
7 across all political issue domains (e.g., Dow, 2008), and this research finds that the gap been males and females narrows when comparing knowledge about issues related to women’s rights or female candidates (Dow, 2008). Other studies do find that both males and females alike perceive women to have less political knowledge, even when no actual differences exist (Dolan, 2010). It is quite possible that the political knowledge gender gap is exacerbated by seemingly intrinsic differences between males and females – for instance, women report having less skill, even when no actual difference exists (Hargittai & Shafer, 2006), women are more risk-averse (see Byrnes, Miller, Schafer, 1999; Croson & Gneezy, 2009), less aggressive (Hyde, 1984), less assertive, have lower self-esteem (King, et al, 1999), and are more anxious (Feingold, 1994; Macoby & Jacklin, 1974). All of these traits and personality attributes indicate that females may be more passive about any political knowledge gain. Media type and the political knowledge gap. Beyond individual characteristics, the medium and delivery of news content also has profound effects on political information acquisition. Yet, as Graber (2004) points out, the impact of news medium on subsequent learning and retention is one of the most understudied elements of political communication. Graber (1998), Luskin (1990), Jerit, Barabas and Bolson (2006), and others have found that individuals do not equally remember information from all media sources. Specifically, that certain media formats – such as newspapers – may do a better job at informing the public than other mediums, such as television (Druckman, 2005). Jerit, Barabas and Bolson (2006) dig a bit deeper into the differences between television and newspaper, and find that while newspapers are more likely to perpetuate existing knowledge gaps, television may level these differences. Their research found that educated individuals learn disproportionately more from newspapers – a cognitively dense and difficult medium to process – while television news programs provide near equal benefit to all levels of the education strata. One possible explanation for this effect is that the added visual information offers additional context with which to process the news information. The effects of audio, visual, and audio-visual stimuli have been studied by a number of scholars (e.g., Crigler, Just, & Neuman, 1994; David, 1998; Druckman, 2005). Crigler, Just, &
8 Neuman (1994) found that while both visual and auditory stimulus enhance emotional responses, the combined channels are not always more effective than audio alone. This is because the visuals can either add or detract from the news item, depending on its congruence to the auditory message. If the picture is not directly related to the text, it may increase cognitive load and confusion during processing. David (2008) also documented the importance of depicting images that are directly related to the story text, in a print news format. This study showed that adding a representative image to the text improved recall. A solid understanding of how text, graphics, and visuals affect the perception and retention of news content is now becoming even more pressing, given the changes in news presentation on digital devices, such as laptops, mobile phones, and tablet devices. News organizations and applications are all competing for attention, and seek to provide compelling visual imagery in their news display to attract the largest audience. News applications for tablet devices, such as The Daily, Flipboard, or Newsy, all engage compelling graphics to supplement news content. Media system and the political knowledge. Another factor contributing to the political knowledge gap between the informed and informed may simply be the media system that exists within a given society (Iyengar & Hahn, 2009; Iyengar et al, 2010). Specifically, Iyengar et al find that a public service and market model of news production affect both an individual’s likelihood of receiving news, as well as that individual’s subsequent level of current affairs knowledge. In particularly, there is a striking political knowledge gap in the United States, which has a market-based media system. This is in contrast to European countries, which have a predominantly public service oriented media structure, one that naturally allows for the broadcasting of more substantive news. Iyengar et al found that citizens in the United States are routinely more ignorant of hard news issues than their international counterparts, yet are no worse of when it comes to knowledge of soft news issues (celebrities, scandals, and entertainment). A likely cause is that the profit-driven demands of the U.S. news media marketplace have decreased the amount of available broadcast news; this, along with the growth of cable, has dramatically shrunk the American inadvertent news audience. This cross-national research also indicates that when it comes to political knowledge,
9 there is a marked difference in the United States between those who are well-educated and engaged in the political process, and those who are not (Iyengar et al, 2010). Those Americans of high socio-economic status (e.g., well-educated and politically-interested) fared no worse, and in some cases better than their international counterparts when it came to recalling factual information about world affairs. The less-interested strata of Americans, however, scored very poorly on knowledge items. This within-country finding is particularly important in the evolving landscape of media choice. It is quite probable that the increased choice and need for proactive news consumption online is contributing to the within-country knowledge gap, providing motivated individuals the opportunity to seek out more content than they previously had at their disposal. This is inline with existing research: potential gains due to the increased availability of political information online may be limited to individuals who are already interested and engaged (Delli Carpini & Keeter, 2003).
10 II. EVOLVING MEDIA ENVIRONMENT While selective exposure and attention to current affairs information is not a new topic, many have questioned – given the growing share of the Internet in a given individual’s media diet – whether current theories of media effects will survive and weather the changes brought by Web access. As with the growth of cable television, the increasing dependence upon the Internet has delivered with it a rapid growth in consumer choice. This has the potential to alter both the practices of news producers, as well as audience-level consumption patterns. Yet, it is also possible that the forecasted changes due to the Internet may instead more closely parallel changes that have occurred throughout history with other new mass media disruptions. Overreaction to digital media? Throughout history, the emergence of a new media technology has routinely been heralded as a notable disruption to the current mass media status quo. While nearly all technological innovations in mass media have brought significant changes – whether it be the introduction of the printing press and birth of pamphleteers, AM and FM radio, or broadcast and cable television – each signified the beginning of a new era in mass communication, seen as bringing more choice and a greater potential for egalitarian access. The same is now being said about the Internet. In hindsight, while it is clear that some media patterns have changed as a result of these inventions, the resulting differences are not always to the forecasted scale, nor entirely consistent with the initial predictions. While not discounting the new opportunities created by media technologies, it may be disingenuous to predict a complete re-conceptualization of the media landscape due to the introduction of a single new technology. The fervor with which many now view the Internet is akin to the democratic acclaim attributed to the printing press in the 15th century. At the same time, the Internet is viewed with similar apprehension that characterized reactions towards the increased choice provided with cable television. It is possible that existing assumptions about the news media should be revisited in light of the opportunities afforded by recent technological change, particularly as the Internet has begun to alter both the production and consumption of news media. Yet, at the same time, some of the hypotheses about digital media are too reactive.
11 Print media gave a voice to citizens. At their inception, new media technologies have frequently been heralded as a disruptive and democratizing force within society. One of the first instances of this was the advent of the printing press: key pamphleteers in early American history, notably Thomas Paine and Benjamin Franklin, capitalized on advances in the printing press to readily distribute information related to the prevailing dissent of the times. In recent times, bloggers have been viewed as a similar democratizing force, reminiscent of the printing press and pamphleteering: in fact, the first chapter of We the Media is aptly titled “From Tom Pain to Blogs and Beyond” (Gillmor, 2005). As anyone could create a pamphlet and profess a political viewpoint, similarly anyone can create a blog or a website to disseminate content.
Broadcast increased entertainment consumption. After newspapers, social forces drove communal consumption of radio broadcasts, with the canonical image of families gathered around in their living room, listening to the evening news broadcast, followed by a light entertainment program. Radio of course then bifurcated, producing more stations and a new FM band (and subsequent government regulations), such that talk radio programming eventually because synonymous of AM radio, and music of FM. The difference in popularity between AM and FM bands was an early indication that increasing choice might cause audiences to favor entertaining content when available, in place of newscasts. Television followed, still encouraging a shared viewing experience with families gathered around a television set to watch one of three primary networks, and inadvertently exposed to news content while waiting for their entertainment program to air. The inadvertent audience argument (Iyengar & McGrady, 2008) implies that acquisition of public affairs knowledge may be more an artifact of one’s media environment, rather than intrinsic motivation to acquire substantive news. Prior to the subsequent mass adoption of cable, it was suspected a good part of an individual’s hard news diet was due to inadvertent exposure to a newscast, while waiting for another, perhaps more entertaining, program to air (Iyengar & McGrady, 2007). Finally, television also changed the nature of political campaigns and news production, as presidents had to start devoting more attention to their outward appearance in televised
12 speeches and public appeals, and news organizations had to devise ways of engaging both audio and visual content.
Cable television increased choice. Finally, cable television emerged, bringing with it more channels and choice. This did not bode well for maintaining public attention to politics and current affairs. As Baum and Kernell (1999), stated, “What broadcast technology gave the president, cable technology seems to be taking away.” Cable television truly allowed consumers to escape from the prison of captive consumption, and while heralded by audiences, was met with dismay by many political theorists (Baum & Kernell; Iyengar & McGrady, 2007). Cable television gave individuals the opportunity to simply seek marginally more entertaining fare on another channel whenever times got dull. Terms such as “news-grazers” emerged (Morris & Forgette, 2007), putting a name to the behavior of flipping through multiple channels until finding a satisfactory program. A cable subscription offers a vast number of stations from which to choose, and with this comes ever more opportunity to disregard news content altogether and replace it with more entertaining fare. This sparked what is now considered to be the decline in the inadvertent audience. With the introduction of cable, and now with digital video recorders (DVR), audiences can ensure that they can both avoid all dull public affairs, and only attend to the shows that interest them – at whatever time they desire to watch them. Cable truly marked the era when audiences were no longer captive to news broadcasts and a small number of stations. Internet: mix of old and new. The Internet assumes certain hallmarks of both print and broadcast media, yet also has a technological framework that distinguishes itself from existing media. The Internet exhibits the democratizing traits of print, yet it also carries the cable quandary of too-much-choice, which may encourage audiences to ignore news altogether in favor of more pleasurable content. While the Internet is not likely to further sap an individuals’ desire to consume news, it does put more onus on the individual’s ability to select content. Information on the Internet primarily represents a pull rather than a push paradigm, meaning that individuals must specifically seek out and identify individual sources and stories that they want to consume. News content is not “pushed” onto individuals as it may be in a 20-30 minute news broadcast, with media juggernauts pre-selecting content (as in the gatekeeping
13 perspective (Schoemaker, 1991)). Nor can a remote control take an individual from ‘website 1’ to ‘website 2’, as is possible with cable television. The increased degree of self-selection implies that individuals do of course have much more control over the media messages they receive. Individuals now need to establish, either consciously or subconsciously, some decision-making criteria whereby they control their exposure to media messages. Yet, traditional news media outlets – the ABCs, New York Times, CNN, BBCs of the offline world are frequently transported to the online news marketplace, where they are also dominate and attract large audiences. At its most basic level, the Internet is a new delivery platform, as really the printing press was – allowing for the creation and mass distribution of new content, whether it be html-written webpages or online videos. Just as news reports were converted from the delivery format of newspaper (print), to radio (audio), and to television (audio-visual) – the Internet again offers up a new platform for media content that is a conglomerate of print and broadcast elements. Yet, on a broader level, the implications of Web infrastructure and digital technology go far beyond the content itself, and extend to the manner in which audiences consume and interact with information (see Manovich, 2001), particularly as the delivery technology switches between desktop, laptop, tablet, and mobile devices. Digital infrastructure distinguishes the Internet. Digital technology affords new ways of disseminating and consuming information, and the scale at which the Internet achieves this represents a dramatic break from the traditional mass media paradigm. Digital technology allows for direct feedback between the consumer and publisher: Website owners can track audience news consumption, and subsequently use that towards the personalization of future content. Digital technology also enables easy manipulation of content, which allows for direct feedback loops between consumers and content creators, and creates more opportunities for information sharing – as individuals can directly link to news content with digital tools (e.g., e-mail, SMS, social networks). These factors have the potential to disrupt current media theories along four dimensions: (1) by increasing the sheer number of media sources available (including citizen content-creators), (2) by removing the constraints of geographic and time-bound delivery (Web content can be updated at any minute of the day, accessed from
14 anywhere), (3) by creating entirely new access points for media consumption (via mobile devices, laptops), and (4) by enabling a direct feedback mechanism between news producers and consumers. While these characteristics are prevalent in other forms of media (cable also increased choice, and print to some extent enabled citizen content- creation), the Internet notably removes much of the time-space boundaries of news consumption, and further, allows for news organizations to acquire more direct feedback about their audience behaviors. News organizations do not have to wait for the evening press run, nor do television producers have to sit on the news until the 6pm broadcast. The Internet allows for immediate dissemination to a large audience, and this in itself is one of the major disruptions to the news media cycle. Mobile phones, blogs, tablet devices, and other digital communication tools have emerged for information sharing, particularly those that rely on mobile SMS infrastructure rather than the Internet (i.e., Twitter). Mobile forms of communication have proven to be particularly influential in countries with restrictive media access, such as Iran, Pakistan (Ramey, 2007; Grossman, 2009), Tunisia, Egypt (McCarthy, 2011), Libia, Bahrain, and others. In these countries, citizens could, via Twitter and other tools, use their mobile phones to send SMS updates on local conditions that traditional media outlets would not allow. In addition to sharing political and to-the- minute current affairs information, these technologies also enabled citizens to organize together for protests, demonstrations, and other joint causes. In the United States, the plane crash on the New York Hudson river (2010) allowed citizens who were in the right place at the right time to share photos of the ditch via their mobile phones. While these are clear examples of citizen empowerment through digital communication, it is clear that these newer citizen empowered digital news platforms succeed primarily in environments where the traditional media system has failed to provide accurate, comprehensive, and uncensored coverage – and where the enabling technology is readily available to the citizenry. In situations like these, new communication tools have enabled citizens and audiences to acquire and share knowledge that would not otherwise be possible. Yet in a more typical news cycle, the role of blogs, citizen websites, and Twitter feeds have largely been to repackage existing news content, and sometimes spin it with
15 an editorialized point of view. A recent Pew report indicates that bloggers still look towards traditional news media for their content: more than 99% of the linked stories on blogs came from legacy media outlets, and 80% originated from only four sources – BBC, CNN, The New York Times, and The Washington Post (2010). When it comes to truly new content, bloggers seem to excel only when the content is salacious (e.g., sex or politics scandal), opportunistic (Hudson Plane crash), or on issues that would have normally been sourced through citizen by-standers, even in legacy media. Digital news that “learns”. The amenability of digital technology allows news organizations to uniquely target the selection and presentation of news content for individual audience members. With digital infrastructure, it is simple for news organizations to collect numerous characteristics about their audience. Some of the most basic data would include: (1) geographic IP address (location of the individual accessing the site), (2) which articles (and how many) and sources an individual clicks, (3) much time is spent on each article, and (4) direct interaction with the content (as via an “email”, “share” link, or “comment” feature). The recording and analysis of these metrics are typically known in the information technology realm as signals of “implicit feedback” – a way to learn the behaviors and demographics of a user, and simultaneously infer the performance of a given system (for a summary see Kelly, 2005; Fox et al, 2005). In other words, an individual website visitor is not explicitly signaling their support or approval for given content, yet is doing so implicitly through their online behavior. It is then up to the website owner to employ machine learning algorithms to then infer how satisfied or interested an individual is in any given item. Implicit feedback has been used most extensively in the context of online information retrieval, with search engines extracting information about users’ search sessions including clicks, queries, reading time, session length, and page scrolling, and has been validated by eyetracking (Joachims, 2007; Radlinski, 2008). Similar metrics can also be employed for online news, with specific attention given to metrics that denote a longer “session” time (meaning how much time individuals spend with a particular Web item before browsing elsewhere). In the technology sector, maximization of the ever-desirable metric of “time on page” is considered the key to both user satisfaction and to revenue: generally, the longer time spent means that an
16 individual likes the content, and more time also increases the likelihood of an individual being exposed to, and/or clicking on associated advertising. Researchers and news organizations, such as Google News, have already addressed the use of implicit feedback in the context of online news (Chung, 2008; Lavie et al, 2010; Kurtz, 2003). Their research addresses the mechanisms whereby personalization can be tuned across different categories and types of news content. Das et al (2007) employ an algorithmic technique to improve Google News called collaborative filtering1, which goes beyond individual user clicks and also aggregates trends in community behavior. Analyzing the actions of a collective news community allows for a more comprehensive understanding of how individual news items relate to each other, ideally resulting in improved recommendations and more accurate estimates of user preferences. With time, it is likely that these techniques will increase in sophistication: personalization and information-targeting is perceived to provide online audiences with a better experience, under the assumption that the recommendations give audiences more of the content they ultimately want.2 Personalized outcomes. The technical capability to track and analyze audience behaviors is likely to alter the traditional model of news media gatekeeping – the process whereby news content is selected for audiences (see Shoemaker, 1991 and Barzilia-Nahon, 2008 for reviews). Research has already begun to recognize that technology is changing some of the standard practices articulated by gatekeeping theories. Some of this research includes an analysis of: how the Web may encourage the reporting of more event-based news (Livingston & Bennett, 2007), how blogs are becoming the new content curators, i.e., “gatewatchers” (Bruns, 2003), how hyperlinking serves as a mechanism of gatekeeping (Dimitrova et al, 2009), and how the 24-hour news cycle has led news organizations to be less selective and reflective, and instead more sensationalist, in their content offerings (Williams & Delli-Carpini, 2000). However, Sunstein (2008) was one of the first to directly address the potential perils of personalization and gatekeeping, by referring to the “Daily Me” model
1 The most frequently-cited example of collaborative filtering is purchasing recommendations shown on Amazon.com. 2 Advertisers also encourage the use of these personalization, as they then have a better chance of assuring that their message is appropriately targeted to a their core, desired audience.
17 (originally described in 1995 by MIT Media Lab founder Nicholas Negroponte): “You will not come across topics and views that you have not sought out. Without any difficulty you are able to see exactly what you want, no more and no less” (Sunstein, 2008). In the Internet age, merely consuming news content as an audience member provides the news organization with new metrics with which to subsequently “suggest” and recommend future news content, possibly unbeknownst to the reader. Personalization can be explicit (as in the reader indicating their dislike in sports and politics), or it could be implicit, simply by triggering the presentation of stories the individual is likely to read, based on prior behavior. Given this technical opportunity, there is also greater responsibility on the news organization to ethically use the information they are obtaining. News organizations can use readership data to not only predict items that audiences would like to read, but further insert stories with alternate viewpoints. News organizations may in the future have more ethical responsibility than they ever did, particularly when it comes to making use of the audience characteristics they collect. News organizations now have the opportunity to assume the moral high ground and uphold the position that diverse viewpoints are of true benefit to society – as research addressing political deliberation and discussion suggests (Fishkin, 1993; Fishkin & Laslett, 2003). News organizations may some day attempt to counter polarization threats by intermingling alternative viewpoints and perspectives from multiple political parties.
18 III. AUDIENCE CHOICE AND NEWS CONSUMPTION
PSYCHOLOGY OF CHOICE Literature in positive psychology might suggest that having more media platforms, channels, and sources is a good thing: offering choice to individuals has been found to confer some positive benefits. Such benefits include an increase in perceived self-control, satisfaction, and motivation (Cordova & Lepper, 1996; Deci & Ryan, 1985; Langer & Rodin, 1976). In other words, having some choice appears better than having no choice at all. This was likely one of the compelling factors of cable television – with more channels, one could certainly find something enjoyable to watch. However, as more nuanced psychology research has pointed out, and which should come as no surprise, there are clear limits to benefits of choice. Namely, there seems to be such a thing as too much choice. Too much choice. Research has recently found that indeed, individuals are paralyzed by too much choice (Iyengar & Lepper, 2000; Dhar, 1997), and may even forgo making a decision when there appear to be too many alternatives (Dhar & Nowlis, 1999). In one of the most cited examples, Iyengar and Lepper chose a naturalistic experiment for their study, setting up stands of jam within a grocery store. One version of the stand had six types of jam; the other had 24. The researchers found that indeed, while marginally more shoppers stopped at the stand when 24 jam choices were displayed, approximately eight times as many shoppers who saw the minimal display actually completed a jam purchase. Other research has come to similar conclusions, ultimately suggesting that while increased choice may initially be quite appealing, it can render poor or entirely absent decision-making when an individual is conflicted amongst the alternatives (Schwartz, 2000). Within the context of media, people are continually subscribing to larger cable plans, offering still greater channel choice at their disposal. People have begun to consume multiple media simultaneously – for example, having television “in the background” while engaging in other online pursuits. Some of this choice and media saturation has no doubt contributed to a distracted media multitasking culture, described by Nass and colleagues (2010). This research shows that media multitasking negatively affects even the most basic cognitive functions, such as attention and memory,
19 challenging the previous lore that multitasking’s rich environ is beneficial and enriching (Ophir, Nass, & Wagner, 2009). Given that the current media environment offers more choice to consumers, it is important to think how the psychological determinants of choice might manifest. Based on prior research, one might simply expect that having to choose from too many outlets might paralyze an individual and encourage them to simply forgo media entirely. But they do not – Americans are now consuming nearly 70 minutes of news a day (Pew, 2010), and three hours of television a day (Bureau of Labor Statistics, 2010), frequently while engaged in other media, particularly computers. The average student surveyed by Nass and colleagues (2010) engages in at least four simultaneous media activities (e.g., chatting, email, television, websites). It seems that the influx of choice is affecting individuals not in their desire to tune out of media entirely, but rather to self-select into specific types of content. Choice and contextual frames. Furthermore, an individual’s choice and decision-making patterns are not always made rationally. Much research has explored the effects of simple contextual frames on decision-making outcomes (Tversky & Kahneman, 1981; Chong & Druckman, 2007). Within a news report, how an issue is reported will influence an individual’s perception and interpretation of the issue. Unique editorial decision-making by journalists and editors, particularly with respect to wording, emphasis, elaboration, and placement of the story, all affect how audiences interpret the topic, and whether audience attribute blame on individuals in society or the overall economic climate (Iyengar & Kinder, 1987; Iyengar, 1991). The impact of framing is important, particularly when assessing how an individual’s media exposure will affect their attitudes, knowledge, and political outlook. Selective exposure. Selective exposure is the notion that individuals will select news and information that agrees with their existing dispositions. Selective exposure and selective attention were hinted at with early media research, which established that media had a reinforcing effect (Klapper, 1960; Lazarsfeld, et al, 1944). This research found that while media was not able to change an individual’s existing attitudes, it was, however able to serve as reinforcement for existing beliefs. Today, researchers have also found evidence that individuals’ online news choices may be more a result of
20 reinforcement-seeking than active avoidance (Garrett, 2009). Certainly a media message cannot simultaneously affirm everyone’s existing, divergent beliefs, and as such, media reinforcement indicates that to some extent, audience members exercise a degree of selective interpretation or attention towards the content they are consuming – described as “selective acceptance” by Kinder (2003). Research has shown that individuals may not only reject the arguments that they see as counter to their own, but also interpret them as weak and inaccurate. Lord, Ross, and Lepper (1979) found that the strength of ones prior beliefs significantly affects how a message is interpreted: “People who hold strong opinions on complex social issues are likely to examine relevant empirical evidence in a biased manner. They are apt to accept "confirming" evidence at face value while subjecting "discontinuing" evidence to critical evaluation, and as a result to draw undue support for their initial positions from mixed or random empirical findings.” As expected, the stronger the existing belief, the more difficult it is to ignore these opinions and the more they will subsequently shape the processing of new information (Taber, Cann & Kucsova, 2008). Cognitive dissonance. These findings about selective exposure essentially all extend from cognitive dissonance theory (Festinger, 1957), which was one of the original theories explaining selective attention to content. The primary thesis of this theory is that people do not want to hold multiple discordant views at any given time, and will seek to avoid this state of disagreement. While this theory gained traction, it also prompted Sears and Freedman (1967) to later refine what exactly might be intimated by the term “selective exposure.” Rather than an active avoidance of dissonance, Sears thought that an individual’s self-selection towards confirming messages was likely to be the result of de facto processes: selectivity could occur subconsciously as the result of other life choices, such as decisions to reside in a given location, or associate with members of a given community. As such, Sears painted selective exposure as a more reciprocal relationship: people exercise some choice about their environments, and in turn, these environments are likely to shape the people in them. Other research has supported this by indicating that while we may exhibit similarities with our social ties, it may not always be due to conscious and active choice (e.g., Mutz, 2006; Mutz & Martin, 2001).
21 Another mediator of selective exposure may be the issue public hypothesis, which states that people seek out information on issues that affect them personally (Iyengar, et al, 2008). In a test of the selective exposure hypothesis, Iyengar and colleagues (2008) conducted an experiment in which participants were given a CD containing political content, and tracked which CD content was viewed. Results affirmed selective exposure and issue publics hypotheses, in that partisan predispositions and even personal employment information determined the information that individuals accessed on the CDs.
22 IV. HEURISTICS USED IN NEWS SELECTION As described, some individuals use heuristics in the political decision-making process, and similarly, many use heuristics when deciding which news media to select.
PARTISAN CUES As discussed, the pull-paradigm of information acquisition on the Internet demands that individuals actively choose some—or all—of the news messages they receive. Sunstein (2001, 2008) in particular has cautioned and predicted that the influx of online choice will simply overwhelm potential news consumers. In attempting to wade through the multitude of choice, consumers may be more likely to rely on decision-making shortcuts; one such example is resorting to partisan bias. Selectivity in online content acquisition may quickly feed an individual’s nascent partisanship, as it becomes easier for audiences to avoid or ignore opposing viewpoints. With this type of behavior, individuals can become further isolated from the diversity within society. It is been repeatedly shown that providing individuals the opportunity to deliberate and discuss issues with other individuals, particularly with those that may hold divergent views, offers a benefit and can moderate existing issue positions (Delli Carpini, Cook, & Jacobs, 2004; Fishkin, 1991; Fishkin & Luskin, 2005). As such, there is of course natural concern that publics will increasingly use the media to reinforce their existing issue positions; this contrasted with the notion that a healthy media diet may actually be offer more diverse positions than ones social network (Mutz & Martin, 2001). The potential effects of media polarization are not limited to the moment of news selection. Instead, there is evidence that these effects may become stronger in the long-term. An individual exposed only to one political viewpoint is predicted to be not only a more passionate member of their own political party, but is also expected to have lower tolerance for alternative viewpoints later on (Sunstein, 2008).
Political polarization in the electorate. The subject of political polarization within the electorate is a subject of intense debate. People have talked about the growing polarization of the American electorate in a number of ways. Most studies addressing polarization have done so longitudinally,
23 comparing National Election Study data over time, and analyzing whether the levels of polarization within the American public have become stronger over time. Some research concludes that the mass public is not growing more divergent along ideological views and issue positions, but instead any slight degree of polarization persist only within the political elite (e.g., Classen & highton, 2008; Fiorina, 2006; Fiorina & Abrams, 2008). Other research supports hypotheses that party activists and highly religious groups are contributing to polarization (Layman, 2007; Layman, Carsey, & Horowitz, 2007), or that a by-product of appealing to the party base during election primaries promotes stronger partisan platforms. Yet other analyses of longitudinal survey research points to clear signs of a growing mass partisan divide (Abramowitz & Saunders, 1998; Abramowitz & Saunders, 2008; Abramowitz, 2008, Brewer, 2009; Evans, 2003; Jacobson, 2005; Kimball & Gross, 2005). While there is clearly debate about whether levels of ideological polarization are growing within the electorate, there are fewer disputes about the hostility that each political party feels for each other. Republicans and Democrats have offered antagonistic opinions of the opposite party throughout history, and a key worry is that partisan-skewed media choices will further fuel this schism and reduce tolerance. Some contend that media choices may be either contributing towards, or reinforcing polarized attitudes towards the opposite party. Media Polarization. The thought that media might have a hand in the polarization of American public has led to a number of research efforts, using both survey and experimental methods, to detect any relationship between media and the growing polarization of political views and partisanship. Survey, experiment and log data all offer evidence that partisans self-select into news outlets that affirm existing political ideologies (Adamic, & Glance, 2005; Arceneaux & Johnson, 2010; Coe et al, 2008; Iyengar et al, 2008; Iyengar & Hahn, 2009, Iyengar et al, 2000; Morris, 2005; Stroud, 2010). Iyengar & Hahn (2009) conducted an online experiment assessing selective exposure to partisan content, whereby all participants saw content pulled from one news feed, yet the individual feed items were randomly attributed to one of four sources. The results of their study found that conservatives consistently opted to read the articles attributed to Fox News, no matter the type of story (political or
24 entertainment), supporting the notion that audiences may be prone to selectively attend to trusted sources that affirm their political ideals. Coe et al (2008) also found that Republicans and Democrats self-select particular news sources, using both survey data and an experiment. Results from Coe et al support Iyengar and colleagues, suggesting that Republicans prefer Fox News, whereas Democrats prefer CNN. Analyzing blogs, Adamaic & Glance (2005) also found evidence of partisan polarization, with bloggers offering very little cross-linking between Republican and Democratic blog sites. Their research also finds that blog readers typically only consume blogs that agree with their partisan-leanings, and furthermore, that these political blogs serve to propagate in-group echo chambers (due to the few links to competing viewpoints) (Adamic & Glance, 2005; Farrell & Drezner, 2007; Lawrence et al, 2010). Blogs have garnered attention from researchers, as blog content is often editorialized and skewed to a particular political party orientation. Studies have assessed how blogs might influence both the production and readership of news, concluding that blog content does indeed present the current affairs in a partisan manner (Adamanic, & Glance, 2005; Drezner & Farrell, 2004; Hindman, 2007). Consequences of political polarization. When individuals exercise such strong preferences for a media outlet, the consequences for polarization and political tolerance become ever more clear. Exposure to opinionated presentations of political news will affect how individuals interpret the information they are presented (Druckman & Parkin, 2005; Gunther & Liebhart, 2006; Matheson & Durson, 2001). Research has shown that in accordance with the hostile media theory (e.g., Vallone, Rosse, & Lepper, 1985), partisans do perceive opinionated news content as biased if that opinion is contrary to their existing belief (Feldman, 2010). Further, individuals who believe news sources to be biased source may in fact develop stronger opinions or polarized attitudes, in reaction against the unfair bias they perceive (Levendusky, 2008; Morris, 2005). Offering a positive spin, research has also shown that there is indeed a strong relationship between polarized and opinionated political attitudes and political knowledge (Delli Carpini and Keeter 1996), as well as between polarization and political engagement (Abramowitz & Saunders, 2008), despite claims made by Fiorina and Abrams (2006). One potential reason for this is that polarized individuals may have
25 higher levels of political knowledge, a factor that unsurprisingly, is found to improve the quality of political discussion (McClurg, 2006). While a more substantive political discussion may be a positive side-effect of polarization, it is still difficult to ignore the adverse democratic consequences of a segregated and polarized electorate. Exaggerated effects of media polarization? While a large body of scholarly research provides strong evidence of political polarization, there may also be a silver lining. Some research suggests that it may be difficult for the Internet to further fracture and stratify the electorate because the majority of online attention is directed towards a small number of dominant websites. By analyzing traffic on news sites across a variety of political topics, Hindman, Johnson, & Tsioutsiouliklis (2003) show that a tiny fraction of sites absorb nearly all of the online traffic. While this analysis was done in the aggregate, it does not account for individual-level effects of partisanship, and suggests that on average, online users obtain information from largely the same leading sources. While the objective of the authors is to cite the inequalities and “monopolistic” structure of the Web, their results provide evidence to counter polarization—namely that a select few Websites provide a shared content experience for the majority of news audiences. Prior to this, Bruns (2003) had also found much overlap between the content on blogs and major news sites. Bruns classified bloggers as gatewatchers, meaning that they rarely make their own news, but rather accumulate and repackage information from traditional media source. Both of these research findings suggest that despite the greater number of news outlets, news consumers may be consuming news content from same dominant players to which they’ve become accustomed, offering evidence that exposure to news content may be more homogenous than suspected. This offers hope that instead of echo chambers, the majority of news audiences will in fact have some degree of shared exposure in the news they consume. Further, it is also possible that having more choice and access to opposing views does not temper the effects of polarization, as found by Arceneaux and Johnson (2010), but instead makes individuals feel even more strongly towards their own existing opinion. Polarization versus commoditized news. When presented with seemingly infinite choice in the Internet environment, news audiences need to rely on some simple
26 mechanisms of news selection. It requires effort to self-select individual morsels of online content, and newsreaders may make the job easier by relying on heuristics and shortcuts to guide their selections. These shortcuts may be motivated by partisanship, availability of content, or possibly even based on a desire for high-quality journalism. Alternatively, online news readers may be agnostic to these shortcuts, by letting newsfeeds or news aggregators do the hard work. It is likely that individuals still desire curated news content – as in the 25 minutes of news broadcasts – yet this may now come in the form of the front page of The New York Times, a daily email summary, or the top 6 stories listed by Google News. Without denying that polarization may grow in highly politicized individuals, it may be difficult for such effects to grow equally across all strata of the electorate, particularly as news becomes more dependent on device. It is possible that instead of becoming more polarized towards specific sources, individuals with weaker partisan leanings may become increasingly apathetic, or even agnostic, to their news sources, treating news more like a commodity. This trend may be encouraged by an increased use of news aggregator sites, such as Google News, or Yahoo News. A majority (56%) of online newsreaders use new aggregators on a typical day, and this percentage increases to 68 in the 18-29 year old demographic (State of the Media, 2010). News aggregators see their job as first, drawing attention to the specific news story, and second, offering a number of links towards several news organizations reporting on that story. As use of news aggregators increases, it is quite possible that individuals may attend less to the political leanings of the source, and more towards the headline of the news story. If this effect manifests, it would not only be important in preventing further bifurcation within the electorate, but it could be important for understanding political campaign effects. Campaign messaging may prove to be more, or less, influential to Independents when it is delivered without a partisan or source influence.1 In this view, perhaps only extreme partisans may congregate in increasingly politicized silos, while the rest of society, particularly independent-leaning members of the electorate, will attribute less importance to the news organization and partisanship
1 However, it is also possible that news providers will catch on to audience attention to headline instead of source, and may instead seek to write ever more polarizing headlines!
27 of the source. News source may become both more and less important at the same time: the majority of audiences may be inclined to disregard source, relying instead on other shortcuts (e.g., the relative prominence of a story on a webpage, or social cues like “most read”), while extreme partisans may continue exercising source selectivity or avoiding use of news aggregators in the first place. The Internet certainly was not the first media to facilitate selective exposure on the basis of partisanship. Many countries had—and some still have—a partisan press system (Hallin & Mancini, 2004), such as Italy. In terms of broadcast, audiences have self-selected into cable television outlets: 51% of regular CNN viewers are Democrats, and 39% of Fox News viewers are Republicans (PEW, 2008). Thus, the Internet could simply be another medium in which this is possible. From one perspective, the Internet may further encourage audiences to engage in partisan source selectivity; from another perspective, it is possible that audiences may instead be relying on other – non-partisan – cues of digital media, such as online Web layout, page speed, ease of access on mobile devices. For the majority of the population, a number of shortcuts in the Web environment may be as, or more, influential than straight party politics. Social cues. Early communication research found only limited media effects when the measureable outcome was behavior change, namely vote choice (Katz & Lazarsfeld, 1955). Their research found only weak and indirect links between media and attitude change; the primary effects of mass media seemed to simply reinforce existing (and even latent) partisan predispositions. Their research revealed that social forces and personal relationships were much more influential in shaping or changing political attitudes and behaviors than the media. In what was called the “two-step flow” model, Katz and Lazarsfeld found that a few key individuals, or opinion leaders, would obtain information and subsequently impart their formed beliefs and opinions to a larger audience (1955). By defining an effect as a behavioral change, additional research also pointed to the notion that social forces produced stronger effects than did media alone (Klapper, 1960). Recent research has attempted to refit the model of two-step information flow and the notion of social capital (Putnam, 1995) into the context of online social networks (Pasek et al, 2008). In the digital computer-mediated environment, social interactions are facilitated through the use of e-mail, mobile
28 communications, or social networking tools (e.g., Facebook, Twitter). With the increased ease of sharing of information, individuals may come to increasingly rely on their social connections for information. With digital infrastructure, website owners also have the ability to leverage social connections and community behavior. Websites can track which news articles are most frequently accessed, commented on, or emailed to others. These cues can be contextualized alongside an online news article, indicating the relative popularity of the given item. Before selecting an article, readers could be informed of the number of people who have read a given story, or the number of people who have commented on the story. With more sophisticated audience tracking, news websites can display how popular a given story is within the geographic region of the reader. Audiences can easily interact with online news content, and websites are now offering many ways in which to do this – notably through the many “share,” “like,” or “thumbs up” icons that pepper a news page. Audiences are also engaging in news content by writing comments alongside news stories. Little or no research to date has explored the diversity of opinions within these online comments, yet there are a number of unanswered questions; for instance, if individuals read online comments, might this serve as a (light) form of deliberation? Visual cues. Vision and perception research suggests that individuals will attend to the salient features of an image, and online this may be stories with appealing photos or imagery (Treisman, 1986; Itti, 1998). Some news aggregators, namely Google Fast Flip, are breaking from the text-only presentation of a news article, and have now included a visible screenshot (image) of the news webpage. Similarly, news applications on tablet devices and mobile phones also resemble this presentation and layout as well. As intended, this type of display is likely to facilitate rapid browsing through articles, as it is well documented that visual cues can be processed more quickly than textual cues (Rayer, 1998; Viviani, 1990). Visual layouts like the one described may enable individuals to more quickly process the available news stories, and make a decision that better reflects the available alternatives. This format may have positive effects, such as enabling individuals to subconsciously absorb more news content than they might otherwise have read; or, it may have negative consequences,
29 such as if the visual format makes entertainment and soft news more appealing, due to the photos, or simply the “softer” feel of such a visual and graphical layout. Further, perhaps the photos of celebrities and faces accompanying softer news content activate neurological reward triggers in the brain. There is also some evidence that attractive faces stimulate neurological reward (Aharon et al, 2001; Bray & O’Dohorty, 2007), which could encourage individuals to repeatedly select this “rewarding” soft content with photos of beautiful celebrities. As individuals can recognize and process faces very rapidly (e.g. Johnson, 2005), this behavior and neurobiological activity can occur quickly and subconsciously. Location cues. Just as newspapers were able to make certain stories salient by printing them on the front-page of the newspaper, there exist similar location biases on Webpages. Much research, some of it involving eyetracking, indicates that English- speakers start looking at a webpage in the top left quadrant (Pan et al, 2003; Stanford Poynter Project, 2000), and in rare instances do individuals pay much attention to the content that can only be visible through scrolling. While position bias is most influential when information is presented linearly—such as online search (see Joachims et al, 2007)—it does manifest in other environs where individuals might resort to selecting the information listed front and center, or at the top of the page.
METHODS FOR ASSESSING POLARIZATION AND MEDIA EFFECTS Most research uses the American National Election Study (ANES) survey data to measure ideological views and polarization. Despite this, is well known that asking users to self-report their behaviors, particularly about media and data use, can produce unreliable estimates (Barabas & Jerit, 2010; Hovland, 1959; Prior, 2009a; Prior, 2009b). The lack of accuracy is known to stem from several possible factors: (i) innocent error on behalf of the participant – merely unable to accurately estimate or recall their behaviors, (ii) social-desirability bias – wanting to present the appearance of engage in behaviors that are well-regarded by society, or (iii) priming (intentional or unintentional) due to question wording. Much research has attempted to validate the methods of media use by altering question wording – such as whether asking about ‘exposure’, ‘attention’ or ‘use’ of news will improve validity (Eveland, Hutchens, &
30 Shen, 2009). Other attempts to improve surveys address whether it is best to ask more refined units of time (e.g., minutes per day or week), or larger units of time (e.g., days per week) (Tewksbury, Althaus, & Hibbing, 2010). To combat the inconsistencies in surveys, many have adopted experiments to isolate causal explanations. In particular, political researchers have used a mixture of online panel surveys and experiments to gather both a large N for both survey responses and experimental outcomes (Barabas & Jerit, 2010; Iyengar, 2010). Recording media behaviors in experiments may be more representative of actual behavior, as compared with self-reported survey data. The studies reported herein also use the experimental methodology to better understand media behaviors.
31 32 CHAPTER 2
TESTING SELECTIVE EXPOSURE: WHO ATTENDS TO HARD NEWS?
ABSTRACT
It is well documented that substantive public affairs content—hard news—is often overlooked when pitted against soft news and sensational topics; this effect is most prevalent among certain strata of the electorate (lower socio-economic status, females, African-Americans). However, less research has assessed whether external factors—such as the visual format and presentation of news content—may also exert a significant influence on the type of news individuals select. Further, little research has also assessed the stability of hard or soft news preferences over time. This chapter presents results of an N=1,000 online experiment, which evaluates the individual and environmental factors affecting hard news selection. Results reveal that a graphical presentation of online news discourages individuals from selecting hard news, and instead seems to make soft news topics more compelling. This effect is compounded for those with lower education levels, likely due to the extra cognitive burden associated with reading text-only content. Personal characteristics, such as affective political polarization, are also associated with increased hard news selection; however, this effect holds only for males. This gender difference between polarization and hard news selection offers new evidence that may help to explain the ever-present gender gap in political knowledge. Finally, we assess the stability of preferences for hard or soft news content, finding that while self-reported news interest influences hard news selection early in the experiment, participants are likely to seek out the opposite news type as the experiment progresses. These results reveal that news preferences are not inert, and given sufficient opportunity, individuals may naturally seek out a diverse news diet. This chapter concludes with suggestions for online news displays and speculations as to how we might attempt to close the widening political knowledge gap in American society.
33 2.1 INTRODUCTION As discussed in Chapter 1, one of the key challenges associated with increased choice in the new media environment is encouraging individuals to choose relevant and substantive current affairs content instead of defaulting to soft news. To assess the factors that may influence an individual’s selection of hard news topics, an online experiment and survey was conducted with 1,000 individuals in the United States. Participants were all selected of a YouGov-Polimetrix panel, which uses sample- matching to recruit a representative subset of the population (Rivers, 2009).
2.2 METHODS The experiment protocol first began with participants completing a pre-test survey (full details in Appendix A), which included questions about news media use and exposure, political interest, and reports of other online activities. The survey was then followed by the news preferences experiment, which was designed in a two-stage manner to evaluate selections to hard and soft news content, and source preferences. This chapter details the findings specific to selective exposure (Chapter 3 discusses the implications for partisan media political polarization). News category selection. Participants were asked to choose one of eight news categories to read: Entertainment, Sports, US Politics, World, Opinion, Business, Life & Style, and Environment. These topics were clustered into two news types: 1) hard news: US Politics, World, Business, Environment, and Opinion, and 2) soft news: Entertainment, Life & Style, and Sports.1 Selection of a news category by a participant directed participants to a page with six news stories related to the chosen category; on this page, participants were also asked to select the story they are most interested in reading. Participants engaged in four sequential trials of this process: four opportunities to select a news category, and four opportunities to select a news story.
1 Note that “Opinion” is a unique category: it does not constitute soft news, though its ideal classification may neither be hard news. To assess the fit of Opinion in the hard news cluster, three models were conducted: Model 1 included Opinion within the hard news cluster, Model 2 removed Opinion from both clusters (and thus the experiment entirely), and Model 3 allowed for a three-level multinomial model, in which the Opinion category served as its own unique cluster. There were no major differences in results, so results from the comprehensive logistic model (with Opinion as hard news) are reported in this paper.
34 The news category screen listed the eight topics in a 4 (rows) x 2 (column) grid. The Polimetrix-YouGov software allowed for the random assignment of each news category to one of the eight unique grid locations for each participant (see Figure 2.1). While all participants viewed a unique ordering of news categories, this arrangement remained constant across all four trials for a given participant (i.e., random arrangement was between participants, but not randomized during an individual’s four trials). Other than the arrangement of categories, the visual layout of the news category selection screen was consistent for all participants.
Figure 2.1: Category Selection screen. All participants viewed the news category selection screen presented above. The news category locations were randomized across all participants, though remained constant during a given individual’s four news selection trials.
Story & source selection. During stage two—the story selection screen following category selection—participants were randomly assigned to one of two treatment conditions: text or graphical (see Figures 2.2 and 2.3). This was a between- subjects manipulation, meaning that each participant viewed only the graphical, or the text news format, throughout all four news selection trials. These two conditions were designed to represent variants of online news presentation, particularly more recent formats of online news, which take advantage of the visual affordances of the Web. The text condition resembled the traditional layout of Google News, with a text headline, source, and the first few sentences of the story. The graphic condition was designed to match the Google Fast Flip layout, which shows a screenshot of the news Webpage. The location of each story on the news page was randomized into one of six positions on the screen, and was further randomly attributed to one of six sources (details on source selection to follow in Chapter 3).
35
Figure 2.2: Sample text condition layout One-half (N=500) of participants viewed the text-only news format. This screen was presented after participants selected a news category, and shows news content specific to the topic selected. Source and story are both randomized within the 6 locations.
Figure 2.3: Sample graphical condition layout One-half (N=500) of participants viewed the graphical news format. As above, this screen was presented after participants selected a news category. All source and story locations are randomized within the 6 positions.
Stimulus Materials. 576 total experiment images, of both news stories and sources for both text and visual conditions, were created using Adobe Photoshop. The images were optimized to display in full on a monitor with resolution 1024x768 or
36 larger (at the time of the study, an estimated 95% of consumers have monitors with at least these dimensions). Each of the text images (Figure 2.2) was sized at 310x120 pixels in width, with a blue title, grey source (in Arial 13 point font), and black snippet text. This was modeled after the layout of news stories on Google News. The story content filled the space of 245 pixels wide and 210 pixels in height. The visual format (Figure 2.3), modeled after Google News FastFlip, included a screenshot of the actual news webpage. The visual condition was created by extracting the header banner from the news organization’s website, and randomly attributing that source banner to another image file containing a given news story. The manipulation was done seamlessly such that it was not possible to detect the story overlay (see Figure 2.4). The source banner was approximately 40 pixels in height, and the full visual image templates were sized at 285 pixels wide and 261 pixels in height to ensure that all stories would display fully without scrolling (or at least very limited scrolling) on a 1024x768 browser. In total, 12 total source images were created (six sources in each of the text and visual conditions) and randomly attributed to one of six stories in each of the eight news sections. The news content included approximately 3-4 sentences of the story text, the headline, the source, and in the visual condition only, a corresponding photo. For the comprehensive collection of news headlines, source templates, and a selection of story templates, reference Appendix D.
Figure 2.4: Sample graphical condition source template, with sample story. The above image displays the sample CNN source template in the graphical condition. Note the top source banner and right-hand side of the page, with space remaining for the random insertion of a given news story. Full set in Appendix A.
37 Sources. The six sources used in the study were selected based on their popularity (viewership and circulation), as well as their respective reputations for aligning with a particular political party (see Kohut, 2010). The sources included: The New York Times (reputable left-leaning newspaper), The Wall Street Journal (reputable right-leaning newspaper), Fox News (right-leaning TV), CNN (neutral TV), USA Today (neutral newspaper), and The Huffington Post (left-leaning blog). To validate our assumptions of perceived ideological orientation of each source, during the online survey prior to the experiment, participants were asked questions about how biased, balanced, and fair the reporting is of each of these news organizations. This was used to cluster these six sources into two types of outlets: Republican-Conservative, and Democratic-Liberal. The complete description of source clustering is described in Chapter 3.
News Story Collection. To select the stories for each category, samples were drawn from the Google News RSS reader at five different time intervals – 7pm on April 7, 2010, 10pm on April 7, 2010, 9am on April 8 2010, 10pm on April 8, 2010, and noon on April 8, 2010. The RSS pulled the default Google News feeds for World, Entertainment, Sports, and Business; custom feeds were set up to pull news specific to US Politics, Life & Style, Environment, and Opinion. The Google news RSS returns the story headline, beginning snippet of the story text, and (sometimes) an image. Multiple samples were drawn, making it possible to select a representative sample of news that was fairly politically neutral. To maintain visual consistency in the overlays, the length of the story headlines was limited to two lines in the text condition. Ninety-six news story images were created (six news stories for each of the 8 news sections, replicated for each of two conditions). By assembling separate images for source and story, the page placement of news content and sources was easily randomized within one of six grid locations on the webpage. Participants completed an online survey related to news preferences and media use, and then continued to complete the experiment as per the instructions below:
38 Experiment Instructions
We are experimenting with the structure of news aggregator websites. News aggregators are sites that compile news stories from a number of sources, about a variety of topics. We’d like you to browse through the news on this site, selecting the topics and stories that most interest you. First, you will see a listing of the available news sections. Once you’ve clicked on a news section that you want to read, you will then be presented with specific stories. Again, you’ll have the opportunity to select whichever stories appeal to you. We’ll ask you to do this a few times. After you’ve finished, you’ll complete a short survey. When you are ready, please begin.
2.3 SURVEY RESULTS
MEDIA EXPOSURE, PARTY AFFILIATION & POLARIZATION Prior to the experiment, participants completed a number of questions about self-reported media use and politics. The survey addressed participants’ reported media use (including specific Websites and programs on the Internet and television), along with political ideology (liberalism and conservatism), party affiliation (Republican / Democrat), affective polarization, news interest and exposure, and demographic characteristics. The complete survey instrument is included as Appendix A. The following sections describe the specific questions used in the present analysis.
News exposure and interest. In the survey, participants were asked about their frequency of exposure to news on Web and television outlets, as well as their self- reported level of interest in news and public affairs. Three sub-measures (i) Internet news use, (ii) TV news use, and (iii) self-reported news interest and frequency of discussing politics) were normalized and aggregated to compute one metric of total news interest, yet due to the variance and differences in media types, most analyses reported herein maintain a separation between all three variables. The following section describes each of these four sub-measures in more detail. Most frequently used media source. Table 2.1 below presents data on which media sources participants report using for different types of news content. Most respondents rely on the television for news about politics, sports, and entertainment, followed by the Internet. The picture is swapped for healthcare, with most individuals
39 obtaining health information on the Web, rather than television. Less than 10% of the respondents use either newspapers or radio for any given content type. Overall, this ratio is fairly consistent with external survey data (e.g., Kohut et al, 2010), though due to variations in question wording, it is not possible to expect an exact comparison. Pew survey data suggests that the majority of Americans (58%) get news visa the television, 34% receive news via Internet or radio, and 31% via newspaper (Kohut et al, 2010).
Table 2.1: N participants using each media type as their primary news source Politics Healthcare Sports Entertainment Television 421 294 396 403 Internet 342 395 202 318 Newspapers 85 88 97 74 Radio 107 64 35 24 Other 18 76 15 51 None 25 55 227 104
Web news. Exposure to news websites was measured with 5-level ordered response variables (daily, a few times a week, a few times a month, rarely, and never), asking about the frequency of visiting popular news websites. Nearly half of respondents visit at least one news website on a daily basis (see Table 2.2). These results are consistent with external survey data. With regard to the popularity of specific news sites, results show that Yahoo News was the most frequently read news website, followed by Fox News, Google News, CNN, New York Times, Drudge Report, Huffington Post, and the BBC. This is somewhat consistent with 2010 Hitwise news website traffic data, where Yahoo is also the clear leader in news visits, and Drudge Report, Huffington Post, and BBC trail in popularity. The minor discrepancy is that Fox News is favored by the sample in this study, trailing Google News and CNN in the Hitwise data. It should be noted that both females and less educated individuals reported significantly lower levels of Web news use (Gender gap: t(995.99)= -4.68, p <
0.001; Education gap: t(822.14)= -4.11, p < 0.001).
Table 2.2: N participants visiting at least one website at specified frequency Daily Few x/ week Few x/ month Rarely Never 487 193 112 86 120
40 Television news. Participants were also asked to rate how many days a week they watch morning television news, evening television news, local news, and weekend news programs (options were ordered categories of 0-1 day, 2-3 days, 4-5 days, and 6-7 days). Nearly 40% of respondents watch some type of television news programming 6- 7 days per week. More than three-quarters of participants watch TV news more than 1 day a week. Results are presented in Table 2.3. The demographics of self-reported television news viewers are opposite from Web news readers: females reported significantly higher levels of television news exposure, and less educated individuals reported marginally significant higher levels of television exposure (Gender gap: t=
2.68 (976.90), p < 0.001; Education gap: t= 1.67 (744.1), p < 0.001).
Table 2.3: N participants watching any TV news program* at specified frequency 0-1 days 2-3 days 4-5 days 6-7 days 231 176 197 393 *Includes morning news, national news, and local news broadcasts, at weekly frequency.
Self-reported interest. Finally, participants were also asked to report their interest in current affairs as well as their frequency of discussing politics with others (both were 4-level responses, ranging from most of the time / almost every day to hardly at all). Most of the participants in this sample reported very high frequencies of discussing politics and being interested and following current affairs content (nearly 70% of the sample reported discussing politics on a daily basis). Further, as is consistent with existing research, females report less frequent discussion and less interest in news than their male counterparts (t= -7.212 (997.73), p < 0.001), as do individuals in lower education strata (t = -6.93(856.55), p < 0.001) (Delli Carpini, 1999; Dow, 2008; Luskin, 1990; Mendez & Osborn, 2009; Mondak & Anderson, 2004).
Table 2.4: N participants reporting levels of discussion and interest in politics Discuss politics News interest Almost every day/ Most of the time 380 671 Frequently / Some of the time 287 194 Occasionally / Now and then 236 84 Almost never / Hardly at all 94 32
41 Political party affiliation & ideology. Participants were asked to self-report their political ideology on a seven-point scale; levels ranged from strong Democrat, not very strong Democrat, lean Democrat, Independent, lean Republican, not very strong Republican, and strong Republican. This seven-point scale was used to construct a three-level party ID variable: Independents were classified as those who did not report leaning to a particular party, Republicans were classified as those who reported lean, not very strong, and strong Republican ratings, and Democrats were similarly coded. Based on these responses, 123 participants were classified as Independents, 409 as Republicans, and 444 as Democrats. Participants were also asked to express their ideology, on a spectrum of conservatism to liberalism. While the sample skewed slightly towards those identifying as Democrats, more participants articulated a conservative ideology instead of liberal. This is consistent with the literature, finding that some Americans with centrist or leftist policies prefer to not classify themselves as “liberal” (Ellis & Stimson, 2007). 361 participants identified as moderate, 384 as conservative, and 255 as liberal. There was some minor variability between self-reported ideology and party affiliation, reported below in Table 2.5 (e.g., while approximately 5% of Democrats profess a conservative ideology, less than 2% of Republicans claim a liberal ideology).
Table 2.5: Number of Participants reporting specified partisanship and ideology Party / Strong Not Lean Indpnt Lean Not Strong Ideology Dem strong Dem Rep strong Rep N=1000 Dem Rep Moderate 76 58 56 75 33 27 18 Conservative 11 7 4 35 122 39 162 Liberal 168 26 38 13 3 3 2
Gender and party affiliation. As is consistent with the literature (Dow, 2008; Kaufman, 2002), there was a significant difference between party identification and gender, with more females identifying as Democrat and Independent, and more males as
Republican (Chi-square test: χ (2) = 14.98, p < 0.001). Similarly, there was a significant difference between gender and self-identified ideology (Chi-square test: χ (2) = 29.07, p
42 < 0.001), with more females identifying as liberal and moderate, and more males as conservative – again, consistent with existing literature (Norrander & Wilcox, 2008; Zchirnt, 2010). Table 2.6 shows the values for gender and political identification.
Table 2.6: Gender and political identification Dem Indpt Rep Lib Mod Cons Female 260 85 188 143 225 165 Male 184 62 221 112 136 219
N Female = 533, N Male = 467 χ (2) = 29.07, p < 0.001 Bolded values represent instances where observed values exceed the Chi-square expected values, as determined through a visual inspection.
Affective Political Polarization. As articulated in Chapter 1, political science literature suggests that while political elites may be increasingly polarized in their ideologies and policies, the mass public has not significantly shifted from centrist ideologies (e.g. Fiorina, 2007). Yet a growing body of work finds that the mass public is in fact more polarized, and that perhaps divisions among media sources are further contribution to this division. While there may be disagreement over whether the public is developing more polarized issue positions, there is some evidence of affective political polarization – strong negative sentiments towards the opposing political party, based on affective reactions to political labels (e.g., Abrajano & Poole, 2009; Abramowitz & Saunders, 2008; DiMaggio, Evans, & Bethany, 1996). In other words, individuals may not necessarily identify as a strong partisan, but will have negative sentiments about the other party and its members. This may create stronger party-line divisions, and an in-group, out-group mentality, and may further be fueled by increased partisan media exposure. To measure affective polarization and its relationship (if any) to news source preferences, ten survey questions asked participants to rate how well the following attributes apply separately to Democrats and Republicans: (i) Patriotic, (ii) Closed minded, (iii) Knowledgeable, (iv) Compassionate, and (v) Selfish. Responses to these 10 questions (five for each party) were transformed to binary items, with 0 indicating responses of “Not at all,” “A little,” and “Some,” and 1 consisting of the responses “A great deal” and “A lot.” For each participant, the absolute value of the difference
43 between the ratings of Democrats and Republicans was computed and summed across each of the attributes, such that a mean of zero would indicate no difference in rating between the two parties, and a mean of one would indicate strong differences on every single attribute. This 5-item scale showed adequate internal reliability, with an overall mean rating of 0.618 (sd=0.329) and a Cronbach’s alpha of 0.71. As expected, based on this composite scale, Independents were significantly less polarized in their ratings of the two political parties (Independents and Democrats: t (168.08) = 6.65, p <0.01; Independents and Republicans: t (168.22) = 7.42, p <0.01; Dem mean = 0.646; Rep mean = 0.678, Indpt mean = 0.405.) However, based on the aggregate polarization scale, neither Republicans nor Democrats exhibited more polarized responses than each other (t (804.64)= -1.49, p =0.135).
Affective Polarization Ratings
200
150
100 N N Participants
50
0
0.0 0.2 0.4 0.6 0.8 1.0 Affective Political Polarization Figure 2.5a Histogram of affective political polarization scores.
Gender and polarization. There was, however a gender difference, with men offering more polarized responses than women (Male mean = 0.643; Female mean = 0.597, t (1) = 2.15, p < 0.05). (Recall that females were more likely than males to identify as Independents / moderates, and as Democrats /liberals.) While a gender gap is well documented in the political literature, it typically emphasizes the differences in party identification between males and females, rather than polarized attitudes. Evans (2003) cites that women have not become more polarized over time along issues positions, consistent with the results of DiMaggio, Evans, & Bethany (1996). However,
44 research does suggest that males have, and are expected to have, stronger political opinions than females (Bem, 1974; Delli Carpini & Keeter, 1993; Koch, 1997; Scheufele, Shanahan & Lee, 2001), which is consistent with the psychology literature, suggesting that there are in fact distinct personality and trait differences between males and females along issues of opinion and aggression (Eagly, 1995; Hyde, 1984; Kling et. al, 1999). Figure 2.5a depicts the male-female differences in polarization across Republican, Democrat, liberal, and conservative identifications, showing that males are consistently more polarized than females.
Figure 2.5b Polarization, by party and gender. Y-axis is the mean polarization level (0= not at all polarized, 1 = highly polarized). Males have higher levels of polarization than females across all political strata.
Partisanship: Negativity, and opinion strength. Overall polarization measures—from the five-attribute composite polarization scale—showed that neither Republicans nor Democrats were significantly more polarized than the other. However, Republicans were significantly more likely to report more negative responses about Democrats: a test of proportions showed that average negative ratings of each group versus the other are in fact statistically different (χ2(1) = 59.15, p < 0.001). Republicans reported more negative ratings (ratings of ‘not at all’ and ‘a little’) towards Democrats,
45 with 67.5% of Republicans attributing negative ratings towards Democrats, and 55.9% of Democrats attributing negative ratings towards Republicans (Table 2.7).
Table 2.7: Percentage of respondents rating the other party negatively, by trait Party Knowl- Compass- Selfish Patriotic Close- Average edgeable ionate minded Negative Ratings Rep ratings 61.1% 62.0% 77.6% 64.6% 72.1% 67.5% of Dems Dem ratings 36.6% 71.1% 71.3% 23.8% 76.9% 55.9% of Reps Note: Selfish and close-minded were reverse-coded to capture the N who rated the other party as "a great deal" or "not at all". The average rating is computed with these recoded variables.
Between-party attribute-level differences. Recall there was no significant difference between Republicans and Democrats on the affective polarization scale. Yet, of interest is detecting which of the five attributes generated the strongest partisan differences. Welch two-sample t-tests (means reported below) assessed which attributes Democrats, Republicans, and Independents were most likely to attribute to Republicans and Democrats. The rating differences between Republicans and Democrats were significantly different along four of the five attributes: patriotism, knowledge, compassion, and closed-mindedness. Meaning, Republicans rate themselves as significantly more patriotic and knowledgeable than Democrats, and Democrats rate themselves as significantly more compassionate and less close-minded. There were no differences along ratings of selfishness (Table 2.8).
Table 2.8: Partisan Attributes and Attitudes Difference between Dem - Rep rating Attribute Republican Democrat Independent Patriotic * 0.796 0.443 0.388 Knowledgeable * 0.646 0.559 0.310 Compassionate * 0.584 0.761 0.314 Closed-minded * 0.656 0.761 0.554 Selfish 0.710 0.703 0.475 * Differences between Republicans and Democrats significant at p <.001.
46 2.4 DESCRIPTIVE EXPERIMENT RESULTS
EXPERIMENT COMPLETION AND DROP-OFF While 1,000 total participants were recruited, there was a noticeable drop-off in participation towards the final stages of the experiment (100% completion across the four trials would result in a total of 4,000 news category clicks and 4,000 news story clicks.) In fact, 990 participants completed at least one, 978 completed at least two, 954 completed at least three, and 840 participants completed all four trials. Note that some participants did not complete the first or second news selection trial, but then later engaged in at least one of the latter three trials. A simple linear regression model was conducted to assess whether there are consistent characteristics of individuals choosing to drop out of the experiment. A “completion” variable was constructed, accounting for the number of experiment trials that a participant completed; the maximum value of this variable is eight, accounting for the sum of four category selections and four story selections (as such, the minimum value for this dependent variable is zero, representing the ten participants who chose not to complete any part of the experiment). Predictor variables were selected based on their use in the following news and source preferences models, to obtain a baseline measure of participation.
Table 2.9 Experiment Completion Number of participants completing stages of the experiment N complete trials 0 1 2 3 4 5 6 7 8 N participants 10 8 6 10 19 18 103 68 758 N= 1,000
Three models of experiment completion are reported in Table 2.9. All models included variables for education, visual treatment condition, polarization, and Web news use, TV news use, and self-reported news interest and discussion. Model 1 accounts for the influence of party ID, Model 2 accounts for the influence of political ideology, and Model 3 includes variables for both party ID and ideology.
47 The consistent effect across all three models is that the individuals who report high levels of television news exposure are more likely to drop out of the experiment. This is possibly because these individuals are not used to actively selecting and reading news in an online format, and perhaps more prefer the passive information consumption that defines television. Another consistent effect on experiment completion is the graphical news layout: the graphical treatment had a significant negative relationship on completion rate, suggesting that those presented with the graphical condition completed fewer trials. As will be discussed in Chapters 3 and 4, the graphical presentation of news content seems to have additional unintended and adverse and effects, one of which is exhibited here: lower experiment completion rates. Further, as a whole, being conservative was significantly negatively associated with experiment completion (see Models 2 and 3), meaning fewer conservatives completed all experiment trials. Yet, there was also a significant interaction between ideology and polarization: the conservatives with stronger polarization were more likely to complete the experiment. There were no consistent effects of total news exposure and interest. While these results do not warrant a modification on the news selection or source preference models that will be later presented, it is worth noting that the conservatives completing this experiment may in fact be the more polarized ones.
48 Table 2.10: Experiment Completion: Logistic Regression Results Model 1: Model 2: Model 3: Party ID Ideology Both (Intercept) 7.55 7.65 7.71 (0.27) (0.26) (0.26) Education -0.01 -0.02 -0.02 (0.05) (0.05) (0.05) Graphical treatment -0.42 + -0.46 * -0.44 + (0.23) (0.23) (0.23) Frequency of Web news use 0.03 0.03 0.03 (0.04) (0.04) (0.04) Frequency of TV news use -0.12 ** -0.10 ** -0.10 * (0.04) (0.04) (0.04) News interest & discussion 0.06 0.06 0.06 (0.07) (0.07) (0.07) Polarization 0.07 -0.38 + -0.31 (0.07) (0.23) (0.23) Republican -0.21 – 0.01 (0.25) – (0.16) Democrat -0.05 – -0.22 (0.24) – (0.16) Education * Graphical 0.09 0.10 0.09 treatment (0.06) (0.06) (0.06) Conservative – -0.54 * -0.60 ** – (0.22) (0.23) Liberal – -0.02 0.07 – (0.26) (0.27) Polarization * Republican 0.27 – – (0.41) – – Polarization * Democrat -0.14 – – (0.41) – – Polarization* Conservative – 1.00 ** 0.90 ** – (0.32) (0.32) Polarization * Liberal – 0.32 0.30 – (0.38) (0.38) N 948 948 948 R Square 0.02 0.03 0.03 Adj. R Square 0.01 0.02 0.02 Residual st error 1.40 1.39 1.39 Model 3 accounts for both ideology and political party, and is referenced in the text. ** p <0.01, * p <0.05, + p <0.10.
49 NEWS CATEGORY SELECTED Before clustering the eight news categories into hard and soft news topics, descriptive selection data was collected for all categories. US Politics was the most frequently selected category (29.7% of all category selections), followed by World (13.7% of clicks), Opinion (12.2%), Entertainment (11.1%), Business (8.7%), Sports (8.3%), Environment (8.3%), and Life & Style (7.9%). Over 70% (N=713) of participants selected US Politics as a new category during at least one of the four trials, and nearly half of participants (N=442) selected US Politics as their first news choice (Tables 2.10 and 2.11).
Table 2.11: N participants selecting a news category, by trial Choice 1 Choice 2 Choice 3 Choice 4 Totals Business 55 (28/27) 88 (43/45) 95 (41/54) 89 (44/45) 327 Entertainment 116 (63/53) 109 (43/58) 110 (48/62) 83 (38/45) 418 Environment 58 (19/39) 65 (51/32) 91 (48/43) 98 (51/47) 312 Life and Style 59 (29/30) 60 (33/27) 69 (38/31) 109 (54/55) 297 Opinion 44 (23/21) 107 (33/51) 165 (77/88) 143 (82/61) 459 Sports 95 (41/54) 78 (56/46) 71 (32/39) 69 (35/34) 313 US Politics 442 (227/215) 328 (176/152) 199 (115/84) 150 (70/80) 1119 World 113 (60/53) 130 (61/69) 132 (70/62) 143 (70/73) 518 Totals 982 965 932 884 3763 Data in parentheses indicates selections in the (text/graphical) condition
Table 2.12: N participants selecting a news category Not at all At least once US Politics 277 713 Soft news 273 717 749 – Sports 241 – Sports 721 – Lifesty 269 – Lifesty 674 - Entert 316 - Entert World 533 457 Opinion 585 405 Business 696 294 Environment 708 282
Repeat Selections. Repeat category selections were also fairly common: 150 participants selected the same category across at least three subsequent trails (Table
50 2.12). Participants were not told that they would be viewing the same stories, so participants may have expected to see different news stories related to the topic, or simply wanted to read more of the existing stories that were shown on the screen. It is clear that the Polimetrix sample of registered voters skews towards interest in politics: 8.5% of participants selected US Politics for the first three trials, much higher than the independent probability of this event (less than 1%.) Conversely, the independent probability of repeatedly selecting any soft news category (three of the eight total categories) across the first three trials is 5.27%, which the participants also exceeded: approximately 8% of participants only selected soft news across three trials.
Table 2.13: N participants making repeat category selections, first three trials Category N Soft News only 75 Entertainment 21 Sports 16 Life & Style 2 US Politics only 80 Opinion only 10 World only 6 Business only 4 Environment only 5
Stability of news choice across trials. It is hypothesized that there is a strong relationship between the news categories an individual selects across all four trials; specifically, that a given category selection is predictive of a future choice. In order to infer the effects of trial on news choice, an 8x8 table was constructed, where columns represent the category selected in the first choice (Trial 1) and rows represent the selections made in the following choice (Trial 2). Analogous tables were computed for all subsequent pair-wise trials (e.g., between Trial 2 and 3, and between Trial 3 and 4). While the eight news categories were ultimately segmented into hard and soft news topics, the relationship between trial and news category was first assessed using all eight categories in order to detect whether any topics behaved uniquely from the rest of the categories.
51 In each table, the behavior of every participant is represented by a single count in each cell. Table 2.14 presents the relationship between category selection in trials 1 and 2, for all participants. For instance, the table depicts that 12 participants selected the Business category in both Trial 1 and Trial 2. Bolded numbers represent which cells exceed the Chi-square expected counts, as inferred through a visual inspection of the expected frequency table. Tests of independence between trials. To assess independence between the news selections made in adjacent trials, a Chi-Square test of independence was conducted. Results show that category selections are certainly not independent, indicating that an individual’s preferences carry across all four trials. While the Chi square values decrease slightly as participants progress through the experiment, the test statistic is still highly significant: Choice 1 and Choice 2 (χ2 (49) = 387.75, p<0.001), Choice 2 and Choice 3 (χ2 (49) =154.32, p<0.001), and Choice 3 and 4 ((χ2 (49) =122.74, p<0.001). While participants may be more likely to branch into alternative categories later in the experiment, their choices are not independent from their prior selection.
Table 2.14: N participants selecting each news category pair in Trial 1 and Trial 2 Choice 1 Busin Enter Envir LifeSt Opin Sports USPol World Business 12 4 4 1 5 6 16 7
Enter 2 39 6 28 9 2 16 10 Environ 5 4 11 6 7 4 8 11 LifeSty 2 16 5 12 5 2 10 4
Choice 2 Choice Opinion 2 3 4 0 16 1 14 2 Sports 7 8 5 2 5 28 30 8 USPol 51 25 23 8 51 28 181 65 World 7 8 6 2 9 7 51 23
Multinomial analysis of repeated category selections. As another test of how news category selections may vary or remain constant across trials, three multinomial regressions were conducted regressing Choice 2 on Choice 1, Choice 3 on Choice 2, and Choice 3 on Choice 4. Specifically: the eight categories alone were used as predictors of a subsequent category selection. Multinomial models were generated with
52 R package nnet (Venables & Ripley, 2002), and the predictive utility assessed with R package pscl (Jackman, 2010). Results again clearly indicate that category selections in earlier trials affect selections in subsequent trials. By using news category alone, Choice 1 predicts Choice 2 with 38% accuracy, Choice 2 predicts Choice 3 with 26% accuracy, and Choice 3 predicts Choice 4 with 23% accuracy.
Repeated hard and soft news selections. Because the news categories were ultimately clustered into hard and soft news, similar tests of independence between adjacent trials were conducted for the binary news type (hard and soft) variables. This time, in addition to aggregate measures, news choice was further segmented according to whether the participant was allocated to the text or the graphical treatment condition (Table 2.14). As an example, in Table 2.14, we can see from the middle column that 67 participants selected soft news in Trial 1 and followed that with a hard news selection in Trial 2. In Trial 2, 66 participants selected soft news, and followed that with hard news in Trial 3; finally 71 participants selected soft news in Trial 3 and hard news in Trial 4. As we might also expect unique behaviors based on visual display, this comparison is a preliminary step in assessing whether visual display affects the occurrence of repeat category selections. The key feature here is of course the diagonal, emphasizing the consistent influence of prior behavior choice. Predictive accuracy, as expected, is also greatly improved by using the binary hard and soft news clusters: Choice 1 predicts Choice 2 with 76% accuracy, Choice 2 predicts Choice 3 with 73% accuracy, and Choice 3 predicts Choice 4 with 70% accuracy. In fact, the graphical news display has a significant effect on news category selections. Specifically, all Chi-square tests of independence between selections made in the graphical treatment remain significant across all trials, indicating a consistent influence of prior choice. However, a non-significant Chi-square test of independence between trials 3 and 4 in the text-only condition reveals that these trials are in fact independent from one another (Table 2.15). As such, there is some indication that the text-only condition, when compared with the graphical, may diminish the influence of previous choice. These results indicate that accounting for previous choice in the
53 comprehensive mixed model analyses will be necessary to fully vet the impact of these variables.
Table 2.15: N participants selecting same news categories in Trials 1-4 Choice 2: Graphics Choice 2: Text Choice 2:All
Soft Hard Soft Hard Soft Hard News News News News News News Soft 74 57 63 67 137 124 News Choice 1 1 Choice Hard 56 292 51 299 107 591 News Choice 3: Graphics Choice 3: Text Choice 3: All
Soft Hard Soft Hard Soft Hard News News News News News News Soft 57 66 47 66 104 132 News Choice 2 Choice Hard 75 260 70 279 145 539 News Choice 4: Graphics Choice 4: Text Choice 4: All
Soft Hard Soft Hard Soft Hard News News News News News News Soft 51 69 38 71 89 140 News Choice 3 Choice Hard 80 227 86 233 166 460 News Bolded values indicate instances when the observed values exceed the expected values, according to the Chi-square expected values table.
Table 2.16: Chi-square tests of Independence between adjacent trials: Segmented by visual treatment Trial Graphical Text Treatment Aggregate Treatment Choice 1 and 2 χ2 = 76.51 ** χ2 = 58.26 ** χ2 = 136.34 ** Choice 2 and 3 χ2 = 24.01 ** χ2 = 19.81 ** χ2 = 45.34 **
Choice 3 and 4 χ2 =10.21 ** χ2 = 2.097 χ2 = 11.62 **
54 2.5 ANALYSIS: HARD AND SOFT NEWS SELECTION
HYPOTHESES: FACTORS AFFECTING THE SELECTION OF HARD AND SOFT NEWS A comprehensive mixed effects model predicting hard news selection in each trial was conducted using the lme4 package in R (Bates, Maechler, & Bolker, 2010) to account for all fixed and random experimental effects. For the purposes of the experiment, soft news was defined as the three explicitly soft categories – Entertainment, Sports, and Life & Style. Hard news was classified as the remaining five categories: US Politics, World, Business, Environment, and Opinion. As previously described, a second model, excluding Opinion from either category, was conducted to assess the fit of that category as hard news, and what differences it might produce. To recap, nearly 30% (N= 1,028) of the total news selections were of soft news categories, and the remaining 72% (N= 2,735) of selections were made on hard news categories. In terms of individual participants, about a third of participants in the experiment were averse to clicking on US Politics, and nearly another third were averse to selecting any of the soft news categories (277 individuals did not select US Politics during any of their four trials, and 273 individuals did not select soft news during any of their four trials). In order to assess which factors encourage an individual to select hard news, a binomial variable was constructed, with a 1 indicating that a hard news category was selected on that trial, and a zero if otherwise. A logistic mixed effects model (with trial and participant as random effects) (Bates, 2010) was used to predict hard news selection in trials 2-4. As this model excluded the first trial, a separate logistic regression was computed to predict the factors selecting hard news selection in only the first trial. Fixed effects (a) Visual treatment: text-only or graphical. Recall that the visual treatment affected only the news story screen—the second experiment phase—and did not alter the appearance of the news category selection page. Despite this, we may hypothesize that once assigned to a treatment condition, the graphical display of news may make soft news content (with its photos of celebrities and entertainment) more appealing in subsequent choices.
55 (b) Previous choice. As described, an individual’s prior news selection has a significant impact on their later news category selections. In order to assess this in the mixed model, a lagged choice variable was computed, accounting for the prior choice (i.e. choice 1, 2, and 3) associated with trials 2-4. (c) Trial. Individuals may be more likely to select hard news in earlier trials, perhaps exaggerated by experimental artifacts, such as the social-desirability of appearing interested in hard news. Further, we are also interested in assessing the stability of an individual’s preferences over time. (d) Total News Exposure1. The regression models included variables account for total news exposure, which was comprised of a three sub-measures. Assuming consistency with self-reported and experiment behaviors, we will expect that total news exposure will positive and significantly predict hard news selection in the experiment. a. Web News Exposure. Individuals who self-reported greater levels of Web news exposure should be more likely to select hard news in the experiment, assuming consistencies between self-report data and actual behavior. Web and TV news were separated, as there is some indication that different demographics rely on either Web or TV, and there is naturally a difference in the level of engagement required to seek out Web versus TV news. b. TV News Exposure. As above, we do expect more TV news exposure to increase hard news selection in the experiment. However, we do decide to distinguish TV news, as receiving broadcast news is different than actively selecting it, as in this experiment. c. Self-reported news interest and discussion. We hypothesize that self- reported interest and discussion in current affairs is actually correlated with hard news selections in the experiment.
1 While these three measures of news use are separated, a different model, using one aggregate measure of total news exposure was also calculated. There are no major differences, yet separating the media types and self-reported behaviors is preferred as it offers more granularity, and is recommended by previous research (e.g., Druckman & Parkin, 2005; Price & Zaller, 1993).
56 (e) Polarization. Highly polarized individuals may be more likely to select hard news. By definition, individuals who score high in polarization have stronger opinions about political parties, and existing research shows that this is correlated with increased political engagement. By extension, these individuals might be more likely to seek out hard news. (f) Education. Individuals with higher education levels should be more likely to select hard news, as higher socio-economic status has previously been correlated with political knowledge and interest. While education was provided as a 6-level categorical variable (1= no high school, 2= high school, 3=some college, 4= 2-year college, 5=4-year college, 6=post- graduate), there was no definitive linear trend across the 6 data positions. To simplify analyses, education was treated as a binary variable, with low levels (less than 2-year college) and high levels of education (college or post- graduate degree). (g) Gender. Research has repeatedly shown that women are less interested and involved in politics. Therefore, we may hypothesize that women will also be less likely to select hard news in this experiment, when given the choice. Random effects. Participant. The linear mixed model accounts for the random variance attributed to each participant’s behavior across the four trials.
2.6 RESULTS
TRIAL 1 NEWS SELECTIONS. A logistic regression was constructed to assess which demographic factors affect selection of a hard news topic in the first experiment trial. Two additional models were constructed to ensure no main effects differences of party identification and ideology: Model 2 regressed party identification on hard news selection in Choice 1, and Model 4 regressed ideology on hard news selection in Choice 1. All models are reported in Table 2.16. Model 3 showed a marginally significant effect of being Conservative on hard news selection (β = .31, Z= 1.88, p = 0.06), which translates to a 36.3% increase in odds
57 from being moderate (the baseline). There were no effects of party identification in Model 2. There were also remarkably few demographic predictors of hard news selection in the first trial. The model included basic demographics and self-reports of news exposure and interest: education, gender, age, polarization, web news use, TV news use, and self-reported news interest and political discussion. Significant effects included age (β = -0.04, Z= -1.99, p < 0.05) and the interaction between age and self- reported news interest (β = 0.01, Z= 2.14, p <0.05). Essentially, these results show that the relationship between self-reported news interest and selection between hard news is stronger for older individuals—notably, that when older individuals report having a high or low interest in politics, their actual behavior matches their reported interest level. When older participants reported high interest in news, they were more likely to select hard news, and when they reported less interest in news, they selected more soft content during the experiment. Conversely, when younger individuals report having high news interest, they are no more likely to select hard news than younger individuals who profess no news interest. These results are somewhat disappointing as they present a very vague picture of what individual characteristics are most likely to encourage an individual to select hard or soft news. It is well documented that self-reports of news media use are frequently inaccurate and variable, and these results partially confirm the existing research. Further, there were no significant effects of education or gender on hard news selection, which is somewhat surprising as both of those qualities are associated with more political knowledge, news interest, and news media use. However, it is possible that the sample of registered voters already attracts individuals with high levels of news usage, and there may be little inter-group variability when assessing the individuals who have actually expressed some political initiative by registering to vote.
58 Table 2.17 Hard News Selection in Trial 1 Logistic Regression Results Model 1 Model 2 Model 3 Party ID Ideology (Intercept) 2.270 * 0.767 * 0.789 * (0.914) (0.193) (0.115) Education (high) 0.028 — — (0.159) Age -0.039 * — — (0.020) Polarization 0.063 — — (0.236) Gender (female) -0.110 — — (0.153) Self-reported news interest and -0.395 — — discussion (0.292) Frequency of Web news use -0.022 — — (0.057) Frequency of TV news use -0.028 — — (0.067) Age * Self-reported news 0.013 * — — interest (0.006)
Republican — 0.358 — (0.226) Democrat — 0.106 — (0.221) Liberal — — 0.258 (0.184) Conservative — — 0.310 + (0.165)
N 931 958 982 AIC 1106.90 1132.19 1157.15 BIC 1281.01 1190.57 1215.82 Log Likelihood -517.45 -554.10 -566.57 * p <0.05
59 TRIALS 2-4: NEWS SELECTION, MAIN EFFECTS ONLY First, a model was constructed that included only main effects to assesses hard news selections in experiment trials 2-4. Predictor variables were the same as in predicting news choice in the Trial 1 (education, gender, age, polarization, news interest, web news use, and TV news use), and included additional variables for trial (a four-level factor) and previous news choice (a two-level factor denoting whether the prior news selection was soft or hard). This model with main effects showed a marginally significant negative effect of gender (Model 1: β = -0.161, Z= -1.70, p = 0.09), indicating that being female is associated with a 15% decrease in odds of selecting hard news selections, as opposed to males. As hypothesized, previous news choice had a significant positive effect on subsequent selections (Model 1: β = 1.12, Z=11.75, p < 0.001), meaning that choice is highly dependent upon the prior selection, increasing the odds of hard news selection by 206%, compared to a previous soft news choice. Trial number also had a significant negative effect on the selection of hard news: trial 4 was significantly and negatively associated with fewer hard news selections (β = -0.28, Z= -2.55, p < 0.05), a 24% decrease in odds from trial 2 (the baseline). These results would indicate that as the experiment progressed, the selection of soft news becomes increasingly common. However, as the following section explains, the effect of trial is more nuanced than what the main effects model suggests: experiment trial produced unique effects dependent on prior selections.
TRIALS 2-4: NEWS SELECTION, INTERACTION TERMS To fully vet any potential interaction between trial and previous choice, a full model was constructed accounting for hypothesized interaction terms. Hypothesized interactions included: (1) an interaction between gender and polarization, as affective polarization was shown to be stronger in males, (2) an interaction between previous choice and trial – as perhaps the influence of prior selection diminishes as news reading progresses, (3) an interaction between graphical display and education – as the visually appealing format may have a unique effect based on education, and (4) an interaction between age and self-reported news interest, as was shown to be present in the model predicting news choice in Trial 1.
60 Table 2.18: Hard News Selection in Trials 2-4 Generalized Linear (Logistic) Mixed Model Results Model 1: Model 2: Hard News Hard News Main Effects Interactions (Intercept) 0.420 -0.429 (0.28) (0.591) Education (college grad +) 0.107 0.156 (0.098) (0.140) Graphical Treatment -0.122 -0.289 * (0.091) (0.115) Polarization 0.201 0.587 ** (0.145) (0.213) Gender (female) -0.161 + 0.260 (0.192) (0.194) Age -0.002 0.002 (0.003) (0.012) Reported news interest & discussion 0.004 0.089 (0.070) (0.181) Web news use -0.009 -0.011 (0.035) (0.036) TV news use 0.014 0.026 (0.041) (0.042) Trial 3 -0.111 0.385 * (0.111) (0.187) Trial 4 -0.283 * 0.598 ** (0.111) (0.190) Prior News Choice (hard) 1.124 ** 1.846 ** (0.095) (0.168) Education * Graphical Treatment __ 0.504 * (0.196) Age* Self-reported news interest __ -0.001 (0.004) Polarization * Gender __ -0.697 * (0.279) Trial 3 * Previous Choice __ -0.814 ** (0.238) Trial 4 * Previous Choice __ -1.374 ** (0.238) AIC 2921 2885 BIC 2997 2991 Log Likelihood -1447 -1425 N obs = 2601; participants = 927 Random effects participant variance: 0
61 The final model showed a significant positive relationship between hard news selection and polarization (Model 2: β= 0.59, Z= 2.76, p < 0.01), increasing the odds by 80% for selecting hard news over soft. As hypothesized, the graphical display of news (Model 2: β = -0.29, Z = -2.52, p < 0.05) also had a significant negative relationship on hard news selection, decreasing the odds of hard news selection by 25%. As the earlier data suggested, the lagged (previous) choice was highly significant in predicting hard news selection (β = 1.85, Z = 10.97, p<0.001). In fact, this was the most influential variable, increasing the odds of selecting hard news by 533%, as compared to the baseline of a previous soft news choice. The 4th experiment trial also had a significant positive effect on selecting hard news (Model 2: β = 0.60, Z=3.15, p < 0.01), as did the 3rd trial (Model 2: β= 0.39, Z =2.06, p < 0.05), increasing the odds of hard news selection by 82% and 47% respectively. There were no significant main effects for reported news interest and exposure, gender, education, and age. Recall that while self-reported news exposure and interest was insignificant in these models, it is to some extent represented in the lagged choice variable, as it positively affected hard news selection in the first trial. Interactions. The first interaction emerged between polarization and gender (β= -0.70, Z=-2.50, p < 0.05), a plot of which is depicted in Figure 2.5. This interaction suggests that the relationship between polarized attitudes and hard news selection is limited to males: being a polarized male increases the likelihood of selecting hard news, while this effect of polarization does not persist for females (in fact, polarization may even slightly decrease a female’s likelihood of selecting hard news). The second interaction emerged between education and graphical treatment (β = 0.50, Z = 2.57, p <0.05), indicating that while the graphical treatment is negatively associated with hard news selection, the soft-content-inducing effects of graphical display appears limited to those individuals with lower education levels, and more educated individuals actually are more likely to select hard news in this display. The partial effects plot of this interaction is depicted in Figure 2.6. Finally, there emerged highly significant negative interactions between trial 4 and previous choice (β = -1.37, Z =-5.77, p <0.001), and trial 3 and previous choice (β = -0.81, Z =-3.43, p <0.001). This data can be evidenced in the partial effects
62 interaction plot in Figure 2.7. These interactions suggest that the influence of the prior news selection decreases throughout the experiment, and that individuals become more apt to select the opposite news type as the experiment progresses.
Trial and Hard News Choice: Model with main effects model only 0.61 0.59 0.57 Mean Newstype Choice Newstype Mean 0.55 2 3 4
Trial
Trial and Hard News Choice: Model with interaction between trial & previous choice 0.64 0.60 0.56 Mean Newstype Choice Newstype Mean 0.52 2 3 4
Trial
Figure 2.6 Effect of ith trial on hard news selection: main effects versus interactions The above figures demonstrate that when ignoring the interaction between ith trial and lagged choice, it appears that participants are overall more likely to select soft news in subsequent trials. However, when including an interaction between the prior selection and subsequent news choice, it becomes clear that the influence of prior news selection significantly decreases –those who had previously selected soft news later select hard; those who selected hard news in earlier stages of the experiment become more likely to select soft content.
63
Effects of Polarization and Gender On Hard News Selection
0.0 0.2 0.4 0.6 0.8 1.0 Male Female
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Selection of Hard News Interaction Between Polarization and Gender 0.58 0.56 0.54 0.52 Female 0.50 0.48 Probability of Selecting Hard News Hard of Selecting Probability 0.46
Male 0.44 Gender
0.0 0.2 0.4 0.6 0.8 1.0
Polarization Scale: Low to High Figures 2.7a and 2.7b. Interaction between gender and polarization: marginal effects plot. The x-axis represents the degree of polarization. Y-axis represents the estimated value (top panel; confidence intervals approximated with a traditional glm ignoring random effects using effects package) and probability (bottom panel; plotted fitted values from random effects model using languageR package) of hard news choice. As males become increasingly polarized, their likelihood of selecting hard news increases; this effect does not exist for females.
64 Effects of Education and Graphical Display On Hard News Selection
Low High Text Graphic 0.8
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0.72 Estimated News Selection (0 Soft, 1 Hard) Selection News Estimated
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Figures 2.8a and 2.8b. Interaction between education and visual treatment In top figure, y-axis represents the estimated hard news selection, and the x-axis represents education. Confidence intervals approximated with a traditional glm ignoring random effects using effects package. Left panel represents text, right represents graphical news layout. The graphical condition encourages more hard news selection for those with high education. The partial-effects plot in bottom figure represents the interaction between education and display; education is on x- axis, and the two slopes are partial effects probabilities of selecting text or graphical news display. Plotted with languageR package.
Selection of Hard News Interaction Between Education and Visual Display 0.54
Text 0.52 0.50 0.48 Probability of Selecting Hard News Hard of Selecting Probability
0.46 Graphic Visual Display Visual
Low High Education: Low or High (College grad)
65 Selection of Hard News Interaction Between Trial and Previous Choice 0.9 Prev Choice Hard 0.8 0.7 Probability of Selecting Hard News Hard of Selecting Probability 0.6
Prev Choice Soft 0.5 Previous Choice Previous
2 3 4 Trial Number: 2, 3, 4 Figures 2.9a and 2.9b. Interaction between experiment trial and previous choice Y-axis in the top figure represents probability of hard news selection (plotted with languageR), and the x-axis represents the trial number. The previous news choice (soft, hard) are interaction lines. Bottom figure plots the estimated news value (y-axis; confidence intervals approximated with effects package using traditional glm) and the prior news choice (soft or hard) is in separate panels.
Effects of Trial and Previous Choice On Hard News Selection
2 3 4 Prev Choice Soft Prev Choice Hard
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66
2.7 DISCUSSION: HARD NEWS & SELECTIVE EXPOSURE Polarization. Results from the regression model suggest that hard news is more likely to be selected by males who are highly polarized, with polarization having little to no effect on females’ selection of hard news content. This partially affirms the hypothesized relationship between polarized political attitudes and hard news interest. As existing research suggests, polarization is frequently associated with political involvement and interest, yet this experiment documents that any political “advantage” of polarization is limited to males. Interestingly, while females are on average less polarized than males, their likelihood of selecting hard news is fairly consistent across all strata of polarization; conversely, there is a much greater gap in political interest between the males who are polarized, and the males who aren’t. This perhaps suggests that there may be greater within-gender knowledge gaps for males than for females, attributed to opinionated and partisan attitudes. Further research should more carefully assess the relationship between gender and affective political polarization, assessing the extent to which negative reactions to the opposing party labels may be predominately a male symptom. Nearly all of the comparative gender and politics research assesses the differences between males and females while holding certain demographics constant, such as education, income, or age. Instead, perhaps a better control may be the relative opinion strength of each gender, and how these beliefs may transfer to hard news selection. The political gender gap may simply be exacerbated by the weaker political opinions of females, an hypothesis that is somewhat supported by other research: specifically that the ideological and knowledge differences between males and females diminish when evaluating women-centric issues (such as abortion or womens’ rights) and when assessing recognition of female political candidates – both topics that women are likely to be more opinionated about. It is quite possible that women are selective about the political issues they attend to, and are less motivated by general, or male- dominated political talk. Visual design of news. The presentation of news content had strong effects on which type of news individuals chose to select. The likelihood of selecting hard news
67 significantly decreased in the graphical condition: when reading news in this graphical and pictorial format, participants were overall more likely to select soft news. This finding has far-reaching implications for the future of news Web design: as the visual presentation of news content alone can encourage individuals to select hard or soft content, news organizations should think ever more critically about the user-interface decisions regarding news content. One possible explanation for such a dramatic preference for soft news is that a news layout with greater visual aesthetic is simply more rewarding to view. Research has shown that attractive faces – such as those of celebrities and athletes that accompany entertainment and sports stories – stimulate neurological reward signals (Aharon et al, 2001; Bray & O’Doherty, 2007). When individuals view news stories that are supplemented with attractive photos, they may feel encouraged to again select that type of information. Education and visual design. Even more striking, the effect of news design and page layout was mediated by education level. While overall, individuals in the graphical condition were more likely to click on soft news, this effect was stronger for those with lower education levels. When more educated (defined as at least a college degree) viewed the graphical layout, they were more likely to select hard news. This is an important finding, particularly in light of attempts to resolve the political knowledge gap in American society, between the well-educated and well-informed, and the less- educated and less-informed. It seems that presenting news in a graphical visual display is perhaps aesthetically pleasing, yet may only further propagate existing inequalities in society, by providing more incentive for the less-educated to avoid reading substantive content and instead seek out soft news. More educated individuals may simply be less likely to succumb to the temptation that is soft news and celebrities photos. This finding is somewhat contradictory to the work of Jerit, Barabas, and Bolson (2006), who find that television may help to level the political knowledge gap between the more and less educated—by facilitating stronger recall and memory of content—whereas the cognitively dense format of newspapers serves to widen it. The key difference here is that Jerit, Barabas, and Bolsen analyzed knowledge gain, while this study assesses news preferences. A text-only display may do little to attract people to entertainment content,
68 but when entertainment content is combined with appealing photographs in the graphical layout, it inevitably becomes the more attractive choice. Self-reported news interest and exposure. While self-reported interest and exposure to news content was not significant in the final models, it was a somewhat significant predictor of hard news selection in the first choice. However, this was only an accurate predictor for older individuals, who perhaps seem better equipped to more accurately measure their actual levels of current affairs interest. As such, self-reported interest in news does initially steer individuals towards subsequent selection of hard news content, though adds nothing else beyond the first selection. This finding suggests that researchers might be better served by creating another measure of news interest, to assess the stability of news preferences over time rather than single unit measures of exposure. The experiment results reveal that even while an individual reports high levels of news exposure and interest, as the experiment progresses, they are just as susceptible towards selecting soft news. It has long been known that self-reports of media use and exposure can be wildly unreliable (Eveland, 2009; Prior, 2009a; Prior, 2009b; Tewksbury, 2010; Vaverk, 2007). This experiment offers one explanation as to why that might be the case, particularly given the reliability gap between the younger and older demographics to report behavior. One hypothesis explaining inflated measures of media exposure is social-desirability—individuals simply want to appear to be more interested in news than they actually are, or simply an innocent inability to accurately estimate actual news usage and interest. That self-reported news behaviors positively relate to the selection of hard news in only the first experiment trial indicates that this behavior could also be due to social-desirability: throughout the experiment, participants are less influenced by any invisible social pressures. An alternative explanation could simply be that while participants may genuinely have a strong interest in hard news (corresponding to their self-report data), this interest is not easily sustained – once individuals have whetted their appetite with a minimal amount of hard news, their tastes then changes to desire more soft content. Effects of experiment trial: Diversity in the news diet? News selection preferences changed significantly throughout the trials, but most striking in the fourth and final trial. Individuals more interested in news earlier in the experiment ultimately
69 became tempted by soft news; individuals most interested in soft news earlier in the experiment ultimately ventured into hard news topics. For those individuals starting with hard news topics, the fact that they ultimately selected soft news later in the experiment offers some indication that individuals in fact appreciate some diversity in their news diet – analogous to having some “dessert” after the substantive main course. However, the reverse is true for those individuals who began the experiment by selecting soft news. That these individuals ultimately selected hard news in the final stages of the experiment is in some ways akin to news acquisition behaviors of the inadvertent audience – where people simply consume news content because it is just “there,” without making an effort to go elsewhere. It should also be noted that there were only three explicitly “soft news” topics included in the experiment, contrasted with five hard news choices. Thus, if individuals are in fact driven by some diversity in their news diet, there will naturally be a skew towards hard news selection in the final trial: if soft news readers wanted a new category of news content, they would inevitably have to venture into hard news content at the fourth trial. While these results seem to offer promise towards an increasingly diverse news diet, it is very difficult to impose similar limits to choice online. While inundating a single news page with hard news may seem simple in the context of a single website, individuals can just as easily visit a different news source. Thus, in keeping with the competitive news marketplace, news organizations may see little incentive in promoting hard news on their page when competing with other news organizations. One only needs to look at the “news” offerings on tablet news applications; for instance, The Daily, an application for the ipad (created by the media conglomerate News Corp, which also owns Fox News and Dow Jones), offers potential news consumers an option to select from the following categories: “’News’, ‘Gossip’, ‘Opinion’, ‘Arts and Life’, ‘Apps and Games’, and ‘Sports’.” Clearly, only one of these six options is likely to provide substantive content.
70 2.8 DISCUSSION AND FUTURE WORK Visual Display of news. As discussed, both individual and environmental factors affect the selection of hard news content. Namely, the graphical display of news content seems to have two negative effects: when news is presented graphically, individuals are (1) more likely to drop out of the experiment before fully completing all trials, and (2) more likely to select soft news content. Yet, the effects of graphical news display are not constant for all education strata: when highly educated individuals encounter a graphical display, they in fact select more hard news content. There are several possible interpretations why the visual display of news has such a profound effect on news exposure and selection. One positive interpretation of the higher drop- off rates in the graphical condition is that this layout makes it easier to absorb the gist of news stories in a quicker amount of time, as graphic and pictorial imagery is easier to cognitively process than a dense text display. This may make individuals less motivated to continue the experiment, as they were able to quickly scan more news stories, and perhaps feel informed (or bored) more quickly. A different interpretation suggests that the pictures and imagery in a graphical layout promote a “softer” or less serious attitude towards news acquisition, and simply discourage individuals from reading substantive content. Also, as described above, the graphical layout may simply make soft news stories more appealing and rewarding to view. Individuals may repeatedly seek out the pictures of attractive celebrities and athletes, as this stimulates neurological reward feedback processes. Regardless, by only analyzing the click behavior of this experiment, it is difficult to fully understand the impact of graphical news display. Selection behavior does not reveal what parts of a page individuals may be looking at, nor how many stories they may be viewing. To answer some of these questions, Chapter 4 presents the results of an eyetracking study, where study participants viewed both the text-only and graphical news conditions. The eyetracking metrics better explain how people are processing text-only and graphical news format; in particular, which parts of the news story an individual is looking at. Self reported news interest and Polarization. As described, higher levels of affective political polarization lead to more selections of hard news content, though this
71 effect holds predominantly for males: affective political polarization has no effect for females. The construct of affective polarization used in this study taps into opinionated responses towards in- and out- group sentiments. However, as will be discussed further in Chapter 4, this measure of polarization measures more or less the overall strength of convictions, not how likely an individual is to change these convictions. In other words, when attempting to understand polarized attitudes towards political information consumption, it may also be important to include a construct of willingness to change, or desire for content diversity. In fact, this type of measure may be more influential than political polarization in determining an individual’s relative constancy or inertness of hard news selections. Similarly, measures of self-reported news interest and exposure merely tap into the volume of news consumption, and not how much individuals desire the same, or diverse content in their news diets. Further studies that include self-report measures should attempt to capture an individual’s susceptibility to change – in other words, how easily swayed an individual may be to seek out a variety of news types.
Sample. It was also surprising to see that so many of the recruited study participants were interested in selecting US politics and hard news topics. One potential explanation for this is due to the Polimetrix sample skew towards registered voters. While recruiting registered voters is important to assess how well news display and polarization affect the subset of the electorate actually participating in politics, it likely follows that more polarized or news-savvy individuals were recruited to participate1. Despite that, if these effects of news display and polarization are as strong as they are for registered voters – the citizenry most interested in politics – then it should follow that these effects may be even more pronounced in the larger population. A future
1 Note that 911 of the 1,000 participants recruited for this study were registered to vote, which is in fact greater than the proportion of registered voters among the United States voting eligible population. The Census reports that in 2009, 71% of the voting age citizen population was registered to vote (http://www.census.gov/newsroom/releases/archives/voting/cb09-110.html). While there is 20- point disparity between the Polimetrix sample and that of the US population, there are still some clear benefits to sampling such a subset of registered voters. Namely, registered voters are likely a more politically-minded subset of the electorate, so the effects present in this dataset may be more pronounced when considering non-registered voters. Further, there were no main effects of voter registration in the models generated; tables available upon request.
72 study could intentionally include a 50-50 split between registered and non-registered voters to assess whether there are in fact differences in levels of news interest and engagement between the different subsets of the population, and whether in fact self- reported news interest has a greater effect.
News category offerings. Another potential reason for the bias towards hard news selection could simply be the explicit presentation of news category listings. While individuals do in fact see news category headings on live news Websites, social- desirability bias in the experimental setting may have encouraged experiment participants to simply select hard news topics due to the nature of the experiment. A future design of this study should assess hard and soft news interest in a more nuanced way: namely by presenting with participants the stage of news story selection, whereby six news stories represent a unique—unlabeled—news category (e.g., 3 hard, 3 soft). Presenting participants with news content without a category label may be closer to their experience on real-world news sites, and may produce less social pressure to select hard news content. In addition to non-labeled news categories, an equal balance of hard and soft news topics may better assess the effects of experimental trial. The results in this experiment revealed that participants were more likely to select hard news in the latter trials – perhaps when they had exhausted, or nearly exhausted, their soft news arsenal. An equal distribution of hard-soft news may diminish this effect, or a skew towards soft news (e.g., 5 soft news topics and 3 hard news topics) might reveal a converse effect. Designing an experiment in one of these latter designs may be able to more thoroughly assess sustained interest in hard news, and the desired mix of hard and soft content.
73 74 CHAPTER 3:
POLITICAL POLARIZATION: NEWS SOURCE AND PARTISAN CUES
ABSTRACT This chapter presents results of an N=1000 online experiment to assess how political ideology, partisan affiliation, and polarization affect preferences for a given news provider. News sources were clustered into right, left, and neutral leaning sources, in accordance with previous research and the results of a pre-experiment survey, where participants rated their perceived fairness of news organizations. Results revealed that political orientation does affect the news sources that participants select – notably in the first two trials of the experiment. Further, preferences for a specific source are quite stable over time—as the experiment progressed, individuals rarely deviated from their original sources, and when they did it was more likely to be a politically neutral source rather than the opposite ideology. This lends additional support to the argument that that the ideological division between media outlets may be growing increasingly stronger. The graphical display of news, which offered a prominent depiction of the news organization brand, also had an effect on source selection, in that partisans were more likely to repeatedly select the same source as compared with text-only displays of news. Finally, it is important to note that loyalty towards a specific news provider may in fact be stronger than the influence of ideology: when liberals or conservatives chose an ideologically opposite news source, they showed strong allegiance to this source throughout the experiment, and were just as likely to repeatedly select the source as someone whose ideology matches that of the news provider. Thus, while partisan selectivity towards news choices may in fact be a prevalent phenomenon, it is likely much more nuanced than previously suspected.
75 3.1 DESCRIPTIVE RESULTS
SOURCE SELECTIONS AND COMPLETION
Recall from Chapter 2 that participants first selected a category of news to read. Upon selecting a news category, participants were directed to a page with six news stories for that given topic; each news story was randomly attributed to one of six sources, and randomly placed in one of the six grid locations on the page. Further, there was a between-subjects manipulation, with half of the participants viewing news in a text-only format, and the other half viewing news presented as a graphical display. Participants completed the cycle of news category and story selection a total of four times. Fewer participants completed the news story selection portion than the news category selection stage. While there was a total of 3,763 news category selections, there were only 3,611 news story selections (indicating that in 152 instances, a participant chose a news category, but did not follow up by also clicking a specific story). Using a Chi squared test of proportions, this difference in completion rates between the news category selection phase and the news story selection stage is significant (χ2 (1)= 39.514, p < 0.001): 94.1% completion rate for the news category selection, and 90.3% completion rate for story selection). 935 participants selected a story on the first trial, 936 selected a story on the second trial, 899 selected a story on the third trial, and 841 selected a story on the final fourth trial. There was a significantly higher participant drop-off in the graphical condition, which produced 1,782 total story selections, as compared with the text-only condition, which produced 1,829 selections (χ2 (1) = 6.025, p = 0.01; 89.1% completion in the graphical condition and 91.4% completion in the text). For a discussion on why completion rates may differ, see Chapter 2. One possibility is the graphical treatment simply encourages fewer clicks, as more visual context and story can be gleaned.
SOURCE PREFERENCES: DESCRIPTIVE DATA This first section describes results about all unique sources, prior to the left- and right- leaning source clustering. Overall, Fox News generated the most total clicks, accounting for 698 of the total 3,611 completed story selections (19.3%). Fox News is
76 currently one of the most popular news providers, with an estimated 23% of adults reported tuning into Fox News (Kohut et al, 200). The second most selected source was The Wall Street Journal (which received 18.0% of clicks), CNN (16.3%), then The New York Times (16.0%), USA Today (16.5%), and The Huffington Post (13.8%). To determine whether this difference in proportions is significant, a Chi-square test was conducted, evaluating the null hypothesis of an equivalent number of selections to each source. The Chi-square test was significant, rejecting the null hypothesis that each source received an equivalent number of selections (χ2 (5) = 37.97, p < 0.001), Visual treatment and selection rate. There were no significant differences in the proportion of times a source was selected based on the treatment condition: each source was selected a statistically equivalent number of times in the text and graphical conditions (χ2 (5)= 1.61, p = 0.899) (Table 3.1). Note that this analysis assesses only the raw frequency of source selections across the two conditions, without accounting for the random effects of participant. The source-specific mixed effects models reported later report the fixed and random effects associated with source selection, including interactions between visual treatment, partisanship, and overall news interest.
Table 3.1. N selections to News Source, by Treatment Condition Source Text Graphical Total clicks Chi-Sq CNN 299 291 590 FOX 348 350 698 HUFF 261 238 499 NYT 299 280 579 USA To 303 292 595 WSJ 319 331 650 Totals 1829 1782 3611 1.61
News category and source. It is hypothesized that preferences for selecting a given news source are dependent on the type of news being read; specifically, source may be a less-important differentiator when selecting content in soft news categories. While previous research suggests that, at least for the case of Republicans, partisan source preferences persist across all news type (Iyengar & Hahn, 2009), there is still reason to believe that hard news topics may produce stronger incentive to seek out a specific source. Indeed, results of Chi-square tests reject the null hypothesis that the
77 proportion of clicks within each news category is equally distributed by source (χ2 (35) = 67.90, p< 0.001). Based on a visual inspection from the expected values table, it is clear that certain sources were more frequently selected for hard news categories (e.g., Fox News, and The Wall Street Journal), and other sources were preferred for soft news topics (e.g., USA Today). Bolded counts in Tables 3.2a-3.2c indicate the instances when the observed frequency greatly exceeds the expected values. For example, Table 3.2a shows that 77 (24%) of the participants who selected Business did so within the Wall Street Journal source. Table 3.2a shows data for all news categories and news sources, while Table 3.2b and 3.2c partition news along the hard- and soft- divisions. As a point of reference, Table 3.2c excludes the Opinion from the hard news category, as there may be debate about whether this should be classified as hard news. (For more details about the fit of Opinion as a hard news topic, see Chapter 2.) Note that the inclusion of opinion as hard news does not significantly alter the proportion of hard- to soft- news selections by source, and as such, for data comprehensives, opinion is retained as a hard news category in subsequent source analyses.
Table 3.2a Number of selections to a news source, by news category CNN Fox Huff NYT USAT WSJ Total Business 46 82 31 46 39 77 321 Entertainment 68 66 60 57 81 66 398 Environment 47 54 44 51 54 54 304 Life and Style 46 41 41 50 50 48 276 Opinion 72 74 58 83 67 92 446 Sports 52 43 48 51 48 49 291 US Politics 155 227 147 166 193 190 1078 World 104 111 70 75 63 74 497 Totals 590 698 499 579 595 650 3611 χ2 (35) = 67.90, p < 0.001
78 Table 3.2b. Number of selections to a news source, by hard* and soft news CNN Fox Huff NYT USAT WSJ Total Soft News 166 150 149 158 179 163 965 Hard News 424 548 350 421 416 487 2646 Totals 590 698 499 579 595 650 3611 *Opinion is included as hard news χ2 (5) = 17.30, p < 0.01
Table 3.3c. Number of selections to a news source, by hard and soft news Excluding the Opinion category CNN Fox Huff NYT USAT WSJ Total Soft News 166 150 149 158 179 163 965 Hard News 352 474 292 338 349 395 2200 Totals 590 698 499 579 595 650 3165 χ2 (5) = 18.87, p < 0.01
Location influence on story selection. A Chi-square test was used to test the null hypothesis that there is an equal number of story clicks to each of the six grid locations. The null hypothesis was supported, indicating there was no significant influence of location on story selection between the two treatment conditions (χ2 (25)= 25.22, p = 0.45). While proportions were not significantly different between layouts, there were still about 10% more story selections to the “first” news story—in the upper left screen location—when compared to the second most selected grid region (Text: 352 clicks in the upper left compared to 314 in bottom left; Graphical: 333 clicks in the upper-left and 300 in bottom middle). Despite this, page location is not included in subsequent source prediction models, as all sources and news stories were randomized within all page regions. For reference, the observed frequency counts for each grid location are presented below in Table 3.3.
Table 3.3. N Clicks to each of the six page locations 1 2 3 4 5 6 Totals Text 352 258 308 314 298 299 1829 Graphical 333 283 319 271 300 276 1782 2 χ (25)= 25.22, p = 0.450 N = 3,611
79 3.2 CLUSTERING IDEOLOGICALLY SIMILAR SOURCES In order to assess polarization – whether participants self-select sources that match their existing political ideology or affiliation –the six sources used in this experiment should be clustered accordingly to partisan and ideological lines. To assess this, questions from the pre-test survey were used: a set of survey items asked participants to evaluate how fair they perceive the reporting of several news organizations. Ratings of perceived fairness were compared along lines of both party affiliation and ideology. Recall that the Ideology-Party mappings are not always consistent (see data in Table 3.4 for party and ideological affiliation, and reference Ellis & Stimson, 2007 for a review); as such, both ideology and party id were used as predictor variables of perceived media fairness.
Figure 3.1 Mean perceived fairness of news organizations, grouped by political ideology
80
Figure 3.2 Mean perceived fairness of news organizations, grouped by political party identification; error bars reflect standard deviations within each group.
Clearly, results in Figures 3.1 and 3.2 show that certain sources do indeed skew towards a particular political ideology or party affiliation. Specifically, The Huffington Post, New York Times, and CNN skew towards Democratic and liberal affiliations, whereas Fox News and The Drudge Report skew towards Republicans and conservatives. Unfortunately, only Fox News was included as a source in this given experiment, and as such, Fox News stands alone as single right-leaning source. For the purposes of the experiment, CNN, The New York Times, and The Huffington Post were grouped as left-leaning sources. The Wall Street Journal, which was expected to cater towards conservatives or Republicans, stands out as a fairly politically neutral news site, also consistent from recent Pew survey data (see Kohut, 2010). The survey did not tap perceptions of USA Today, so this source was classified as neutral, along with The WSJ.
81 Table 3.4: Party and Ideological Affiliation: Self-reported identification Party/ Ideology Democrat Independent Republican
Liberal 232 15 8 Moderate 190 93 78 Conservative 22 39 323
Suitability of source clustering. As another assessment of the described source clustering, linear regression models were constructed, with “perceived fairness” (as measured by a 1-5 response scale) of the left-leaning or right-leaning news organization as dependent variables. Two perceived-fairness models were constructed, one for the right-leaning source (Fox News), and the second for the left-leaning sources (The New York Times, CNN, and The Huffington Post). The perceived fairness of the three left- leaning sources was averaged to produce the left-leaning construct1. Regression results are reported in Table 3.5, and confirm the hypothesized clustering of sources along ideological and political party lines. Model 1 assesses the factors affecting perceived fairness of right-leaning news sources (as defined solely by Fox News). Perceptions of fairness are increased by being Republican (β= 0.59, Z = 2.67, p < 0.01), conservative (β= 0.74, Z = 3.69, p <0.001), and high levels of self- reported news interest and exposure (β = 0.26, Z =3.57, p < 0.001). The perception of Fox as a fair news organization decreases for polarized liberals (β = -0.88, Z =-2.72, p < 0.01), polarized Democrats (β = -0.80, Z =-2.23, p < 0.05), and also for those with higher education levels (β = -0.29, Z = -3.95, p < 0.01). This model accounts for nearly 60% of the variance in perceiving Fox News as a fair news organization. Similarly Model 2 predicted the perceived fairness of left-leaning news organization. Results show a significant positive relationship between perceived fairness and being Democrat (β = 0.47, Z = 3.34, p < 0.01), college educated (β = 0.14, Z = 2.74, p < 0.01), and higher self-reported news interest and exposure (β = 0.36, Z
1 Nearly half (N=402) of the participants in the sample were unfamiliar with The Huffington Post and therefore did not rate it. To avoid a significantly smaller N for the left-leaning source fairness regression, all missing values were subbed with the median value for the left-leaning source fairness.
82 =7.61, p < 0.01). There are significant negative effects of being conservative (β = - 0.24, Z -1.79, p = 0.05), and a polarized conservative (β = -0.40, Z -1.95, p = 0.05). As this grouping of left- and right- leaning sources falls neatly between party lines, these clusters are used in later ordinal logistic regressions predicting source selection.
Table 3.5 Linear Regression Model Results Perceived fairness of right-leaning and left-leaning news organizations Model 1: Right- Model 2: Left- Leaning Source leaning Source (Intercept) 2.921 * 1.180 * (0.283) (0.155) Republican 0.593 * 0.066 (0.222) (0.146) Democrat 0.107 0.471 ** (0.215) (0.141) Polarization 0.146 + 0.272 (0.307) (0.202) Conservative 0.743 ** -0.238 + (0.201) (0.133) Liberal 0.173 0.231 (0.226) (0.151) Self-reported news interest 0.258 ** 0.360 ** (0.072) (0.047) Males -0.084 0.070 (0.072) (0.049) Education (college grad +) -0.293 ** 0.137 ** (0.148) (0.050) Republican * Polarization 0.298 -0.391 (0.371) (0.245) Democrat * Polarization -0.802 * -0.255 (0.359) (0.237) Conservative * Polarization 0.509 -0.398 * (0.310) (0.205) Liberal * Polarization -0.879 * 0.207 (0.323) (0.216) N 906 952 R2 0.578 0.404 Adjusted R2 0.573 0.396 Residual Standard deviation 1.058 0.729 Denotes p < .05, + p < 0.10; Right source: Fox News; Left sources: NY Times, Huffington Post, CNN
83 3.3 ANALYSIS OF SOURCE SELECTIONS
Source Loyalty: Stability of source preferences
It is hypothesized that in the graphical condition, where source is prominently displayed, participants will be more likely to repeatedly select their preferred source throughout multiple trials of the experiment. Put simply, the overt visual depiction of source will foster brand recognition, enabling individuals to repeatedly select it. The effect is hypothesized to be stronger than in the text condition, and possibly also for polarized individuals. Analyses of source stability were assessed with both the raw (six individual sources) and clustered (left-leaning, right-leaning, and neutral) source data. Repeated selections to individual sources. The data for each of the six news sources shows that over one-third (N=666) of participants selected the same source in at least two trials, and over half (N=553) selected the same source in two adjacent trials. In order to infer the effects of trial on source selections, a 6x6 table was constructed, where rows represent the source selected in the first choice (Choice 1) and columns represent the selections made in the following choice (Choice 2). Data for all adjacent trials are presented in Tables 3.6a, 3.6b, and 3.6c. The behavior of every participant is represented by a single count in each cell. For example, 76 participants selected Fox News in both Trial 1 and Trial 2. Based on a Chi-Square test of independence, the null hypothesis that source selections are independent across subsequent trials is rejected for all experiment trials, though diminishes somewhat as the experiment progresses (χ2 (25)= 308.91, p < 0.001). Bolded values represent instances when the observed counts are much greater than the Chi-square expected values, as based upon a visual inspection. The important feature of these tables is the diagonal—which denotes cases where the same source was selected in adjacent trials. In all adjacent trials, repeat selections to the same source exceed the expected values. Yet, the picture is complicated when disaggregating the text and graphical treatments. As suggested by the Chi-square values in Table 3.7, the lack of independence is limited only to the graphical news display.
84 Table 3.6a: Source selections in trials 1 and 2, both text and graphical conditions
Choice 2 CNN FOX Huff NY USA WSJ Post Times Today CNN 49 13 19 14 26 24
(21) (28) (20) (23) (25) (26) 1 FOX 12 (23) 76 (31) 17 (22) 13 (25) 13 (27) 27 (29)
Huffington 18 (19) 12 (24) 39 (17) 17 (20) 24 (22) 17 (23)
Choice Choice NY Times 16 (23) 20 (30) 15 (21) 59 (24) 19 (26) 23 (28)
USA Today 24 (23) 19 (30) 22 (21) 18 (25) 56 (27) 16 (28)
WSJ 16 (25) 38 (33) 13 (23) 22 (26) 20 (29) 59 (30) χ2 (25)= 308.913, p < 0.001. Expected values are denoted in parentheses. Note the highly stable adjacent source preferences across the diagonal.
Table 3.6b: Source selections in trials 2 and 3, both text and graphical conditions
Choice 3 CNN FOX Huff NY USA WSJ Post Times Today CNN 32 26 12 22 26 16
(21) (24) (15) (23) (21) (24) 2 FOX 13 (28) 63 (32) 16 (20) 24 (30) 13 (27) 32 (32)
Huffington 19 (20) 12 (23) 27 (14) 24 (21) 24 (19) 21 (23)
Choice Choice NY Times 33 (22) 16 (26) 14 (16) 38 (24) 19 (22) 23 (26)
USA Today 27 (25) 22 (29) 17 (18) 23 (27) 56 (24) 24 (29)
WSJ 19 (26) 29 (30) 18 (19) 23 (28) 20 (26) 51 (31) χ2 (25)= 118.14, p < 0.001. Expected values are denoted in parentheses. Note the highly stable adjacent source preferences across the diagonal.
Table 3.6c: Source selections in trials 3 and 4, both text and graphical conditions
Choice 4 CNN FOX Huff NY USA WSJ Post Times Today CNN 33 23 16 21 21 22
(25) (29) (21) (18) (20) (22) 3 FOX 24 (28) 56 (32) 14 (23) 18 (20) 9 (21) 29 (25)
Huffington 12 (17) 13 (19) 31 (14) 11 (13) 15 (13) 11 (15)
Choice Choice NY Times 26 (27) 21 (31) 31 (22) 25 (20) 24 (21) 19 (24)
USA Today 21 (22) 25 (25) 18 (18) 19 (16) 23 (17) 13 (19)
WSJ 33 (28) 31 (32) 13 (23) 15 (21) 22 (22) 36 (25) χ2 (25)= 86.75, p < 0.001. Expected values are denoted in parentheses. Note the highly stable adjacent source preferences across the diagonal.
85 Trial and visual treatment. When separating the data into the two treatment conditions, as hypothesized, the lack of independence is limited to the graphical condition. Individuals in the text condition were no more likely to click on their previously selected source as the experiment progressed (e.g. Trial 2 and 3: χ2 = 36.33, p =0.40). Table 3.7 presents the Chi-square of adjacent sources selections; results show that repeat source selections are indeed more frequent in the graphical condition, when all sources are aggregated.
Table 3.7: Chi-square test of independence of news source, by adjacent trials Trial Number Text Condition Graphical Condition χ2 χ2 1 and 2 146.88** 188.44** 2 and 3 36.33 117.78** 3 and 4 38.18 83.04** Chi-square values on 25 degrees of freedom * < 0.05, ** < 0.001, Bonferroni-adjusted p-values are used to account for multiple tests.
To quantify the effect of repeated source selections, multinomial regression models were computed, regressing each source selection on the previous trial (i.e., the source selection in Trial 2 on Trial 1, the source selection in Trial 3 on 2, and selection 4 on 3). Multinomial models were produced for each of the two visual conditions (using R package nnet, Venables & Ripley (2002)), and their predictive utility of was assessed with R package pscl (Jackman, 2010). Results again show that prior selections influence future choices. Also of note is that the predictive utility of prior selections weakens throughout the four trials in the text display (by 14 percentage points) yet remains fairly stable in the graphical condition (decline of 6 percentage points). These results suggest that graphical and aesthetically-appealing news formats may propagate media silos more rapidly than simple text displays. The graphical condition clearly provides the relevant cues that enable individuals to repeatedly self- select content.
86 Table 3.8: Accuracy of source as predictor of a subsequent source selection Trial Number Aggregate Text Condition Graphical Condition 1 and 2 37.4% 36.7% 38.0% 2 and 3 28.8% 26.1% 32.9% 3 and 4 26.5% 23.0% 32.2%
Repeated selections to left, right, and neutral sources. As prior source choice influences the future selection of news sources, it should follow that comparable results will also hold, and ideally improve, when clustering news sources into right, left, and neutral source groups. Indeed, when sources are clustered, the predictive accuracy between adjacent trials selections is improved. Compare the accuracy of multinomial models in Table 3.9 (which represents clustered sources), with Table 3.8 (individual sources). Clustering sources causes only a 4% decrease in accuracy from the first and last prediction; conversely there is an 11% accuracy decrease for individual sources: nearly a 7% improvement with the clustered data.
Table 3.9: Accuracy of clustered source as predictor of next source selections Trial Number Aggregate Text Condition Graphical Condition 1 and 2 52.27% 52.39% 52.13% 2 and 3 48.06% 46.49% 50.57% 3 and 4 48.61% 50.25% 47.46%
Chi-square tests of independence reveal that in the third and fourth choices in the text condition, we cannot reject the null hypothesis that these selections are independent (χ2 (1) = 0.50), whereas the same is not true for the graphical condition (χ2 (1) = 9.25). Table 3.10 presents the Chi-square values for tests of independence between adjacent trials for the clustered (left, neutral, and right) source data, and Table 3.11 presents the per-participant click data associated with repeatedly selecting a left, neutral, or right leaning source. The bolded values indicate instances when the observed values greatly exceed those predicted by the expected values table, indicating again that repeat selections are highly stable across adjacent trials for all sources.
87
Table 3.10: Repeat selections of clustered news sources Trial Aggregate Graphical Text Treatment Treatment Choice 1 and 2 χ2 = 135.82 ** χ2 = 74.00 ** χ2 = 68.02 ** Choice 2 and 3 χ2 = 65.67 ** χ2 = 49.51 ** χ2 = 20.36 ** Choice 3 and 4 χ2 = 38.88 ** χ2 = 28.92 ** χ2 = 12.99 + χ2 values on 1 degree of freedom. Sources clustered according to left, neutral, and right
Table 3.11: N participants selecting ideologically similar sources over repeat trials Aggregate Source Selections Choice 2 Left Neutral Right
Choice 1 Choice Left 151 (115) 115 (143) 47 (63) Neutral 133 (151) 246 (188) 45 (83) Right 40 (56) 42 (70) 76 (31)
Choice 3
Left 137 (110) 127 (144) 51 (60)
Choice 2 2 Choice Neutral 116 (137) 221 (178) 54 (75)
Right 54 (59) 53 (77) 63 (32)
Choice 4
3 Left 94 (82) 119 (129) 56 (57) Choice Choice Neutral 112 (115) 206 (170) 57 (79) Right 38 (46) 56 (71) 56 (31) Bolded cells denote when the expected values exceed the Chi-square expected values. Expected values are denoted in subscripts in parentheses
WHO REPEATEDLY SELECTS NEWS SOURCES?
It is fairly evident that source loyalty does exist for news consumers, whether for one specific source, or for news sources within an ideologically-similar cluster. Two logistic regressions were conducted to better understand the characteristics influencing repeated source selections. The dependent outcome measure was a newly created binary variable: repeat source, indicating whether an individual repeatedly selected a source three or four times (i.e., 1 represents individuals who selected the same source at least
88 three times, and 0 represents the absence of this behavior). Be clear that this variable accounts for repeat selections to individual—unclustered—sources, such that each of the six sources starts off with equal probability of being selected again. 248 participants selected the same source 3 or 4 times, which is fairly substantial, particularly as only 841 participants completed all four trials of story-selection. Two models were created: one using Party ID as a factor, and the other using ideology, and the remaining predictor variables included gender, polarization, education, graphical treatment, and whether or not a participant selected a soft news story at least once throughout the experiment. Self-reported news interest and age was insignificant and not included in the final model. Results are reported in Table 3.12 (and are in reference to the baseline category of a moderate/ Independent female, with no polarization, less than a college education, and selecting only hard news in the text condition). Both models show that repeatedly selecting a source is significantly and negatively associated with selecting soft news content (Model 1: β = -0.76, Z = -4.69, p <0.001; Model 2: β = -0.78, Z = -4.66, p <0.001), meaning that selecting only one soft news category decreases an individuals’ odds of repeat source selection by 53% from the baseline (participants who selected only hard news in Model 1). In other words, repeat source selections were more common for those who only selected hard news. This offers some indirect evidence that repeat source selections are not completely innocuous—repeat selections are made by individuals most interested in the political process and most attentive to current affairs. Being male was also significantly and positively associated with repeat selections (Model 1: β = 0.91, Z = 2.76, p <0.05; Model 2: β = 0.83, Z = 2.30, p <0.05): a 148% increase in odds over the female baseline, again using Model 1. Males have overall higher levels of polarization, and by extension, possibly stronger feelings about politics. These strong beliefs may manifest into consistent source preferences, as evidenced in the model interactions. There were no effects of ideology on repeat source selection, though being Democrat was positively associated with repeat source selections (Model 2: β = 0.43, Z = 2.15, p <0.05).
89 Table 3.12: Characteristics of those selecting same source 3-4 times Logistic Regression Results
Model 1: Model 2: Ideology Party ID (Intercept) -0.87 * -0.30 (0.29) (0.83) Conservative -0.20 — (0.19) — Liberal 0.24 — (0.20) — Republican — 0.27 — (0.21) Democrat — 0.43 * — (0.20) Polarization 0.37 0.46 (0.33) (0.36) Gender (Male) 0.91 * 0.83 * (0.33) (0.36) Graphical Treatment -0.11 -0.80 (0.20) (0.48) Education (College grad +) -0.26 -0.84 (0.24) (0.53) At least 1 soft news choice -0.76 ** -0.78 ** (0.16) (0.17) Polarization * Male -1.16 * -1.04 * (0.48) (0.51) Graphical treatment * Education 0.77 * 0.69 * (high) (0.32) (0.33)
N 943 902 AIC 1041.20 985.87 BIC 1235.17 1178.06 log L -480.60 -452.94
Two significant interactions also emerged – between polarization and gender (Model 1: β = -1.16, Z = -2.45, p <0.05; Model 2: β = -1.04, Z = -2.05, p <0.05) and between education and graphical treatment (Model 1: β = 0.77, Z = 2.40, p <0.05; Model 2: β = 0.69, Z = 2.13, p <0.05). As discussed, women are on average less polarized than males, and on average are less likely to repeatedly select a source. Yet, the highly polarized females in this sample did in fact exercise strong repeated source
90 preferences, more so than the most polarized males. While fewer females are polarized, it appears that when they are, they may be very particular about the news information they are consuming. The interaction between education and visual treatment indicates that the most educated individuals were most likely to repeatedly select a source in the graphical condition. This adds some additional evidence to the findings discussed in Chapter 2, regarding the relationship between education, graphical display, and hard news selection. The college-educated strata were more likely to select hard news when reading the graphical layout, and it also appears that the college-educated strata are more likely to repeatedly select sources in the graphical condition. Educated individuals may be more motivated by news source, as they may be more critical of the information they receive. The graphical layout offers an obvious visual brand cue, through which educated individuals can use to determine the potential accuracy or desirability of news content.
Interaction between Education and Visual Treatment on Repeat Source Selection
Text Graphic Low High 0.4
0.35
0.3
0.25
0.2 Probability of Repeat Source Selection Source of Repeat Probability
0.15
Text Graphic Visual Treatment: Text or Graphical Figure 3.3. Interaction between education and visual display. Y-axis represents probability of repeated source selection and the x-axis represents the different display layouts: text or graphic. College-educated individuals are more likely to repeatedly select a source in the graphical condition. 95% confidence intervals are shown.
91 3.4 ANALYSIS: SOURCE PREFERENCES An ordered logistic regression was created (using the polr function in the MASS R package) to predict selection preferences for left, neutral, or right leaning sources. Recall that “left” sources included The New York Times, Huffington Post, and CNN, neutral sources included The Wall Street Journal and USA Today, and the right source included Fox News only. The ordered logistic regression enables one to determine the probability of moving from one source threshold to another, i.e., from a left source (the baseline) to a neutral source, and from a neutral to a right. The model computes log- odds values for the model coefficients, which provide context for interpreting the class boundary intercepts (the thresholds associated with the three source types). First, tables are produced, assessing the number and percentage of liberals, moderates, and conservatives selecting a left, neutral, or right news source. Table 3.13 presents aggregate results from all trials; Table 3.14 depicts the first source alone. The consistent diagonals in Table 3.13 make it clear that ideological preferences do 2 correspond to the source that is ultimately selected (χ (4) = 10.15, p = 0.04), yet in looking only at trial 1, it is clear that ideology alone is not a strong predictor of the first source selected. An ordered logistic regression with additional variables is then used.
Table 3.13: Ideology and Source Selection – All Trials
Source Liberal Moderate Conservative (% of Group) (% of Group) (% of Group) Left (NY Times, Huff Po, CNN) 439 (46.8) 602 (46.9) 627 (45.1) Neutral (WSJ, USA Today) 323 (34.4) 462 (36.0) 460 (33.1) Right (Fox) 176 (18.8) 219 (17.1) 303 (21.8) 2 Parentheses denote the percentage of the ideology χ (4) = 10.15, p = 0.04
Table 3.14: Ideology and Source Selection – Trial 1 Only
Source Liberal Moderate Conservative (% of Group) (% of Group) (% of Group) Left (NY Times, Huff Po, CNN) 112 (47.3) 166 (49.6) 161 (44.4) Neutral (WSJ, USA Today) 88 (37.1) 116 (34.6) 129 (35.5) Right (Fox) 37 (15.6) 53 (15.8) 73 (20.1) 2 Bolded values indicate when observed > expected χ (4) = 3.70, p = 0.45
92 3.5 RESULTS As described, ideology alone does not adequately predict which news source an individual is likely to select first (note the p values of 0.45 in Table 3.14). As such, an ordered logistic regression with additional demographic variables is used to improve the predictive power of left-, neutral- and right-leaning source preferences1.
Predicting Source in Trial 1: Ordinal Logistic Regressions. In addition to ideology, other hypothesized effects on source selection include: (a) News type (hard or soft). Individuals may be more selective of their news sources when reading hard news, and source preferences may be stronger for hard news categories. Further, left and rights source may be preferred above neutral sources for hard news content, as individuals may seek political coverage from sources that agree with their dispositions. (b) Visual treatment. The news source logo is naturally more prominent in the graphical condition, potentially making certain sources more appealing to select, and increasing source-recognition. As such, partisan preferences may be stronger in the graphical condition, along with a greater influence of previous choice. (c) Education (low < college education, high >= college education). Prior analyses show that education mediates the effects of graphical news display, and higher education levels are associated with repeat source selections. Education may also effects source selection. (d) Political Polarization. Polarized attitudes may interact with ideology or gender, encouraging individuals to select sources in ideological alignment. (e) Gender. Descriptive data shows that females are more likely to express certain political ideologies (e.g., Moderate and Democratic) than males, and this may also manifest in source preferences.
1 While clustering sources along ideological lines is appropriate for the purposes of this analysis, two logistic regression results are reported for reference in Appendix E, which use binary source-selection variables: Model 1: right-leaning source=1, 0=else; Model 2: left-leaning source selection=1, 0=else.
93 (f) Total News Interest. Individuals who report higher levels of news exposure may be more familiar with the news sources used in this experiment, and may thus be more particular about selecting a specific content provider. (g) Marital status. The communication literature documents that individuals in politics take cues from opinion leaders (e.g., Katz, 1957; Lazarsfeld, Berelson, & Gaudet, 1948). The level of potential political discussion between married males and females may encourage the adoption of spousal preferences (e.g., married males may prefer left-leaning sources, or married females may prefer right-leaning sources). (h) Previous choice: As discussed, an individual’s prior news selection has a significant impact on their subsequent source selections. To assess this, a lagged choice variable was computed, accounting for the prior source choice associated with trials 2-4. (i) Trial: As previously described, the influence of previous source selection may diminish across trials in the experiment. As the experiment continues, individuals may be more likely to deviate from initial predispositions. To assess the effects of source preferences, an ordered logistic regression was computed for the first trial only, predicting the selection of a left, neutral, or right source (using the “baseline” of a moderate, unmarried male with no polarization, low education (less than college), and who previously read soft news in the text condition). The right-leaning source was coded as a “2”, neutral as “1”, and left as “0”. By comparing the log odds coefficients and threshold values for each source cluster, one can better understand how a given person will behave when selecting news sources. Results indicate that being conservative (β = 0.28, T= 1.81) has a marginally significant positive effect on source selection, increasing the odds of selecting a neutral or right source by 32%, relative to the baseline (a moderate). Another way to interpret the log- odds conservative coefficient is it moves an individual rightward by 0.28, keeping in mind the liberal-neutral boundary of 0.24, and the neutral-right boundary of 1.97. Hard news (β = 0.32, T= 2.19) was also significantly and positively related to source selection, increasing the probability by 38% relative to the baseline. The log-
94 odds of selecting hard news (β = 0.32) could alone move the baseline individual 0.32 points rightward, above the left source threshold. Source selections in the first trial offer a baseline from which we can interpret source preferences in the remaining three trials. Results show that neutral and right leaning sources (WSJ, USA Today, Fox News) are more likely to be selected by conservatives and for hard news topics. However, the effect of hard news may be an artifact of source selection, particularly given Fox News’ political posturing and its recognition for political coverage. Fox News may have established a reputation for itself as a political hard news provider, and people rely on this impression when selecting the source. Being conservative is associated with non-left sources, and confirms initial hypotheses about ideologically-aligned source preferences.
Table 3.15: Source selections in Trial 1 Ordered Logistic Regression Results1
Value Std. Error T Value Gender (Female) -0.151 0.195 -0.77 Married -0.261 0.192 -1.36 Liberal 0.096 0.169 0.57 Conservative 0.283 0.157 1.81 + Polarization 0.087 0.200 0.43 Education (College or post-grad) 0.034 0.134 0.25 Graphical Treatment 0.043 0.127 0.34 Hard News 0.315 0.144 2.19 Female * Married 0.353 0.256 1.36 Using 3 Source Levels (CNN as Left)
Source Threshold Estimate St. Error T value Values Left | Neutral 0.240 0.286 0.837 Neutral | Right 1.976 0.295 6.704 Residual Deviance: 1807.986 AIC: 1829.986 N =829
1 Note the unique table formatting and lack of p-values. The unique t-distribution for this class of glm makes it non-trivial to produce accurate p-values; instead, variables close to a 1.96 T-value are bolded.
95 SOURCE SELECTION IN TRIALS 2-4 Hypotheses. Based on this initial analysis of source selection in the first trial, the next step is to assess how these preferences persist or change throughout the remaining three trials. Another ordered logistic regression was computed using the same variables as the previous model, yet this time accounting for interactions between trial and previous choice (the influence of past behavior may wear off as the experiment progresses), ideology and previous choice (the influence of past behavior may increase or decrease given an individual’s ideology), graphical treatment and previous choice (earlier data analysis suggests that the influence of previous choice may be stronger in the graphical layout), and education and graphical treatment (specifically as we have seen that education moderates the influnce of a graphical display). Recall again that the “baseline” point of comparison is a moderate, unmarried male with no polarization, low education (less than college), and who previously read soft news in the text condition.
Results. Full model results are presented in Table 3.16. Coefficients can be interpreted relative to the log-odds intercept thresholds: moving from a left to neutral source is 0.18, and between a neutral and right source is 1.84. Ideology. Having a liberal ideology was significantly associated with selecting a left-leaning source, decreasing the log odds from the moderate baseline by 30% (β= - 0.36). While being liberal is strongly associated with selecting a left-leaning source, there was no significant difference between conservatives and moderates. While this data may seem to suggest that liberals have the most polarized media preferences of all, it should be noted that there were three sources in the left category, compared to two in the neutral category, and one in the right category. This naturally increases the probability of selecting a left-leaning source, and provides more opportunity for liberals to obtain source diversity, while still selecting ideologically aligned sources.
96 Table 3.16: Factors Affecting Selection of a Clustered Source in Trials 2-4 Ordered Logistic Regression Model
Value Std. Error T Value Gender (Female) -0.265 0.120 -2.211 Married -0.263 0.117 -2.237 Liberal -0.358 0.151 -2.367 Conservative -0.058 0.138 -0.423 Prior Choice: Neutral 0.269 0.199 1.351 Prior Choice: Right 1.173 0.270 4.342 Trial 3 0.090 0.141 0.636 Trial 4 0.204 0.141 1.440 Polarization 0.168 0.121 1.382 Education (College or post-grad) 0.112 0.113 0.983 Graphical Treatment 0.197 0.250 0.7881 Hard News 0.123 0.088 1.449 Female * Married 0.364 0.158 2.305 Liberal * Prior Choice Neutral 0.664 0.217 3.058 Conservative * Prior Choice Neutral 0.268 0.199 1.344 Liberal * Prior Choice Right 0.789 0.292 2.699 Conservative * Prior Choice Right 0.245 0.255 0.956 Trial 3 * Prior Choice: Neutral -0.230 0.203 -1.132 Trial 3 * Prior Choice: Right -0.600 0.262 -2.287 Trial 4 * Prior Choice: Neutral -0.291 0.210 -1.382 Trial 4 * Prior Choice: Right -0.870 0.273 -3.188 Education (College grad) * Graphical -0.293 0.162 -1.811 Graphical* Prior choice: Neutral 0.379 0.171 2.222 Graphical* Prior choice: Right 0.517 0.218 2.368 N=2456 Residual Deviance: 4936.266 AIC: 4988.266
Intercept Thresholds Value St Error T value Left | Neutral 0.18 0.18 0.99 Neutral | Right 1.84 0.18 9.79
97 Previous Source Selection. The most significant variable affecting source selection was the previous selection of right-leaning source. If an individual previously selected a right leaning source, the expected log-odds of doing so again increases by 1.17; a 223% increase in relative odds when compared to having previously selected a left-leaning (baseline) source. This remarkably high probability provides evidence in support of media silos and echo chambers—that individuals are unlikely to deviate from their existing patterns of behavior, and will repeatedly seek out the same familiar, or preferred, sources. Interaction: Graphical display and previous choice. There was also a significant interaction between previous choice and graphical treatment—specifically, a previously selected right-leaning source in the graphical condition increases the log odds by 0.517 for moving from a leftward source (translating to a relative increase in odds of 68%)— amplifying the already present main effects of a right-leaning previous choice (β =1.173) and graphical treatment (β =0.197). While the effects of graphical display and previous right-leaning source selection are strong on their own, when simultaneously present, the odds of moving from a left source increase by an aggregate 560% from the model baseline of a previously selected left source in text display. Clearly this is a substantial increase, and supports initial hypotheses that the graphical treatment may have differential effects depending on the source in question. Interaction: Ideology and Previous Choice. There were also significant interactions between ideology and previous choice (see Figure 3.4). Specifically, both conservatives and liberals were more likely than moderates to be influenced by their prior choice, as evidenced by the positive coefficient values. As expected, prior selections to neutral or right leaning sources increase the log odds of again selecting these categories, as compared with the baseline of a moderate selecting a left-leaning source. This supports hypotheses that individuals with stronger ideologies are more attentive to their selected news sources, and more likely to repeat their prior behavior. Also interesting is that liberals may exhibit behavior that is least typical of their ideology. For instance, being a liberal who previously selected a right-leaning source increases the log odds by 0.789 for moving from a leftward source—amplifying the already present main effect of a right-leaning previous choice (β=1.173) and counter-
98 acting that of being liberal (β= -0.358). Together, this increases the odds of moving from the baseline (a moderate selecting a left source) by 397%. It appears that source loyalty may sometimes be more influential than self-classified ideological labels, as liberals who choose to read a right-leaning source are highly likely to select it again. These results point to strong source loyalty. While ideologically-motivated source selections were hypothesized, the data shows strong evidence that individuals express an allegiance to a specific source, and repeatedly selecting it, even when the source is ideologically dissimilar. Specifically, when liberals select a right or neutral source, they are just as likely, or more likely, to repeatedly select the same source as conservatives, and certainly more so than moderates. In fact, the evidence for source loyalty appears stronger than clean-cut main effects of ideology. Interaction: Trial and Previous Choice. The influence of prior selections to a right source was most likely to diminish across the 3rd and 4th trial. This is to be somewhat expected, as there was only one single right-leaning source, such that if any participant wanted the slightest bit of source diversity in their news diet, it would naturally mean moving towards a neutral or left category. With that caveat, it is still fairly remarkable that results reveal such strong patterns of behavior. Figure 3.5 presents the marginal effects and associated probabilities of the interaction between the influence of previous choice and trial. The influence of the previously selected source is fairly stable across trials, with the exception of right sources, which naturally have a diminished influence over time. Furthermore, the results in Figure 3.5 need to be interpreted in light of the overall random probabilities of selecting a given source: there exists 16% chance of selecting a right source (1/6), 33% chance of selecting a neutral source, (2/6) and a 50% chance of selecting a left source (3/6). We can contrast these expected probabilities with the results in Figure 3.5, which presents the marginal effects and probabilities of nine potential outcomes, based on trial number and previous choice.
99 Interaction between Prior Choice and Ideology on Source Selection
Moderate Liberal Conservative
Choice: Right Choice: Right Choice: Right Prior Left Prior Neutral Prior Right
0.6 0.5 0.4 0.3 0.2 0.1 Choice: Neutral Choice: Neutral Choice: Neutral Prior Left Prior Neutral Prior Right
0.6 0.5 0.4 0.3 0.2 0.1 Choice: Left Choice: Left Choice: Left Prior Left Prior Neutral Prior Right
0.6 Probability of Selecting a Given Source a Given of Selecting Probability 0.5 0.4 0.3 0.2 0.1 Moderate Liberal Conservative Moderate Liberal Conservative Ideology: Moderate, Liberal, Conservative
Figure 3.4: Effects of ideology and prior choice on source selection. The 3-3 grid plot above represents the interaction between ideology and previous choice on news source selection. The y-axis represents the probability of source selection, and each grid region represents a specific choice outcome. For example: the lower left grid represents instances when an individual selected a left-leaning source after having previously also selected a left-leaning source. The probability of this is only slightly higher for liberals than moderates and conservatives. Yet, compare this lower-left to the lower-right grid location—when an individual selected a left-leaning source after a right-leaning source. The influence of previous source choice shows a 20% difference in probability of selecting a left-leaning source for conservatives and moderates, and 40% difference for liberals.
100 Interaction between Prior Choice and Trial on Source Selection
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Figure 3.5 Effects of trial and prior choice on source selection. The 3-3 grid plot above represents the interaction between trial and previous choice on news source selection. The y-axis represents the probability of source selection, and each grid region represents a specific choice outcome. Recall also the baseline probabilities of selecting each source: 16% for right source, 33% for a neutral source, and 50% for a left source. To interpret these effects, as an example: the top right grid represents instances when an individual selected a right-leaning source after having previously also selected a right-leaning source, across trials 2-4. While this probability decreases after the second trial, the probability of selecting a right source in the fourth trial is still higher than chance (16%) for these individuals, and no lower than the probability of selecting a left source.
101 Demographics: Gender, Marital Status, and Education. Being female is associated with lowering the log-odds of selecting a rightward source by -0.27. However, the picture is reversed for married females—when accounting for the interaction term (β=0.36), and the baseline effects of female (β= -0.27) and marriage (β- 0.26), marriage increases the rightward selection odds for females by 11%. Conversely, marriage decreases the rightward source selection odds for males by 23%. While these are not very large effects, it is important to note the inverse effect of marriage on gender. These results indicate that the ideological influence of one’s spouse can affect their own news media preferences. Finally, in Chapter 2 we saw that more educated individuals were likely to select hard news in the graphical condition. The results of this source preference model provide additional evidence that the graphical treatment has a unique effect for college- educated individuals. While the main effects of education (β= 0.112) and graphical treatment (β= 0.197) are positive, when interacted (β= -0.293), the overall log odds of selecting a rightward source are lowered, meaning there is only a 1.6% increase in odds of selecting a neutral or right source (from the left-leaning source as a baseline). When educated people view news in a graphical presentation, they appear more likely to select a left-leaning source.
3.6 DISCUSSION
POLARIZATION & SOURCE SELECTION
Ideology. As expected, ideological identification did affect source selection. While this effect appears strongest for liberals, results should be interpreted with the caveat that there were three opportunities for liberals to select an ideologically-aligned source; conversely, conservatives only had one preferred source. Polarization. Interestingly, polarization had no effect. It was included as an interaction term with ideology, but due to insignificance, was removed from the final model. It appears that stronger partisan attitudes do not directly translate onto media preferences beyond what ones ideology would suggest.
102 Graphical treatment. The right-leaning news source was most frequently selected in the graphical condition. As the only right-leaning news source included in this experiment was Fox News, which is in fact one of the most popular news outlets, it is quite possible that the visual recognition and sheer popularity of this source encouraged selection. Further, the visual treatment did significantly interact with the previous choice, making individuals in the graphical condition more likely to repeatedly select a left-leaning source.
Previous choice. The influence of prior selection was extremely influential for the selection of all sources, though there were some diminishing returns. The influence of previous selection to a right-leaning source was weakest, but this is to be expected, as there was only one single right-leaning source in the experiment. Overall, individuals were most likely to repeat their news source preferences early in the experiment, but after selecting two or three stories, individuals were slightly more willing and interested in exposing themselves to additional sources in the latter two trials. There is currently much discussion about ideological diversity in news selection – whether in fact people are diverse in their attention to online news content, or whether individuals choose to silo themselves. Research has attempted to resolve this question from divergent research paradigms, presenting seemingly conflicting evidence. Namely, research that analyzes the demographics of news website visitors (e.g., Shapiro & Gentzwick) finds that news online consumers visit a diverse repertoire of news websites. Experimental research (e.g., Iyegnar & Hahn) suggests otherwise – that partisan selectivity is more rampant, particularly amongst Republicans. The findings presented here certainly support the latter claim, and offer some explanation for the first: individuals are likely to see out news sites that agree with their dispositions, and even more likely to be influenced by these source preferences throughout all news selections. However, while individuals are certainly selective about their news sources, the picture becomes more complicated based on the total time allotted to reading news articles. As individuals spend more time online, they may more willing, or simply more likely, to consume content from a different news content providers. This might suggest that in real-world contexts, the more time an individual
103 spends engaging with news, the more opportunity and willingness to branch out into other diverse sources.
3.7 FUTURE WORK
Source selections. It is duly noted that a future study assessing political polarization in news source selection should do a more intensive pre-trial of source political leanings. For instance, The Drudge Report would have been an ideal right- leaning source to include in this study based on the survey pre-test, potentially balancing the left, neutral, and right source clusters. One clear caveat of this experiment is that only one source – Fox News – was clearly an ideologically right-leaning news source. A clear finding from this research is that the ideological basis of source selection does not align as closely as one might expect with the perceived fairness of news organizations. It appears that while news organizations have established reputations for themselves as appealing toward a particular partisan or ideological base, there is in fact some spread in the ideological demographic of their audience. This suggests that more work needs to be done to better understand the characteristics of news audiences. While nearly all survey data points to clear ideological divisions (e.g., Coe et al, 2008; Kohut, 2010) behavioral data presents a slightly more nuanced picture. There are clearly partisans who prefer to see out news sources that are inconsistent with their own beliefs. The larger question here is why—perhaps these individuals simply do not recognize the perceived reputation of the source, or perhaps they choose to add diverse opinions to their news diet.
Diversity in content selection. While the results of these models reveal complex and nuanced interactions between partisanship, prior selection, and polarization, there are some key constants and that can be learned. Namely, the results presented here, as well as in Chapter 4, demonstrate that source and news preferences are generally stable, but susceptible to slight change as time goes on. Individuals most interested in hard news, or affiliated with a particular political group are initially likely to select content and sources that agree with their existing behavior and/ or political affiliations, yet as
104 they read more news content, they eventually become less motivated by their initial ideologies or preferences, and may branch out into other domains.
Measuring Political Polarization. There were no significant effects of political polarization, suggesting that an alternative construct of polarization may be helpful. Specifically, a more comprehensive measure of political polarization may not simply measure affect towards opposite political parties (such as in the form of a feeling thermometer or attribute rankings), but would also capture another dimension – resistance to change, or resistance to alternative viewpoints. Current constructs of polarization effectively capture belief strength, but avoid the latter dimension of belief stability. The measure of affective political polarization in this experiment assessed polarization by attributing a single number to a person. Instead, political polarization may represent other emotive states, such as happiness, or anger, which can wax and wane over time –in which case, the implications of affective polarization on media selection may be better tapped by assessing the elasticity of polarized attitudes, rather than their sheer strength. When initially confronted with news content in this study, individuals appeared very eager to seek out the news sources aligned with their own political perspective. However, as the experiment progressed, the desire to again select the same source diminished somewhat. Thus instead, a different metric with which to assess polarized attitudes would be not an overall affective temperature towards another party, but would instead encompass resistance to changing or straying from one’s political beliefs. This may also more easily facilitate comparisons in other political systems, where the two-party model is not as prevalent. As designed, the polarization scale measures opinions to two opposite parties. There is no data about how highly polarized individuals—according to this scale— would rate Independents or moderates. For instance, would Democrats rate Independents as low as they rate Republicans, or would ratings of Independents be more in line with what they attribute to their own party? Incorporating a third group into the polarization measure may be better able to assess tolerance, and whether polarized attitudes are driven by negative opinions towards any out group, or whether negative affect is only attributed to the polar opposite party. Adding measures for additional groups—Independents, the Green party, or even religious groups (Mormons, Catholics)
105 and societal groups like labor unions—could reveal more nuanced in-group out-group preferences. Even a sheer baseline measure of attitudes towards fans of specific sports teams (e.g. Yankees versus Mets) could assess how deep-rooted an individual’s in- group out-group polarization may be, or whether their polarized attitudes are simply directed towards the political boundaries between Republicans and Democrats. Such measures may also more accurately tap into females’ latent polarization, as research consistently shows females as having less political interest and weaker political opinions than men. Polarization is currently high on the agenda of political scholars, and correlating political polarization with other contexts could offer more context and interpretation beyond what is currently known.
106 CHAPTER 4
EYETRACKING STUDY: BEHAVIORAL PROCESSING OF ONLINE NEWS
ABSTRACT
News organizations routinely strive for greater revenue and market share, and now offer news content in a variety of formats beyond mere text display. It is common to see online news supplemented with graphics, animated slideshows, and other graphical features. Yet, relatively little research has assessed how these formats of news display will affect audience reading and news selection behaviors. Graphical presentation of news information may induce unintended biases, making certain types of content more or less appealing, as was shown in Chapters 2 and 3: graphical news layouts encourage the repeated selection of soft news (instead of hard), and also encourage repeated selections to the same source. Yet, little research has evaluated the behavioral processing of news—what cognitive and eye movement processes guide the reading, scanning, and selection of news content. Most media effects research is founded in other methodological paradigms, predominantly surveys (for self-reported news behaviors) and experiments. While these methods provide insight into what content an individual ultimately selects, they do little to explain how these decisions are made. This study uses eyetracking to better understand the cognitive and behavioral processes behind news selection, contrasting two interfaces – graphical and text. Results reveal clear differences in the processing of text and graphical news displays. Specifically, fixation duration—a measure of interest and cognitive processing—is shorter when news is presented graphically, and for soft news topics. The graphical condition also facilitates more rapid decision-making, with less time spent per page. Furthermore, page design and story location have significant effects on how much attention is given to individual news content on the page: unsurprisingly, in both conditions, more attention is given to news stories on the top of the page. Finally,
107 saccade length, a measure of the overall pattern of scanning a news page is significantly longer in the graphical condition, indicating a less linear reading and page viewing.
108 4.1 INTRODUCTION
The stimulus materials used in this study are the same as those described in Chapters 2 and 3, and are modeled after the standard layout of Google News and Google Fast Flip at the time of the study. Chapters 2 and 3 present the results of an online survey and experiment, where participants were asked to select from eight categories of news to read (ranging from hard to soft topics), as well as a specific news story to read, within the selected topic. (The news story content was randomly attributed to one of six news sources – CNN, Fox News, Huffington Post, The New York Times, USA Today, and The Wall Street Journal). The results of the analyses in Chapters 2 and 3 indicate that the graphical presentation of news encourages the selection of soft news topics, most notably for the less educated, and the selection of—or repeated selection of—specific news sources. The graphical layout of news can influence both of these behaviors, indicating there are two, potentially competing, hypotheses: (1) the pictures and photographs included in the graphical treatment activate neurological reward mechanisms (e.g., Aharon et al, 2001; Bray & O’Doherty, 2006), encouraging individuals to seek out more soft content, or (2) that the graphical treatment emphasizes the source brand, adding a salient visual cue on which newsreaders can default. In order to assess the strength of each hypothesis, we use eyetracking to better understand how the visual structure and layout of an online news page affects viewing patterns and news choices. Eyetracking data provides insight into what components of a news story an individual fixates upon – whether it is the source logo, the headline, the photo, or the actual story text. In addition to providing more evidence to the results presented in Chapters 4 and 5, this eyetracking study will assess more basic metrics about the processing of text versus graphical news content, such as the order in which page content is scanned, how long individuals attend to each region of the page, and even simply how memorable are aspects of the story that was read.
109 VISUAL PROCESSING Neuroscientific literature identifies unique behaviors in the processing of textual versus graphical information. Visual scene perception. In visual scene perception, researchers have found that viewers immediately process the image in a way that enables them to grasp the “gist” of the content (Biederman, 1972; Li et al, 2007; Oliva, 2005 Rayner, et al, 2009), which frequently occurs in fewer than 100 milliseconds (Oliva, 2005; Rousselet et al, 2005). The allocation of attention in a visual scene is also driven by (potentially) competing cognitive (top-down) and visual factor (bottom-up) interests, making it important to understand both the perspective and motivation of the viewer (in terms of user goals and intent), as well as the unique and salient attributes of the content itself (Desimone & Duncan, 1995; Itti, 2005; Loftus & Mackworth, 1978; Parkhurst, Law, & Niebur, 2002; Rayer, 1995; Treisman & Gelade, 1980). In visual scenes, much work has shown that the eye is driven to more “informative” regions of the page, frequently defined as the “non-redundant”, e.g.. unexpected items (Wolfe et al, 2011; Loftus & Makworth, 1978). Visual imagery is also known to aid in content comprehension, information storage and long-term memory (see Mandi & Levin, 1989; Waddil & McDaniel, 1992). Text processing. The processing of textual information has been well documented (see Rayner, 1998 for an extensive review). Research shows that text processing is affected by (i) content difficulty, such that when text information is more difficult to parse, it will require longer fixations and regressions (defined as “backwards” eye movements, when the individual re-reads content) (Rayner, 1995); (ii) reader expertise, such that individuals can more easily process content in a familiar domain, and (iii) most basically, the typography and alignment of text (Ojanpää, 2002). Eyetracking and reading research also shed light insight into parafoveal (peripheral) vision: readers can process more to the right of the fixated word (by about 10 characters), than to the left- or vertical boundaries of the visual field (Juhasz et al, 2008; Ojanpää, 2002; Rayner, 1995). Multimedia and Web content. Clearly, Web content is a mix of both text and visuals, yet much of the research in Web domains is not driven by establishing new theories of eye movements, but rather applies foundational knowledge of eyetracking to
110 more varied Web stimuli. With technological improvements and cost reduction in the past five to ten years, there has been an influx of eyetracking research in Web-based contexts (e.g., Buscher, et al, 2009; Hotchkiss, Alston, & Edwards, 2006; Jacob & Karn, 2003; Nielsen, 2006; Pan et al, 2004). In particular, an especially large body of research has evaluated the visual processing of online search engine result pages (e.g., Cuttrell, Guan, & Morris, 2007; Goldberg et al, 2002; Granka et al, 2004, Joachims, et al, 2007; Lorigo et al, 2006). All of this research points to a clear location bias towards the content positioned at the top of the Web search results page – commonly referred to in industry as the “golden triangle” of attention at the top-left of a web page. Beyond the concentration of eye fixations, the ordered list of search results is viewed very quickly (typically in under five seconds), and attention typically wanes after the first 3-5 results. However, the visual position bias is also contingent on result quality: when content is reversed or when the desired content is listed lower on the page, viewing patterns will be more extensive (e.g., Joachims, 2007; Guan & Cuttrell, 2007). Other Web-based eyetracking research suggests that the content positioned at the top of the webpage will generate the most attention; in particular the upper-left quadrant, as this provides the context for parsing the rest of the page (Goldberg et al, 2002; Granka, Hembrooke & Gay, 2006; Nielsen, 2006). More recent research has also evaluated the processing of a list versus a grid layout, indicating that fixations are more evenly distributed when information is presented in a grid form (Kammerer & Gerjets, 2010). Although no eye-tracking experiments have isolated the effects of graphical and textual news display, the Stanford Poynter eyetracking project did an extensive study of online news reading, using existing online news sites, such as The Washington Post or The New York Times (Stanford Poynter project, 2000 & 2004), and evaluated what parts of these news webpages—whether it be photos, headlines, or advertisements—attract the most attention. The Stanford Poynter project was one of the first studies to provide comprehensive descriptive accounts of online news reading, finding that text content – namely headlines – generate the majority of eye fixations, and that pictures tend only to receive multiple fixations during a second viewing (i.e., after the user comes back to the news page from reading another story).
111 MEASURING EYE MOVEMENTS
Eye movements have long been used to better understand gaze patterns. Much eyetracking research has been done in the field of psychology, to understand reading behaviors (see Rayner, 1998), visual search behaviors, and the coordination between visual processing and movement (e.g., Pelz, Canosa & Babcock, 2002). Eyetracking has been used with much success to evaluate decision-making processes (e.g., Russo & Leclerc, 1994), medical applications, driving and flying simulations, evaluating effectiveness of marketing (e.g., Pieters & Warlop, 1999), and numerous other domains (for an extensive review of eyetracking applications see Duchowski, 2002). As discussed, eyetracking has recently become very popular in Web-based contexts due to cost reductions and hardware and software developments. Before proposing specific hypothesis and related research, it is first important to understand the basics of eye movement. The eye moves extremely rapidly, and with current technology, is most commonly measured in milliseconds. Several key variables have emerged as significant indicators of ocular behaviors, including fixations, saccades, pupil dilation, and scan paths (for a review, see Rayner, 1998; McConkie & Loschky, 2002). These described metrics are the ocular movements that will be used in this research, to evaluate the visual processing of graphical and textual news displays. Fixations. Recall that the eye moves extremely rapidly. Several times a second, the eye “pauses”, and it is during these instances that an individual can absorb information (Rayner, Salthouse & Ellis, 1980; Henderson & Pierce, 2008; Just & Carpenter, 1980; Rayner, 1998). These pauses are defined as fixations – a spatially stable gaze lasting for approximately 150-300 milliseconds, during which visual attention is directed to a specific area of the display. Eye fixations are standard and the most relevant metric for evaluating information processing, as they provide the most basic indication of what the eye is looking at. Fixations are clearly understood to be indicative of where a viewer’s attention is directed, and represent the instances in which information acquisition and processing is able to occur. Fixation Duration. Each fixation has a unique duration – during which information processing occurs. Research has shown that a number of things influence fixation duration, including an individual’s interest in the content, the complexity of the
112 content, and associated levels of cognitive processing (e.g., Duchowski, 2002; Pelz et al, 2000; Rayner, 1995; Salthouse & Ellis, 1980). Fixations average about 150-250 milliseconds during text reading (and will be longer for more complex content), and approach 350-400 milliseconds during other types of reading – such as music reading, or when individuals engage in another task during the study – such as speaking aloud or typing (Rayner, 1998, Pelz, et al, 2000). Translated to a Web interface, the existing research reasons that information complexity, task complexity, and unfamiliarity of visual display will all influence fixation duration (namely, by increasing it).
Saccades. Saccades are the natural by-product of fixations, defined by the time between fixations, and are frequently measured as a distance – the line connecting two consecutive fixations. Saccades are very rapid (lasting only 40-50 milliseconds), and approach velocities of nearly 500 degrees per second. Information acquisition is unable to occur during this time. During reading, saccades are fairly short, predicated on the short distances between each eye fixation, due to reading sequential individual letters and words. In more graphical and pictorial contexts, saccades are frequently longer, as viewers obtain the gist of the content via fixations across the page. Further, saccades and fixations change throughout a single viewing session: when an individual is first presented with an image, saccades are longer—people fixate on diverse regions of the scene to obtain the gist, then saccades eventually decrease in length. The converse is generally true for fixation duration – initial short fixations become longer when individuals more carefully attend to the content (Antes, 1974; Myers & Gray, 2010; Tseng & Howe, 2008). Scanpath. A scanpath is defined as a user’s sequence of fixations and saccades, representing the pattern of eye movement across the visual scene. Scanpath behavior provides insight into how a user navigates visual content, and is not random, but highly related to a viewer’s frame of mind, expectations, and purpose (Josephson & Holmes, 2002, Lorigo et al, 2006; Noton & Stark, 1971; Yarbus, 1967). In Yarbus’ seminal work, study participants viewed a painting titled “The Unexpected Visitor,” which depicts a family in a living room and an unknown man walking through the door. This research showed that study participants produced unique scanpaths based on the task they were given; for instance “Estimate the material circumstances of the family” or
113 “Remember the clothes worn by the people”. This was one of the first works to emphasize the cognitive top-down effects of the viewer’s goals, and other research has since replicated this. Another insight into scanpath behavior has shown that repeated viewing of a visually similar stimulus produces more efficient scanpaths and fewer fixations on subsequent visits. Repeated viewing is hypothesized to have such an effect because individuals have internalized the structure of information display (Josephson & Holmes, 2002; Myers & Grey, 2010). Scanpath analysis has enabled researchers to create a more comprehensive understanding of the entire behavioral processes during a visual search or scanning session. Despite this, eye movement patterns are very difficult to analyze, due to the high level of natural variance noise inherent to subconscious eye movements. Much of the research comparing scanpaths has typically been qualitative and evaluative, and only recently have researchers have begun to compare the difference in scanpaths with robust statistical analysis (Feusner & Lukoff, 2010).
4.2 METHODS
Stimulus Materials. The news stimuli materials used in the eyetracking experiment were the same as those used in the online experiments described in Chapters 4 and 5. The online experiment was conducted over a Friday-Sunday period, and the eyetracking experiments were conducted on the following Monday-Wednesday, ensuring the news content was still timely. While the materials were the same, the experimental structure was modified to power the analysis of this smaller scale experiment. The eyetracking experiment was administered as a repeated-measures within- subjects design. Participants viewed six pages of news: three of the text-only condition, and three of the graphical condition, which were presented in an alternating sequence (i.e., text, graphic, text, graphic…, or graphic, text, graphic, text…). Contrast this to the experiment design in Chapters 2 and 3 where participants saw only one type of news display. All 34 participants viewed six pre-selected categories of news—World, US Politics, Sports, Opinion, Entertainment, and Environment—a single page for each
114 news category. The presentation order of news categories cycled through a Latin Squares rotation scheme for all participants. Each news page contained stories from a specific news section. These news categories were ultimately clustered into hard news (World, US Politics, Environment, and Opinion, as per Chapters 4 and 5), and soft news (Sports, Entertainment). News type is treated as a fixed factor in the subsequent analyses, as it is hypothesized that individuals may process hard and soft news differently, due to the differing cognitive complexity of the associated content. Upon selecting a story that interested them, study participants would advance to the next screen of news and repeat the procedure, until they had viewed all six screens. Upon completing the news selection eyetracking component, participants completed an online survey, asking general questions about news consumption, news interest, and recall measures – what sources they remember seeing in each news condition (full questionnaire listed in Appendix B). Survey questions were combined to produce two primary metrics: (1) soft news interest (as measured through participants’ self-reported frequency of reading or watching Sports and Entertainment content, and (2) hard news interest (as measured by participants self-reported frequency of reading or watching news about (i) world affairs, (ii) the environment, (iii) US politics, and (iv) opinion). The questionnaire also captured gender, age, and party identification.
Eyetracking study instructions We are studying how the layout of news aggregator websites might influence user behavior. We’d like you to browse through the following 6 news pages and select the stories that most interest you.
Please use the mouse to select a story that you are most interested in reading. Once you’ve clicked on a story, please use the forward arrow key to advance to the next screen. You will do this six times. All of your responses will be kept completely confidential, and will not be personally identifiable.
Participants. Thirty-six employees of a technology company in northern California participated in this study. Participants were recruited via email by sending a message to the company’s general miscellaneous list. Membership to the list is optional, and dependent on the employee to opt-in. At the time of the study, an estimated 4,710 employees were subscribed to the list. Study participants ranged in age
115 from 22 to 40 with a mean age of 30.67. Participants were given a company t-shirt as a token of appreciation. Eyetracking data from two participants were removed from the analysis due to suboptimal eye calibration and low quality precision in eye movements1. In total, data from 34 total subjects were used in the analysis. Twenty-four of the 34 participants were registered to vote in the United States. There was a gender imbalance, with 9 female participants, as well as a ideological skew: 26 of the 34 participants self- identified as Democrats, two as Republican, and six as Independents.
Data Capture. The eyetracking data was captured with the Tobii 1750 eyetracking hardware and Tobii Studio software. When configured with the ideal participant calibration, the eyetracker is accurate within 0.5 degrees of visual angle, which is approximately 15 pixels on the screen (based on the participant sitting about 16 inches from the monitor). The screen resolution was set to 1280 x 1024, and the news iamges were magnified at 150% so as to fill the screen at that resolution.
Data Processing. The eyetracker produces a unique data file for each participant, which stores all metrics of the viewing session – including the timestamp, fixation duration, fixation location. These files were then reformatted and merged into one master file. Additional calculations were computed to produce the metrics used in this subsequent analysis, including: total time to click (per page), the selected story (its location, source, and ID), and total time spent looking at each of the six stories (per page). The survey responses from the separate survey form were also added to the master eyetracking data file.
1 It is not uncommon for eyetracking software to fail on calibration for individuals with hard contact lenses or bifocal glasses, which is what these two participants were wearing.
116 4.3 ANALYSIS
OVERVIEW In total, 203 pages of news were viewed by all of the 34 participants (one participant viewed only five news pages instead of six, due to an error.) Participants fixated at least once on all six stories in 192 out of the 203 total pages. To analyze results, multilevel mixed models were computed in R with the lme4 package (Bates, 2010). Participant was always treated as a random factor, and additional random and fixed factors are specific to each analysis, based on hypothesis and theory.
HYPOTHESES
a) Time to click: longer in text condition. On average, individuals will take longer before making a selection in the text condition. Because the information cues are text-only, the only way to get a sense of the story is through reading, which takes longer to cognitively process than pictures. Conversely, in the visual condition, individuals will use source logos and associated story pictures in lieu of reading, which could reduce the total time needed before making a selection. b) Total stories read: fewer in text condition. Presented with an information-dense display, individuals will be more likely to satisfice in the text condition, measured by viewing fewer than the total number of available stories. The text information is more labor intensive to process, with richer information density, and individuals may simply opt to read less. c) Fixation Duration: longer in text condition and for selected stories. Recall that fixation duration is positively associated with content complexity and an individual’s interest in the content. On average, fixation duration is hypothesized to be longer in the text condition—it requires more cognitive effort to process text as opposed to pictorial or graphical information. We also hypothesize that fixation duration will be longer when individuals are interested in the news content they are reading, as measured by which story is clicked. d) Saccade length: longer in visual condition. On average, the graphical condition will produce longer saccades than in the text condition. Because graphical
117 stimuli can more quickly provide a gist of the information content, individuals will be better equipped to fixate on disparate regions of the page with each fixation, extracting the key or salient elements of each news story. With a text- only presentation of news, more sequential viewing is required, thus producing shorter saccades between fixations. e) Location effects: more time viewing content in top-left. As previously documented, there is a strong attentional bias towards content placed at the top of the page – in particular, towards the content placed “first” (in the top-left quadrant of the page). However, some recent research findings indicate that grid layouts, as opposed to list layouts, produce a near-equal distribution of viewing time to all page content (Kammerer & Gerjets, 2010). Despite this, we still hypothesize that across both conditions, the story located in the upper-left of the screen is expected to generate on average more fixations and viewing time. The variance between most-viewed and least-viewed story may be larger in the text- only condition, as some people may choose to read stories in their entirety, and others may lose interest in reading beyond the “golden triangle”. f) News story elements viewed: headlines (text), source logo (graphic). In both the text and graphical condition, participants may fixate most on story headlines, as this is also the most “informative” and easy to parse. Individuals may not fully read the story content in attempts to simplify the information-dense display. In the text condition, participants are hypothesized to fixate relatively little on source, potentially due to the unobtrusive font size and grey color. In the graphical condition, the source banner and logo may generate the most initial fixations, due to the visual salience of this item. Previous news eyetracking research has shown that individuals do not fixate on an associated story picture during the first viewing (Stanford Poynter Project, 2004); this behavior might also be present in this study. g) Source recall: higher for graphical content. It follows from the hypothesis above that if individuals are more likely to attend to the source banner in the graphical condition, they may also be more likely to recall the names of the sources they viewed in the graphical condition.
118 4.4 RESULTS
DESCRIPTIVE EYETRACKING DATA
Overall plots contrasting all fixations in the text and graphical conditions are presented in Figures 4.1 and 4.2. A visual inspection shows that individuals read more of the available content in the text display when compared with the graphical. It is clear from Figure 4.2 that participants predominantly read the logo, headline, and occasionally fixate on the accompanying story photo. (Though it should be noted, due to the size of the images, the story text in the graphical content was slightly blurred, thus making it difficult to read).
Figure 4.1: All plotted fixations in text condition. Darker regions were looked at more frequently.
119
Figure 4.2: All plotted fixation locations in the graphical condition. Darker regions were looked at more frequently.
TIME TO CLICK
A basic measure of page viewing between the two conditions is time to click. Time to click measures the length of time an individual takes to choose a news story: a shorter time-to-click is indicative of an individual’s ability to more quickly decide upon an interesting news item. As previously stated, it is hypothesized time to click will be shorter in the graphical condition, and for soft news topics. The average time to click across both treatment conditions combined was 20.570 seconds (sd= 12.276), and was indeed significantly shorter in the graphical condition
(Welch 2-sample T-test: t (170.43)= 2.88, p-value <0.01). On average, story selection in the text condition took 23.06 seconds (sd= 14.288), and in the visual condition, 18.14 seconds (sd = 9.396). Note also the difference in standard deviations, meaning that people may behave more like one another in the graphical display, rather than the text; perhaps the graphical condition has some “averaging” effects. There were no significant gender differences in the time to select a story (Male = 20.78 seconds,
120 Female = 19.950 seconds). Between participants, the average time to click was fairly evenly distributed, though there were in fact two outliers, who took 10 to 15 seconds longer before making a news selection than the next slowest participant. Upon further inspection, while these individuals took longer to select a story, it was due only to longer reading time in the text condition: they did not emerge as outliers in the graphical condition. Plots of these effects are included in the Chapter Appendix. We first evaluated the presence of participant-level effects with a simple linear regression, treating participant (N=34) as a fixed factor (using the “within” model specification in the plm R package (Croissant & Milo, 2010)). The model adjusted R- square shows that a third (33.7%) of the time-to-click variance is to be attributed to participant differences. However, the results of the Hausman test indicate that we cannot reject the null hypothesis that the two (fixed and random) estimators are equivalent (χ (4)= 0.7116, p = 0.945), so we prefer a model with the more efficient random effects estimator of participant.
Hypotheses: time to click. In order to measure which factors might influence an individual’s ability to more quickly make a news selection, a linear mixed model was generated in R (Bates, 2010). Two models were constructed: one model included all participants, and the second model removed the two time-to-click outliers. Only the full model results are reported here, as the same predictor variables were significant in each model, with only slight differences in effect size. Predictor variables included: (a) News type (i.e., hard or soft news): Individuals may spend more time selecting content in the hard news categories, as the subject matter is more complex and potentially difficult to process. (b) Visual Treatment (i.e., text or graphical): News selections will be made more quickly in the graphical condition, as it is easier for the human eye to parse visual, as opposed to textual, content. (c) Hard News Interest. Individuals with greater levels of news and public affairs interest will be able to make a decision more quickly, as they are more familiar with the current events. Conversely, per their interest level, these individuals may spend more time reading, simply to more fully consume news content.
121 (d) Soft News Interest. Individuals who express much interest in soft news may be more prone to scan hard news content, or make selections more quickly. (e) Trial number: Participants may make news selections more quickly as trials progress. This may either be due to participants’ increased efficiency in viewing the similar visual stimulus (as found by Josephson & Holmes, 2002; Myers & Grey, 2010), or may simply be due to participants becoming less vested or interested in the experiment.
Results: Time to Click. Results indicate a significant negative relationship between time to click and: (i) graphical condition (β = -13,264.84, t = -3.96, p <0.001), (ii) age (β = -634.08, t = -2.29, p <0.05), (iii) soft news interest (β = -3491.39, t = -2.28, p <0.05), and (iv) experiment trial (β = -1289.53, t = -3.45, p <0.001). These results suggest that news stories are selected in shorter time in the graphical treatment condition, when an individual is older in age, and when an individual is more interested in soft news. The largest effect on time to click was the graphical condition—on average, individuals selected content 13.3 seconds faster in the graphical condition than they did in the text condition. This shorter time to selection may be viewed either positively or negatively depending on the circumstances. A positive interpretation is that the graphical news layout may simply enable individuals to read more news content in a shorter amount of time; a negative interpretation is that individuals may simply not read all of the stories, acquiring only a cursory, rather than an in-depth reading of news content. Further, when combined with the results discussed in Chapters 2 and 3, it is clear that not only do people make selections more quickly in the graphical format, they are also more likely to subsequently select soft news topics. These findings combined offer slightly more weight to the negative implications of the graphical condition – that individuals may engage in a less-substantive reading of the news. Finally, there was a significant positive interaction between graphical treatment and soft news interest (β = 3206.60, t = 2.65, p <0.01), which suggests that as an individual’s soft news interest increases, their time to click is more likely to decrease in the text condition than in the graphical (Figure 4.3). Specifically, time to click was
122 fairly constant across all individuals in the graphical condition, while in the text layout, there was a distinct decrease in time to click for those individuals most interested in reading soft news. For example, having the highest level of soft news interest decreases time to click by 17 seconds (-3,491.39 *5 = -17,456.95 ms) in the text display, compared to those scoring lowest on soft news interest. However, the graphical condition tempers these effects, suggests potential averaging effects of a graphical display. When presented with text-only content, individuals with more soft news interest engage in a less thorough evaluation and selection of news.
Table 4.1: Factors Affecting Time to Click Linear Mixed Model Results
Estimate Variable (associated levels) (Milliseconds) (Intercept) 59433.87 (10490.66) Graphical Condition (1) -13264.84** (3353.67) Soft News Exposure (1-5) -3491.39* (1533.39) Hard News Interest (1-5) -1343.16 (1756.01) Hard News Category (1) 1387.14 (1369.75) Age -634.08* (277.14) Page (Trial) Number (1-6) -1289.53** (374.07) Graphics * Soft News Exposure 3206.60** (1210.67) N= 204, Participants= 34 Participant 46983191 (Std. dev.) (6854.4) Residual 82944256 (9107.4) AIC 4236.6 BIC 4269.8 Log Likelihood -2108.3 Deviance 4340.8 * p < 0.05, ** p < 0.01
123
Figure 4.3. Time to Click Plot: Interaction between graphical display and soft news interest. X-axis represents soft news interest (low to high), and the y-axis represents time to click. When people have more interest in soft news, they take overall less time to click in the text condition. There is no such significant effect in the graphical condition.
Figure 4.4: Time to Click: Partial Effects Plot of Main Effects Significant effects of time to click are the graphical news layout, experiment trial, participants’ self- reported soft news interest, and age. Insignificant effects include hard news interest and news type.
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Figure 4.5: Time to Click, by Participant and trial (N=6) This plot depicts the viewing behavior of all 34 participants, as they view each of the text and graphical conditions. Recall that participants’ experimental stimuli alternated between text and graphical news displays: this graph makes it clear that the time to click is nearly always shorter in the graphical condition.
125 INFLUENCE OF PAGE LOCATION
Time spent in each page location. As described, existing eyetracking research has analyzed the amount of attention people give to Web content, particularly in the case of ordered list layouts (as in the case of search result listings) (Granka, et al, 2004; Cuttrell, Guan, & Morris, 2007; Guan & Cuttrell, 2007), and more recently in grid layouts (Kammerer & Gerjets, 2010). In list layouts, individuals allocate significantly more attention to the items at the top of the list, and attention drops significantly after about the third item. Given a non-linear layout, one might expect that individuals allocate more equal levels of attention to each story, as the ranking effects are much less pronounced. As we can infer from the time to click data, it is only reasonable that participants spend relatively less time in all grid locations in the graphical condition as compared with the text condition. The more interesting question is therefore how much time individuals spend in each page region, relative to each visual condition.
RESULTS Results indicate that despite presenting content in a grid format, a non-equal amount of time was given to the six news stories presented (χ (5)=789.713, p < 0.001). Greater time was spent reading / viewing news stories on the top row of the page, particularly the upper-left. This effect held for both text (χ=787.71, p < 0.001) and graphical conditions (χ =126.35, p <0.001). The tables below report the total time spent viewing each news story in each of the two conditions; bolded numbers indicate the regions where the observed viewing time was much greater than the expected value, as inferred through visual inspection of the expected values table. Participants spent the most time reading the story in first grid location (top left) in both the graphical and the text condition (see Table 4.2). In the text condition, top left page location generated longer reading times by at least 799ms (when compared with top middle), and by1701 ms (bottom middle). In the graphical condition, the difference in viewing time was marked by 733ms (top middle), and by 844ms (bottom right). The average time spent reading each of the six news stories on the page was 4,958 milliseconds (sd = 3600.429ms), with an average of 3,942.39 milliseconds (sd = 2605.275) in the graphical condition and an average of 5,783.75ms (sd = 4057.705) in the text condition. Indeed, a Welch 2-sample T-test shows that the text condition
126 generated significantly longer look-times (t(12142.58) = -31.011, p < 0.001). Participants spent nearly six seconds on average reading each news stories in the text condition, and less than four seconds on average in the graphical condition. A simple inspection of the data in Table 4.2 does not provide ready proof of our hypothesis that an equal amount of attention (time) is given to news content in the graphical format. Instead we see that in both visual displays, the story presented “first” in the top-left of the page is in fact the one viewed disproportionately longer. Participants spend nearly 3 seconds longer viewing the top left story in the text condition as compared with bottom left.
Table 4.2: Total time spent in location, in milliseconds Text Treatment Graphical Treatment Left Middle Right Left Middle Right 6396.36 5538.74 4591.49 Top 5212.741 4479.229 5149.75 3880.60 4478.91 4965.57 Bottom 4794.832 4639.901 4368.74
2 2 χ (5)= 789.71, p <0.001 χ (5) = 126.35, p < 0.001 Expected = 4975.28 Expected = 4774.20
To more fully assess the influence of page location, a linear mixed model was produced, regressing time in page location on the fixed factors of location, visual treatment, whether or not the story was clicked, and whether the story is a hard news topic. As the primary interest in this model is page location, random effects included participant, source, and trial (news page). Results (see Table 4.3 for details) again confirm significant positive effects of graphical treatment (β = 2311.43, t = 23.45, p <0.001), story location, whether a story was clicked (β = 2679.87, t = 52.79, p <0.001), and whether a story was a hard news topic (β = 1054.64, t = 8.18, p <0.01). For example, in the text condition, news stories that are ultimately clicked are looked at for 2.68 seconds longer than stories that aren’t selected. This would increase by another 1.05 seconds for a hard news topic. While seconds of course seem like a very small amount of time with which to be evaluating news reading, in the context of eye movements, the information that can be gleaned from an additional three seconds is substantial, and indicates much more attention.
127 Table 4.3 Time spent in page location Linear Mixed Model Results
Fixed Effects Estimate (Intercept) 7550.38 (1392.43) Graphical Treatment -2311.43 ** (98.64) Top Middle -993.70 ** (96.39) Top Right -1518.34 ** (101.31) Bottom Left -2024.65** (103.86) Bottom Middle -1804.18** (103.18) Bottom Right -1625.97** (102.22) Clicked 2679.87** (50.77) Hard news topic 1054.64** (128.98) Soft news interest 26.08 (333.60) Hard news interest -737.39 (447.25) Graphics* Top Middle 734.06** (142.05) Graphics* Top Right 1324.29** (149.58) Graphics* Bottom Left 974.07** (155.53) Graphics* Bottom Middle 1032.15** (149.32) Graphics* Bottom Right 537.24** (153.79) Hard news * soft news interest -328.15** (47.31) N = 12,527; 34 participants; 6 sources; 6 locations Random Effects Participant: 1886.27; Source =145.75; News Page = 392.85 Residual 2492.49 AIC 235223 BIC 235380 Log likelihood - 117591 Deviance 235369 * p < 0.05, ** p < 0.01. Standard errors in parentheses
128 Interactions existed between the visual treatment and page location (see Figure 4.6), and an interaction between soft news interest and hard news categories (β = - 328.15, t = -6.94, p <0.001). These results confirm that the story ultimately selected is in fact the one read for the longest amount of time, and also that the text and graphic layouts produce unique biases towards particular page locations – the graphic condition generates more near equal viewing time for stories in the top row than does the text condition. Finally, individuals with higher levels of soft news interest spend less time viewing hard news content.
Figure 4.6. Total Story Time Partial Effects Plot: Attention to content in each page region Note the difference in time spent on the top row and bottom row of the news display. In the text condition, it is clear that the top-left story is viewed longer than all other story regions on the page. In both conditions, the content in the top row is viewed for a longer period of time than is the content in the bottom row, creating tiers of attention to content.
129 ORDER OF PAGE VIEWING As discussed, the layout and display of information exerts a bottom-up influence on visual processing; this is driven by both intentional and unintentional visual cues. For example, when information is displayed in a list format, individuals are expected to view content in a linear order; in fact they do. In grid layouts, however, the sequential viewing pattern is less obvious. In order to obtain a crude measurement of viewing order across the six stories on the page—as a proxy for the most common viewing path—we can analyze which ith fixation event when participants first looked at a page region. This measure was obtained by computing the first fixation even in each page region for all participants across all news pages. These first fixation events (minimum fixation values) were then averaged to obtain the data in Table 4.4 and Figure 4.7.
Table 4.4. Minimum fixation per page region, by visual treatment
LOCATION TEXT GRAPHIC LOCATION TEXT GRAPHIC TOP LEFT 7.91 5.76 PAGE TOP 7.91 5.76 TOP MID 6.38 4.59 TOP RIGHT 8.50 5.21 BOT LEFT 9.35 7.82 PAGE BOTTOM 9.35 7.82 BOT MID 7.12 7.03 BOT RIGHT 9.09 5.58
Results indicate that in both text and graphic conditions, the story content in the bottom row of the page was viewed about two fixations after the story content in the top row of the page. In fact, it also appears that a participant’s first fixation typically occurs in the top middle of the page – not the top left. While individuals may spend the most time in the top-left of the page, it is likely that participants first orient themselves to the page by making their first fixation in the center of the screen, as this allows an individual to best obtain the full scene gist. Figure 4.7 depicts the minimum fixation in each grid quadrant, across both the text and graphical content. This data is presented as a qualitative descriptive account of viewing order.
130 Average First Fixation to Page Region, by Visual Condition 10 9
Text 8 7
6 Graphic Mean Minimum Fixation Minimum Mean 5 4 3
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Figure 4.7 Average first fixation to page region, by condition. This plot shows the average first fixation to each page region: clearly the content fixated on earlier in the session is the content at the top of the page, in both conditions. Further, the page region with the earliest fixation value was the story in the top-middle of the page, indicating that individuals may first fixate on this region to obtain a scene gist.
NUMBER OF STORIES VIEWED PER PAGE
It was hypothesized that individuals would read fewer total stories in the text condition, due to the extra effort it requires to process text content, and the greater temptation to select the first interesting news story encountered. Study results cannot confirm this hypothesis. In fact, seven participants did not fixate on all six of the presented news stories at some point during the experiment, though this accounted for only 11 out of the 203 total pages shown experiment (5%). Seven of these instances were in the text condition, and four were in the graphical condition. The one individual (gender=female) who three times did not fixate on all stories was also the participant who completed the experiment in the shortest amount of time, and there were no other significant differences in this population. This thorough reading behavior may be
131 explained by the experimental environment – in which participants felt it appropriate to read fully.
FIXATION DURATION
Hypotheses. Fixation duration can be used to determine how much attention individuals give to certain types of news (hard or soft), news formats (text or graphical), or even individual news stories. Fixation duration can be used a proxy to measure attention and cognitive processing – longer fixation durations have been linked to increased interest, arousal, and cognitive processing. Another linear mixed model was generated to measure which factors might influence an individual’s fixation duration. Predictor variables included: (f) News Type (i.e, hard or soft news). Recall that hard news included the categories of environment, opinion, US politics, and world news, and soft news included entertainment and sports. It is hypothesized that fixation duration will be greater in the hard news categories, as the subject matter is more complex and potentially difficult to process. (g) Visual Treatment (i.e., text or graphical): It is expected that fixations in the graphical condition will be shorter, as it is cognitively easier to parse graphical and pictorial content than it is to read text. (h) Soft and Hard News Interest. Individuals with greater levels of news and public affairs interest will be able to make a decision more quickly, as they are more familiar with the current events.
RESULTS The mixed models show that fixation duration is significantly shorter in the graphical treatment condition (β = 25.60, t = -3.36, p <0.001) and later experiment trials (i.e. order of the news page viewed) (β = -5.32, t = -4.05, p <0.001). This indicates that less intense processing or engagement occurs during the viewing of graphical news stories, and that also perhaps individuals begin to tune-out through later phases of the experiment. Results show that fixation duration is significantly increased when participants are looking at the news story they ultimately click (β = 39.84, t =
132 11.18, p <0.001) and when an individual is engaging with hard news content (β = 6.93, t = 2.06, p <0.05). These results provide an affirmation of using fixation duration as a measure of interest and cognitive processing—we hypothesized that fixation duration would be longer on the story that is selected, as participants must inherently find it more interesting. There is also a significant positive interaction between graphical treatment and trial (β = -5.32, t = -4.05, p <0.001), indicating that the effects of trial were for the most part only present in the text condition (Figure 4.8). The largest effect size was in fact for the story clicked: fixation durations are significantly (nearly 40 ms) longer when an individual fixates on the story that is ultimately selected. This supports the notion that there are physiological differences in individuals’ response to interesting content. Fixation duration also was significantly greater when individuals were reading a hard news story: as this type of news is more substantive in nature, it indicates that people may be making more effort to process this type of news information. The model also revealed that fixation duration was significantly shorter in the graphical condition, by about 25 milliseconds, confirming hypotheses that this content is either easier for individuals to process or that a graphical format of news simply discourages deep engagement and attention. Furthermore, simply by progressing through the experiment, participants’ fixation duration decreased, by about 5 ms on each additional page. However, there was a significant positive interaction between graphical treatment and experiment trial: latter trials containing the graphical layout did not decrease fixation duration, nearly washing out the negative influence of trial. As the graphical treatment produced on average shorter fixation durations, it is possible that there is simply less allowable variance for fixations to further decrease throughout the session.
133 Table 4.5 Factors Affecting Fixation Duration Linear Mixed Model Results
Fixed Effects Estimate (Intercept) 310.445 (26.404) Graphical treatment -25.599 * (7.622) Trial Number (News Page) -5.315 * (1.311) Clicked 39.844** (3.563) Hard News 6.931 * (3.368) Hard news interest -2.646 * (5.438) Soft news interest -1.365 (6.211) Trial * Graphical treatment 5.157 ** (1.957)
N = 12,527; 34 participants; 6 sources; 6 locations Random Effects: Participant 34.002 Source 0.000 Location 5.686 Residual 173.790 AIC 16482 BIC 16491 Log likelihood -82414 Deviance 164863 * p < 0.05, ** p < 0.01. Standard errors in parentheses
134
FIGURE 4.8. Interaction between visual condition and fixation duration. Plot indicates that fixation duration decreases over experiment trials for the text condition, but not for the graphical condition. It appears that attention and processing (as measured by fixation duration) remains fairly stable for graphical content, but is more likely to decrease with a text display.
SCANNING AND SACCADE BEHAVIORS
Saccade length. As hypothesized, saccade length is significantly longer in the graphical condition (t (10,879.48) = -9.44, p <0.001), indicating that individuals may be viewing the news in a less-sequential order, as opposed to how content might be viewed in the text-only condition. The average saccade length in the text condition was 120.1452 pixels, whereas in the graphical condition, it was 141.4272 pixels. Image onset versus entire viewing. A further analysis assesses saccade patterns in the first half of story viewing with the latter half – to determine whether the saccade behaviors in this experiment match what is known about visual processing. Specifically, the literature indicates that the first few saccades will be longer, as individuals seek to obtain a general gist of the scene; later saccades are subsequently shorter, as individuals attend more deeply to specific content. Indeed we do find that saccadic activity is shorter during the first few fixations, across both conditions (Text: T = 2.57 (534.35), p =
135 0.01; Graphic: T (312.34) = 2.07, p = 0.04). Table 4.6 below presents the mean saccadic distance in fixations 1-4 with the rest of the fixations, in both the text and graphic conditions.
Table 4.6: Saccade length during attention onset and later viewing Saccade Length (pixels) Text Graphic Fixations < 5 142.43 144.64 Fixations >= 5 128.06 128.75
Similarly, the text also suggests that the fixation duration of the first few fixations should be shorter as individuals parse the general page gist. Fixation durations are then expected to increase throughout the viewing session as individuals attend more deeply to specific content. Indeed we do find that fixation durations in both conditions matched what is expected in the psychological literature –the first few fixations are shorter, and get longer as individuals more fully process specific content; however this difference was only significant in the text condition (Text: T = 2.72 (636.91), p = 0.01;
Graphic: T (428.84) = -0.72, p = 0.47). Table 4.7 below presents the mean fixation duration of fixations 1-4 as compared with the rest of the fixations.
Table 4.7: Fixation duration during attention onset and later viewing Fixation Duration (ms) Text Graphic Fixations < 5 279.74 281.73 Fixations >= 5 296.47 288.34
To comprehensively assess differences in saccade length, a multilevel mixed model was conducted, regressing saccade length on visual treatment, news type (hard or soft), experiment news page, and a participant’s hard and soft news interest. The ‘time to click’ model (Table 4.1) results reveal that participants more quickly select a news story in the graphical treatment condition, and also in latter stages of the experiment. As such, it might follow that an individual’s page viewing behavior, as measured by saccades, will change as a result of news trial and graphical treatment. As such, an
136 interaction between visual treatment and news page was also included. Again, participant was treated as a random factor. Results presented in Table 4.8 reveal that, saccade length significantly increases in the graphical condition (β = 32.46, t = 6.10, p <0.01) and significantly decreases for hard news topics (β = -9.36, t = -3.99, p < 0.05). The fact that saccades are shorter in the text condition and for hard news topics indicates that fixations are located closer together—potentially signifying more detailed attention to the content. Finally, there was also a significant negative interaction of news page and graphical treatment (β = -3.037, t = -2.23, p <0.001) with saccade length decreasing in the graphical treatment throughout experiment trials, but remaining fairly constant in the text-only condition (Figure 4.9).
Figure 4.9: Saccade length by experiment condition and trial. Saccade length (y-axis) decreases across experimental trials (x-axis) only in the text condition.
As hypothesized, the graphical layout produced the largest effect on saccade length, as it is the most distinct bottom-up effect on the visual processing. An individuals’ ability to process graphical content is driven by eye movements that extend beyond sequential reading, specifically that individuals will be more likely to fixate on disparate regions of the news page with graphical content presented in a grid format.
137 Hard news topics also seem to produce shorter fixations, which indicates that individuals may more closely read the content of the page rather than jumping across to disparate page regions and images. Finally, saccade length is more likely to get shorter as participants progress through experiment trials in the graphical condition, whereas in the text condition, saccade length remains fairly stable. This is possibly because individuals simply become more familiar with the news layouts as the experiments progress, and exhibit more efficient page scanning behaviors. There is the most opportunity to increase viewing efficiency in the graphical condition, as there are multiple types of non-text content.
Table 4.8 Factors Affecting Saccade Length Linear Mixed Model Results Estimate (Intercept) 117.644 (14.759) Graphical treatment 32.461 ** (5.324) News Page -0.489 (0.904) Hard News -9.363 ** (3.368) Hard news interest 3.629 (2.346) Soft news interest 1.510 (3.438) Graphical treatment* -3.037 * News Page (1.362) N = 12,610; 34 participants Random effects Participant St. Dev 18.412 Residual St. Dev 121.977 AIC 157000 BIC 157067 Log likelihood -78941 Deviance 157006 * p < 0.05, ** p < 0.01. Standard errors in parentheses
138 SOURCE RECALL Finally, as hypothesized, the graphical condition triggered greater recall of news source cues than did the text-only condition. Due to technical difficulties, only 27/ 34 participants were asked a survey question regarding what news sources they recall in each of the two conditions. The following table shows some basic statistics based on these 27 individuals. Clearly, the inclusion of visual source cues triggers greater subsequent recall. Source information is likely to be more passively absorbed in the participant’s periphery when it is presented graphically, leading to improved recall performance.
Table 4.9: Average number of sources recalled, per experiment condition Avg. n sources correctly N participants correctly N participants recalling recalled (/participant) recalling 2+ sources 0 sources Text 0.74 5 (18.52%) 16 (59.23%) Visual 2.26 16 (59.23%) 5 (18.52%) N =27
4.5 DISCUSSION This study analyzed the influence of news page layout on viewing behaviors. Specifically, the study addressed how reading news content is affected by page location, news type, and news layout (text or graphical). Viewing metrics used in this study include fixation duration, saccade length, time to click, and time spent in each page region. A post-experiment survey asked participants to report the news sources viewed. Differences in cognitive processing between text and graphical displays. Results indicate that there are distinct differences in the processing of text and graphical news content, as well as between the processing of soft and hard news content. Processing substantive (hard) news content demands more cognitive processing and reading, as exhibited by longer fixation durations, shorter saccades, and longer per-story viewing time. A text-only news presentation also seems to encourage more in-depth processing of content, as is also evidenced by shorter saccades, longer fixation duration, and a longer time to click. However, the graphical news display does temper some of these effects, producing much less variability in behavioral viewing. This apparent
139 “averaging” effect in of visual processing in the graphical display may indicate that different strata of the population will be able to glean near-equal amounts of information from the graphical, versus the textual news display. This is consistent with research findings indicating that watching television news levels the knowledge gap in information gain between the most and least educated individuals, whereas newspapers serve to further widen it. The differences in behaviors of text and graphical display lend additional support to this finding. Effects of page layout. While news content was displayed in a grid, which may minimize order effects as compared with, say, a list, clear location biases still emerged: the majority of reading time on the page was spent viewing the stories in the top row; the content in the bottom row received distinctly less attention. In particular, the story in the top left of the page received disproportionate amounts of attention. This is consistent with results in Chapter 3 indicating that the story in the upper left region of the page was selected more frequently than any other page region, in both conditions. Graphical attraction to soft news. Finally, one question raised by the research findings in Chapters 2 and 3 is whether it is possible for the graphical condition to simultaneously encourage story selection based on the prominent source logo and associated story image. Specifically, whether individuals perceive both the source logo and associated story photograph, and use both cues to affect their subsequent news category selections, or a specific source. Results indicate that both hypotheses are possible, though the most striking evidence is in support of source banner influence. In the graphical condition, most attention (as measured in fixations) is given to the source logo and story headline, and source is also more likely to be recalled in a post-experiment memory test. This data supports the notion that individuals do in fact recognize the source in the graphical condition and may use it to affect subsequent story selections. While there are markedly fewer fixations on the accompanying story photo, it should be noted that fewer fixations are required to absorb this type of pictorial content—people can detect the gist of a scene and determine the content type in a short amount of time, even in less than 30 milliseconds or through one single fixation (Larson & Loschky, 2009; McConkie & Loschky, 2002). Furthermore, as previously described, viewing attractive faces and
140 pictures stimulates neurological reward mechanisms, which has the potentially to strengthen the subconscious effect of this pictorial cue. Therefore, have no evidence to reject the hypothesis that graphical news displays produce two significant effects—(1) the increased reliance on source cues due to the graphical prominence of source branding, and (2) more selections to soft news due to the attractiveness of celebrity and athlete photographs. Experiment trial. As evidenced in Chapters 2 and 3, news selection behaviors do change over time, as a participant progresses through the experiment, and the eyetracking results herein suggest the same. Participants may lose interest in the news content, or process the content less deeply over time. However, an alternative explanation may simply be that individuals become accustomed to the page design and display, and over time simply need to expend less cognitive effort towards navigating through the available display of news information. Future research. Overall results indicate that while it may be visually tempting for a news organization to engage a graphical news display on the Web, there are a number of adverse effects to this aesthetically pleasing type of display, effects that may negatively affect attention and information processing. A future study should also include a post-experiment questionnaire to assess how well individuals also remember the story content they read. As news sources are more likely to be remembered in the graphical news display, it is also possible that news information in a graphical display may also be better retained in memory, thus tempering some adverse effects of this display type.
141 CHAPTER APPENDIX
700 2600 2500 500 1400 900 400 1700 1500 600 200 2800 800 2900 300 1000 3100 2000 1200 Participant ID Participant 1600 3400 2400 1800 2300 2100 2200 3200 3300 1100 100 2700 1300 3000 1900
10000 15000 20000 25000 30000 Average Time to Click in Graphical Condition, in Milliseconds
Figure 4A.1. Average Time to Click, by Participant: Graphical Condition. Graphical display produces a linear distribution of viewing time across all participants.
700 2500 3100 1700 100 1400 500 2100 2700 400 200 300 600 2900 900 1500 1800 2600 2300 Participant ID Participant 3300 3400 800 1100 2000 1300 1200 2400 1900 3200 2200 1600 1000 2800 3000
10000 20000 30000 40000 50000 Average Time to Click in Text Condition, in Milliseconds
Figure 4A.2. Average Time to Click, by Participant: Text Condition Text display produces more variance in time to click across all participants.
142 CHAPTER 5
FUTURE DIRECTIONS FOR POLITICAL COMMUNICATION RESEARCH
5.1 NEWS DESIGN: EFFECTS OF TEXT AND GRAPHICAL DISPLAY Consumption of current affairs information now takes places on a range of different devices—Web browsers, mobile phones, tablet devices, to name a few beyond traditional print and broadcast media. Media owners routinely strive for greater revenue and market share with these new technologies, and are now offer news content in a variety of formats beyond mere text display. It is now common for news interfaces to include graphics, animated slideshows, and other visual features alongside news content. Yet, relatively little research has been done to assess how these new formats of news display affect audience news reading and news selection behaviors. As evidenced by the three studies reported herein, graphical presentation of news information may induce unintended biases, ultimately making certain types of content more or less appealing. Graphical layout encourages more soft news selections. On average, participants exposed to the graphical news layout were more likely to select soft news. Yet, education level moderates the strength of these effects as compared with the text layout (see Chapter 2 for full discussion of results). The gap in hard news selection between the most educated (college degree and/or post-grad) and least educated (no college degree) was most pronounced in the graphical, as opposed to the text, news display. Meaning, with graphical display, the most educated participants were more likely to select hard news, and the least educated were likely to select soft. This at first seems somewhat contrary to the results produced by Jerit, Barabas, and Bolson, who found that television news, a visually-rich information environment, was able to decrease the political knowledge gap between the most and least educated, particularly as compared with television. Their research found that when less-educated people were exposed to both visual and auditory signals (in the form of a television broadcast), they
143 were better able to recall and store the political information received. This conflict is resolved when considering the differences in broadcast television with the choice-based news exposure and selection required of the Internet. As the results indicate, information richness—represented by corresponding text and pictorial content—on the Internet may have quite different effects. Specifically, the Internet requires active choice, rather than passive consumption, meaning that individuals themselves create the opportunity to engage as much or as little in the news at their disposal. Graphical depictions of news content may simply make soft news more appealing to select, and this temptation disproportionately affects the less- educated participants. Graphical content and pictures of attractive celebrities and athletes may have its neurological rewards, in that the brain circuitry associated with processing attractive and beautiful faces does trigger reward signals in the brain. It is possible that individuals with higher education and knowledge levels are able to avoid this subconscious motivation, and instead seek out the content they expect to be most beneficial to consume. It is also possible that because the graphical condition is quicker and more easy to scan, the educated individuals expressly choose hard news, because they expect to see a good general overview of the days’ available hard news. Text, pictures, and the knowledge gap. While this experiment suggests that the graphical layout makes soft news more appealing for the less educated, and less appealing for the educated, it may, however, be possible for graphical imagery to level the knowledge gap in other ways. Specifically that graphical content may encourage or attract people to, and encourage people to use, news sites in the first place. To understand how, imagine a different Internet existed today – an Internet that was accessible in every American home, yet that still looks as it did prior to the 1990s – a text-only format, similar to the command line interface on every computer. This Internet would be much cheaper to provide, as it would consume relatively little bandwith through the transmission of smaller bits of information. If we accept the hypothetical scenario that the Internet is near-universally accessible in most American homes, the access gap between high and low socio-economic strata no longer presents a challenge. However, the greater challenge may be in expecting equal use of this type of Internet across all socio-economic strata. Even if this Internet had all the same basic
144 information that exists today, the text-only format presents a difficult barrier to entry, particularly as this type of Internet offers relatively little appeal over the television, or even the newspaper. We might imagine that only people who have high education, or who very much like to read, would seek out information in this type of display. The maturation of communication mediums have brought improvements in the presentation of content, coupled with increased advertiser participation. For instance, the quality of radio bandwidth increased from AM to FM radio; television broadcast quality changed from black and white, to Technicolor, and more recently to digital and high-definition formats. There have even been substantial changes in newspaper printing—there was much excitement when The New York Times printed its first color photograph on the front page. Like existing news media, the Internet has progressed through stages of content display and presentation, and these outcomes have brought increased advertiser participation, along with new information displays. Inadvertent exposure online. The Internet we know is very information-rich, with text, graphics, and pictures (along with a number of other digital and social qualities). Simply having information presented in an appealing format may simply encourage all strata of the electorate to seek information online in the first place. We unfortunately know very little about online inadvertent exposure, which (as previously described in the introduction) is defined as the “accidental” process of encountering or exposing oneself to news content as a by-product of some other, typically entertainment-driven, intent. One might argue that visually appealing entertainment content online is what initially attracts people to Web use, and once an individual is there, they have the opportunity for inadvertent exposure to substantive news content. When individuals visit websites online, they by nature create new opportunities for inadvertent news acquisition, potentially through a news article emailed by a friend, or news that is presented directly on a homepage, social network, or within search results. Web information is connected easily with hyperlinks, and brings with it the chance that an individual may want to seek out related content. Some research has already begun to assess the opportunities for inadvertent news exposure online (e.g.,Tewksbury et al, 2001). One of the primary opportunities for inadvertent online news exposure is on an individual’s Web homepage (a homepage is defined as the first
145 Website an individual sees upon opening their browser). Many individuals with Web access have portal sites (e.g., Yahoo, MSN) set as their homepages, frequently because that is the default option from the Internet service provider (ISP). These portal sites provide individuals a number of links and distractions, news content among them. As survey data does not yet suggest that Americans are getting severely less informed with all of this choice online, it merely suggests that researchers need to be a bit more creative in the ways that they measure media exposure. Graphical layout encourages repeated source selections. The graphical treatment also seemed to facilitate an individual repeatedly selecting the same news source. The news organization source brand is made to be prominent in the graphical layout, and this cue may simplify the news selection process for some individuals. No matter whether news sources were clustered or treated as six discrete unites – people seemed more likely to repeat their prior selections when they were in the graphical condition, where perhaps it is easier for news readers to recall what content was previously viewed. The effect of graphical display on prior selection was somewhat stronger for the non-left sources, possible because the left sources have very subdued logos (The New York Times is simply white and black), whereas Fox News, USA Today, and CNN (which was included as a left source) have much more colorful logos and web banners. While he limited number of sources presented to participants in the experiments reported herein makes it slightly difficult to fully extrapolate to an understanding of the variance in real-world new choices, one thing is clear: prominently displaying a graphical brand to newsreaders is likely to further exacerbate the already- perceived partisan news media divide. Graphical layout: costs and potential benefits. The graphical layout seems to offer some potential negative consequences: (1) a higher probability that individuals self-select into specific media silos, and (2) a higher probability that news readers, especially less-educated ones, will select soft news instead of hard. However, from the recall measures in the eyetracking experiment, we show that participants were much more likely to remember the news sources in the graphical condition (the source recall rate for the text condition was very low. As previous research suggests, it is likely that the graphical condition promotes better memory of both source information and news
146 content. An ideal experiment design would have included measures to assess memory and recall of the sources read, and recall measures of news information. Obtaining measures of knowledge gain and retention would help to understand whether the negative effects of graphical layout – more soft news selection and more repeat source selection – can be outweighed by greater information retention and memory. It may be better for individuals to remember at least one hard news story than forget the details of three. Regardless, even assuming the graphical layout does promote information recall, the finding still remains that less-educated individuals are more likely to select soft content in graphical layouts than the highly educated. This in itself may widen knowledge gaps within society.
5.2 UNDERSTANDING INITIAL NEWS SELECTIONS The experiments in this study started with the premise of news consumption— that an individual has put herself in a position to obtain news—and the primary question which then follows is, what type of news is selected. As designed, this experiment articulates what might happen when individuals are confronted with choice on news aggregator websites, yet does not fully explain the factors that may put people in such a position to begin with. The pre-experiment survey contained a number of demographic variables and self-reported preferences for news content. Only self-reported interest in and discussion of current affairs had a significant effect on predicting hard news selection in the first trial, and even then, consistent effects only appeared for individuals older in age. Thus, the baseline measure from which to understand the first experimental news choice is an individual’s estimated level of interest in news. While of course intuitive, it is still somewhat disappointing, as a more fine-grained understanding of individual differences would better help to advance the field of political communication research. Controlling for individual-level differences. As Bennett and Iyengar (2009), Prior (2009) and others address – communication scholars need novel mechanisms with which to measure media effects. Prior calls for a new look at measuring online exposure, particularly towards improving question-wording and survey instrumentation. Bennett and Iyengar call for a deeper understanding of media exposure that accounts for
147 individual-level characteristics – e.g., if choice is increasing, it is quite probable that media exposure is an endogenous characteristic, rather than an exogenous one. Individuals will be more or less motivated to seek out news for unique reasons, and these reasons will affect their levels of media exposure and subsequent political knowledge. A further challenge is that media exposure is likely to be stratified across the electorate, and researchers and other stakeholders cannot guarantee that an individual has actually encountered a particular media message without controlling for other factors (e.g., political interest or social networks). Political communication research must acknowledge that audiences are no longer recipients of a shared message. Communication effects can no longer be measured as if they were constant across all strata of the citizenry; instead, the subtleties of individuals’ news diets, or lack thereof, must be considered. The limited predictive utility of self-reported news behaviors in analyzing news type and news source selections indicates that there are some deeper individual-level mechanisms at work affecting the news choices made by the public. Media scholars likely need to incorporate other personality-based and trait-based measures into surveys and experiment design, and better understand the influence of an individual’s social network and family dynamics. For instance, traits such as open-mindedness, empathy, and judgment may affect whether individuals select sources that are ideologically distinct from their own positions. Other individual-level characteristics that might explain source preferences could address an individual’s decision-making strategy: whether decisions are made rationally, calculated, and proactive, rather than reactive and emotionally-driven. Hard news may be more likely to be selected by people who see the bigger picture, or who have more empathy, rather than individuals who are perhaps more selfish and need to foresee a direct personal benefit to any behavior. Further, a better predictor of political motivation may well be an individual’s personal income, rather than household income, as personal income captures how much tax or investment an individual is contributing towards the overall economy. Social networks and family dynamics are also likely to be significant predictors affecting initial news exposure, particularly as it is well-known that individuals take cues from opinion leaders. As was shown in this study, married males and females
148 behave differently from their unmarried counterparts, and select a source that is ideologically more “male” (right) or “female” (left). It follows that a better understanding the politics of the marital partner, including estimates of a partner’s political interest, and whether the partner’s politics match the respondent would better capture the overall political environment in which an individual lives. Traditional matriarchal or patriarchal attitudes might help to account for females’ engagement (or lack thereof) in politics. Improving Measurement of Media Exposure. As discussed, self-reported measures of news exposure had little effect on hard news or source selections in the experiments reported herein, while self-reported interest and frequency of discussion in politics did have slight explanatory power. As is well-documented, self-reports of actual media use may be inaccurate or simply poor predictors of behavior. Despite this, it seems imprudent to completely give up on measuring news exposure; instead, researchers should attempt to find ways that better approximate actual levels of media use. Live behavioral metrics. The same digital infrastructure that allows news organizations to deliver personalized and targeted articles to audience members simultaneously should allow researchers to gather actual measures of online media exposure. Even primitive cookie-based tracking can be designed to record the total time individuals spend on specific websites and webpages. For example, one could program a Web browser extension and deploy it to an opt-in panel of participants. The cookie or extension can be programmed to record actual web behaviors on a pre-determined white-list of news websites, to avoid parsing through entire reams of an individual’s online activity (the latter of which of course introduces an unnecessary invasion of privacy). Some key metrics could include (1) session length – how long does an individual spend reading news at a given time (measured in time); (2) depth of session – how many stories does an individual click; (3) session diversity – the number of different news sources and Websites accessed in a given session; (4) time on story – how much time is spent reading an individual news story; and (5) point of access – from where stories are read, including mobile devices, via twitter feeds, or desktop computers. Capturing actual aggregate metrics such as this will help to account for
149 individual-level variance (as is done in a mixed effects analysis). Other alternatives involve gathering data from news organizations, or making better use of the public APIs that these companies offer. As described, digital technology and increased access to media challenges how communication effects are fundamentally measured. Audiences are presented with an abundance of choice – including the choice to simply “tune out,” or forego making a decision altogether. This increased choice creates more opportunities for media audiences to become stratified, potentially making aggregate media effects more difficult to observe and produce (Bennett & Iyengar, 2008; Chaffee & Metzger, 2001). In fact, this was already evidenced through the experiment results: there were distinct tiers between those individuals who repeatedly selected hard news from those who repeatedly selected soft; similar divisions existed between those who repeatedly select right, left or neutral sources. Live behavioral metrics would also help to more effectively cluster sources into ideological groups. As noted, a shortcoming of this research is that the left, neutral, and right source clusters contained unequal groups. Individuals’ self-reported ideologies do not always match up as neatly as we might expect with their source preferences, and future analyses could better assess which individual qualities encourage individuals to stray from their reported party line.
5.3 UNDERSTANDING BEHAVIOR IN SUBSEQUENT EXPERIMENT TRIALS Desire for Diversity. Participants’ behavior changed throughout the experiment trials, most notably in the 4th and final trial, where they became more willing to select the opposite news type, or a more diverse source. This may of course be an artifact of the experimental environment, in that individuals naturally become less invested or more bored as the experiment progresses. More likely, however, is that individuals naturally seek some, albeit small, level of diversity in their news diet – especially in the news category that gets consumed. Results suggest that no matter how devout or dedicated a hard news reader, they may be just as likely to finishing their reading session with soft news, providing natural balance to their news diet.
150 Implicit in analyzing news behaviors across the four experiment trials is the assumption that what people do initially—in the first or second trial is a better measure of their true preferences (barring any inflated hard news selections as a result of experiment artifacts and social-desirability). The analysis by trial assumes that people do not delay gratification, and select the thing that most interests them first: if a news consumer really cares about entertainment and celebrities, then that category will be selected first, instead of thinking that the healthy hard news should be consumed before dessert. Future research could better analyze patterns of news consumption and establish whether people exercise immediate or delayed gratification in their news reading behaviors. It is likely that more psychological and personality traits will affect hard news selection, including stamina, gratification mechanisms, and rational versus emotive choice. Source loyalty. Participants in this experiment expressed extreme loyalty to specific sources. If anything, this experiment demonstrated that source loyalty is stronger than self-attributed political labels and partisan dispositions. There were in fact some liberals and moderates who chose to read Fox News, and some conservatives who selected left-leaning sources. Even when individuals selected a source misaligned with their professed ideology, they were on average just as likely to repeatedly select this source as someone whose ideology more closely matches the perceived slant of the news provider. This does suggest that individuals are likely to silo themselves into specific media sources, and when they do, they may be very unlikely to diverge. It may also represent that this present analysis omits unique individual characteristics that could enable us to more accurately predict the selection of ideologically dissimilar sources. One hypothesis for this divergent behavior is that individuals may simply appreciate reading a diversity of opinions. An alternative may be more aligned with the “know thy enemy” argument, in that partisans simply like to know the sentiment and talking points of their political opponents. The effects of source loyalty and preference diminished somewhat in the 4th and final experiment trial, yet this effect was true primarily for Conservatives, as only one source was defined as right-leaning. A more careful selection of sources, and a fully- fledged pre-test assessing the ideological interpretations of each source could certainly
151 improve a future news preferences experiment. The interpretation of results should of course be constrained by the lack of “right-leaning” choice offered to news consumers. As discussed, this experiment starts with the premise that individuals are presented with different news stories from which to select. This experiment naturally begins by giving equal opportunity to six distinct sources. Yet, aside from the world of news aggregators, individuals rarely have such an equal opportunity from which to select diverse sources. Most likely is that individuals have bookmarked their favorite news site, or set a news site as their web homepage, making the cost of selecting the preferred source extremely low (it is nearly automatic), and the cost of selecting a different or new source extremely high, such as typing in a distinct url and navigating to a less familiar page. The results in this study suggest that the real-world corollary to such strong source loyalty may naturally mean that real-world behavior is one of bookmarking, homepages, and nearly subconscious or automatic navigation to specific news source.
5.4 EFFECTS OF POLITICAL POLARIZATION It was previously expected that political polarization would affect both news category and source selections. Specifically, that individuals with higher levels of polarization would be more likely to seek out hard news, and more likely to seek out an ideologically-similar source. Stronger affective polarization led males to make more hard news selections, but had no effect for females. Meaning, the least polarized males were also the study participants least likely to select hard news, and the most polarized males were the study participants most likely to select hard news. Polarization levels did not affect females’ preferences for selecting hard or soft news, suggesting that by nature, when males are interested in politics, they may develop strong opinions about their own and the opposite party. Conversely, when females are interested in politics, they do not develop such strong attitude. This finding is best understood in light of existing research about gender and politics – namely that males have stronger political opinions, more interest in politics, and higher levels of participation. Yet, very little research assesses the within-gender political engagement gap: this experiment provides some evidence that males’ opinion strength about politics and political parties is a
152 strong predictor of their actual news selection behaviors, yet this is not true for females – females news selection behaviors do not follow a linear relationship with polarization. While polarized attitudes may be a predominantly masculine phenomena, polarized behaviors were common for both genders, though strongest for males. Both males and females repeatedly selected the same news sources and news types throughout the experiment, with males exercising slightly stronger preferences for their past behavior than did females. There were also limited interaction effects between political party and polarization on source selections. It appeared that the polarized Democrats were the group least likely to select the right-leaning news choice provided (Fox News), though this affected nothing else about source selection. Personalization. News organizations can also leverage the Web infrastructure to collect specific metrics about their audience characteristics and behaviors. This information may then used to infer audience tastes and interests, and is subsequently reflected in personalized news for individual audience members. Currently, news aggregators can track and record which sources an individual selects, and algorithms can be deployed that actively promote or demote specific sources based on an individual’s past behavior. One could also imagine that a news organization uses natural language processing (NLP) to process the reader comments listed at the bottom of a news article. News organizations could intentionally promote, demote, or highlight specific comments they expect to resonate with their key target audience of newsreaders. While very futuristic-sounding, news organizations could alter news story text at the moment a link is selected by inserting a partisan spin or commentary they expect to resonate with the ideological view of the news reader. Political polarization and media silos will only increase with news personalization, particularly if the news organizations’ personalization mechanism is designed to facilitate existing behaviors.
153 5.5 AFTERWARD Historically, the advent of a new media technology has generated anxious forecasts about how the increased choice and delivery will change patterns of media consumption. Scholars have worried that news media and current affairs content will be subordinated by many more entertaining choices available. Cable television was the first media to clearly show how increased choice was coupled with a decline in news consumption. Digital media – including blogs, online news sites, smart-phones, and social networks – continue to alter the consumption, distribution, and production of news content. Individuals have the ability to immediately access breaking news, share this information with anyone of their choosing, and also publicly post opinions, news analyses, or other content of their own. Conversely, individuals can choose to avoid current affairs and only tune into entertainment.
Changing News Media Environment. Audience exposure to, and interaction with, news is also changing within the Internet era. There now exists a feedback mechanism between audiences and news producers, whereby newsreaders can comment directly on an article, or immediately share a story with others in their social network. The current methods and technology used to measure news preferences and behaviors are far behind what is possible. Political communication research is still predominantly based in surveys and experiments, which are of course valuable, yet do little to take advantage of the unique digital infrastructure that distinguishes online media consumption. News consumption is no longer defined as or limited to a nightly thirty-minute news broadcast, or the morning perusal of the daily newspaper. Americans are increasingly online throughout their day, at work and at home. As workers spend more time on computers and using Web browsers, news is just a click away. Pew classifies 51% of Americans as news grazers, meaning they check in on the news from time to time throughout the day, rather than get the news at a regular allotted time interval (2008). News content is more easily shared with other people via email or by seeing news content appear in news feeds and social networking sites. The fragmented and almost distracted manner in which individuals can consume online news indicates that
154 news may more frequently be taken out of its original context of a broadcast, or out of a branded newspaper. Researchers should attempt to understand whether ad hoc consumption of individual stories weakens any adverse effects of political polarization and media selectivity. Furthermore, the manner in which news is consumed online is drastically different from existing forms of news media. No longer is news media represented as a whole—an entire broadcast, radio show, or newspaper. Instead, news media may continue to be consumed on an ad-hoc basis, with a single story acquired from one webpage, another item emailed from a colleague, and another encountered by chance while shopping online. Media scholars have cautioned that these very qualities of digital media, coupled with changes in social dynamics, may falsely signify a return to minimal effects unless the methodological challenges in accurately measuring media exposure and effects are properly addressed. Political communication research needs to ensure theories of media effects are flexible enough to withstand the changing environment—or develop such (new) ones. Given that selection heuristics will uniquely determine the news items each individual sees, media exposure can no longer be assumed a constant as it once was. The same story may be heard by audiences via a number of different channels, all with their own subtleties of reporting, and some laden with framing effects or partisan bias. A correct understanding of media effects will ultimately demand a greater understanding of how individual-level characteristics moderates news exposure. In turn, by assessing exposure, researchers can then begin to understand why, or how, media may have such varied effects across the citizenry. While this combination of factors presents a complex future for studying media effects, there are certain parallels that can be drawn to other eras of mass media technology change. We can look to the introduction of cable television to learn about how choice affects political knowledge gain, or to the era of pamphleteers to understand the empowerment of citizen journalism. The increased choice and competition on the Internet may echo historical lessons of mass media. Yet, due in part to its digital infrastructure, the Internet still provides new opportunities for news organizations, news audiences, and the future of communication research.
155
156 APPENDIX A: SURVEY USED IN ONLINE PANEL STUDY
1. How frequently do you discuss politics with your family or friends? a. Almost every day b. Frequently c. Occasionally d. Almost never
2. – 5. Which of the following sources do you use most frequently to get your news about ______? Insert 4 topics: [POLITICS], [SPORTS], [ENTERTAINMENT], [HEALTH CARE] a. Television b. Newspapers c. Radio d. Magazines e. Internet f. Other (volunteered) g. None
6. Which Internet search engine are you most likely to use? a. Yahoo! b. Google c. Bing d. Other e. None
7. How frequently do you visit the following websites? [Daily, A few times a week, A few times a month, Rarely, Never] a. Drudge Report b. New York Times Online c. Huffington Post d. Yahoo News e. Google News f. BBC g. CNN Online h. Fox News Online i. Ebay j. Facebook k. YouTube l. ESPN m. WebMD n. Match.com
8. How many days in the PAST WEEK did you use an Internet search engine? ___
9. How many days in the PAST WEEK did you watch ____? [0-1, 2-3, 4-5, 6-7] a. Your LOCAL TV news? b. NATIONAL network TV news? c. a TV soap opera? (e.g., General Hospital, Days of Our Lives)
157 d. a TV sports show? (e.g., SportsCenter on ESPN) e. The O’Reilly Factor (with Bill O’Reilly) f. The Daily Show with Jon Stewart g. Countdown with Keith Olbermann j. Morning news shows (e.g., Good Morning America, Today Show)
10. Would you say the news coverage provided by ______is objective or biased in favor of a particular political perspective [INSERT: Fox News, CBS News, The Daily Show, Countdown with Keith Olbermann, The O’Reilly Factor] a. Completely Objective b. Generally Objective c. Generally Biased d. Completely Biased e. Unfamiliar with program
11. How much do you enjoy watching the following television programs? [Very much, Quite a lot, A little, Not at all, Unfamiliar with the program] a. 60 Minutes (CBS) b. Sunday news interviews (e.g., Meet the Press) c. NCAA Basketball (CBS) d. 24 e. House f. Desperate Housewives g. The Iron Chef h. The Pacific i. American Idol
12. Do you generally watch television alone, or with family and friends? a. Generally watch alone b. Generally alone, but occasionally with others c. Generally with others, but occasionally alone d. Generally with others e Don’t watch television at all
13. How much confidence do you have in the accuracy and fairness of the following news organization’s coverage of issues and events? [A great deal, A lot, Some, A little, None at all, Unfamiliar with program] a. Drudge Report b. CNN c. Fox News d. MSNBC e. CBS f. Huffington Post g. National Public Radio h. The New York Times i. The Wall Street Journal
14. We'd like to get your feelings about various people and organizations in the news these days. Using the “feeling thermometer,” ratings between 51 and 100 degrees mean that you feel
158 favorable and warm towards the person, and ratings between 0 and 49 degrees mean that you feel unfavorable and cold towards the person. If you have no feelings one way or the other, rate the person at 50.
a. People who support the Democratic Party b. Mitt Romney c. Sarah Palin d. The “tea party” movement e. Barack Obama f. People who support the Republican Party g. Nancy Pelosi
15. Some people have opinions about almost everything; other people have opinions about just some things; and still other people have very few opinions. What about you? Would you say you have opinions about almost everything, many things, some things, very few things, can’t say
16. Compared to the average person, do you have fewer opinions about whether things are good or bad, about the same number of opinions, or more opinions?
17. Some people say that it is important to have definite opinions about lots of things, while other people prefer to remain neutral on most issues. What about you? Do you think it is better to: Have definite opinions about lots of things; Remain neutral on most issues; Can’t say
18. How well do the following terms apply to Democrats? [A great deal A lot Some A little Not at all] a. Patriotic b. Closed minded c. Knowledgeable d. Compassionate e. Selfish
18. How well do the following terms apply to Republicans? [A great deal A lot Some A little Not at all] a. Patriotic b. Closed minded c. Knowledgeable d. Compassionate e. Selfish
Polimetrix Core Profile Items
1. Are you male or female? [Male, Female]
2. What racial or ethnic group best describes you? [White, black, Hispanic/Latino, Asian, Native American, Middle Eastern, Mixed Race, Other]
159 3. In what year were you born? ____
4. What is the highest level of education you have completed? [No high school degree; High school graduate; Some college, but no degree (yet); 2-year college degree; 4-year college degree; Postgraduate degree]
5. What is your marital status? [Married; Separated; Divorced; Widowed; Single, never married; Domestic partnership]
6. Are you registered to vote? [Yes; No; Don't know]
7. Generally speaking, do you think of yourself as a... ? [Strong Democrat, Weak Democrat, Lean Democrat, Independent, Lean Republican, Weak Republican, Strong Republican, Not sure]
8. Thinking about politics these days, how would you describe your own political viewpoint? [Very liberal; Liberal; Moderate; Conservative; Very conservative; Not sure]
9. How often do you attend formal religious services? [Once a week or more; A few times a month; Less than once a month; Almost never or never; Not sure]
10. What is your present religion, if any? [Protestant; Roman Catholic; Mormon; Eastern or Greek Orthodox; Jewish; Muslim; Buddhist; Hindu; Atheist; Agnostic; Nothing in particular; Something else]
11. Thinking back over the last year, what was your family's annual income? [Less than $10,000; $10,000 - $14,999; $15,000 - $19,999; $20,000 - $24,999 $25,000 - $29,999; $30,000 - $39,999; $40,000 - $49,999; $50,000 - $59,999 $60,000 - $69,999; $70,000 - $79,999; $80,000 - $99,999; $100,000 - $119,999 $120,000 - $149,999; $150,000 or more; Prefer not to say]
12. What is your state of residence ____
13. Some people seem to follow what's going on in government and public affairs most of the time, whether there's an election going on or not. Others aren't that interested. Would you say you follow what's going on in government and public affairs ... ? [Most of the time Some of the time Only now and then Hardly at all Don't know]
160 APPENDIX B: SURVEY USED IN EYETRACKING STUDY
Frequency of news consumption: Responses: 6-7 times a week, 3-5 times a week, Once or twice a week, Once or twice a month, Hardly ever How frequently do you read or watch news about US politics? How frequently do you read or watch sports news? How frequently do you read or watch world news? How frequently do you read or watch news about entertainment and celebrities? How frequently do you read or watch news about the environment? How frequently do you read opinion pieces or editorials?
Exposure to sources: Responses: Daily, A few times a week, A few times a month, Rarely, Never How frequently do you visit the Huffington Post? How frequently do you visit The New York Times online? How frequently do you visit Google News? How frequently do you visit Fox News online? How frequently do you visit CNN online? How frequently do you visit the BBC news website? How frequently do you visit Facebook? How frequently do you listen to NPR (National Public Radio
Demographics What is your gender? In what year were you born? Are you registered to vote in the United States? Generally speaking, do you think of yourself as a…. Strong Democrat, Not very strong Democrat, Lean Democrat, Independent, Lean Republican, Not very strong Republican, Strong Republican Would you say your follow what’s going on in government and public affairs… Most of the time, Some of the time, Only now and then, Hardly at all, Don’t know
161 Recall Please list the news sources you can recall when the news was presented in a text- only format. Please list the news sources you can recall when the news was presented in the graphical format.
162 APPENDIX C: NEWS STORY HEADLINES Section Headline Business Jobless claims in US increased heading into Easter Business Greenspan defends decisions before panel investigating crisis Business Whitacre profit goal in reach after $1 billion cash generation Business Airline stocks climb, with US airways taking the lead Business Forex-Euro edges higher on Trichet comments; yen gains Business China appears set to make currency more flexible Entertainment Why Sandra Bullock broker her silence over sex tape Entertainment Oprah announces new TV show starring Oprah Entertainment Justin Bieber tweets about people magazine cover Entertainment Whitney Houston says drug use reports 'ridiculous' Entertainment Ill wind blows in HBO New Orleans series 'Treme' Entertainment Kate Gosselin to star in TLC's new 'Twist of Kate' Environment How to connect mining disasters and climate change Environment Climate talks could pick up from failed summit Environment Weathercasters and climate change Environment Performers line up for Earth Days' 40th anniversary Environment US to abstain on South African coal plant Environment Public supports energy over environment: poll Life & Style Lindsay Lohan releases more of her fashion line Life & Style For designer bargains, step into their closet Life & Style So bad it's good: dancing with the stars 2010 fashion trends Life & Style Kim Jong Il 'becomes global fashion trendsetter!' Life & Style Believe it: Pregnancy can 'bump' up your style Life & Style Adam Laber talks fashion, Glam and being on American Idol Opinion The Wall Street reform fight we really need Opinion Opinion: Tiger's back, now can we get on with our lives Opinion Opinion: 'Net neutrality' ruling will refocus debate Opinion Who attends Tea Party? Conservative, Southerners Opinion GUEST OPINION: Truth and transparency have left the House Opinion OPINION: You have the right to an (adequate) attorney Sports Frozen four preview: Five questions Sports Turkoglu latest Raptor to suffer nasal fracture; Bosh likely done Sports Phoenix Coyotes (49-25-6) at Los Angeles Kings (45-27-7), 10:30pm Sports SOLOMON: Woods inspires reverence, awe Sports Good start for Watson as Masters begins Sports Lackey makes strong impression in debut US Politics Republicans ponder 2010, and 2010, in New Orleans US Politics Senate proceeds with Liu confirmation hearing US Politics Obama may face election-year Supreme Court battle US Politics Hurdles could delay Senate action on START US Politics Fundraising debate keeps Steele in the headlines US Politics Tea Party ads target Stupak for 'betrayal' on health-care vote World Turkey, Greece agree to enhance military ties World Taliban kidnap gang chief released early World Ousted Kyrgyzstan leader refuses to admit defeat World Asian leaders urged to pressure Burma at Asean summit World Sri Lanka holds polls to elect new parliament World Sudan's vice-president Salva Kiir boycotts elections
163 164 NUMBER OF SELECTIONS TO STORY HEADLINES
Text Graphic Total Story Headline Total Total Clicks No selection 171 218 389 Jobless claims in US increased heading into Easter 68 60 128 Greenspan defends decisions before panel investigating crisis 32 37 69 Whitacre profit goal in reach after $1 billion cash generation 14 17 31 Airline stocks climb, with US airways taking the lead 9 19 28 Forex-Euro edges higher on Trichet comments; yen gains 10 14 24 China appears set to make currency more flexible 22 28 50 Why Sandra Bullock broke her silence over sex tape 84 75 159 Oprah announces new TV show starring Oprah 27 38 65 Justin Bieber tweets about people magazine cover 7 9 16 Whitney Houston says drug use reports 'ridiculous' 21 32 53 Ill wind blows in HBO New Orleans series 'Treme' 36 17 53 Kate Gosselin to star in TLC's new 'Twist of Kate' 17 26 43 How to connect mining disasters and climate change 19 25 44 Climate talks could pick up from failed summit 27 22 49 Weathercasters and climate change 33 26 59 Performers line up for Earth Days' 40th anniversary 11 23 34 US to abstain on South African coal plant 2 7 9 Public supports energy over environment: poll 58 51 109 Lindsay Lohan releases more of her fashion line 14 16 30 For designer bargains, step into their closet 46 37 83 So bad it's good: dancing with the stars 2010 fashion trends 26 26 52 Kim Jong Il 'becomes global fashion trendsetter!' 14 12 26 Believe it: Pregnancy can 'bump' up your style 22 21 43 Adam Laber talks fashion, Glam and being on American Idol 24 18 42 The Wall Street reform fight we really need 26 27 53 Opinion: Tiger's back, now can we get on with our lives 22 22 44 Opinion: 'Net neutrality' ruling will refocus debate 18 16 34 Who attends Tea Party? Conservative, Southerners 41 55 96 GUEST OPINION: Truth and transparency have left the 42 65 107 House OPINION: You have the right to an (adequate) attorney 11 29 40 Frozen four preview: Five questions 23 20 43 Turkoglu latest Raptor to suffer nasal fracture; Bosh likely done 31 17 48 Phoenix Coyotes (49-25-6) at Los Angeles Kings (45-27-7), 14 11 25 10:30pm SOLOMON: Woods inspires reverence, awe 35 30 65 Good start for Watson as Masters begins 51 40 91 Lackey makes strong impression in debut 47 44 91 Republicans ponder 2010, and 2010, in New Orleans 131 105 236 Senate proceeds with Liu confirmation hearing 55 49 104 Obama may face election-year Supreme Court battle 195 160 355 Hurdles could delay Senate action on START 39 44 83 Fundraising debate keeps Steele in the headlines 30 40 70 Tea Party ads target Stupak for 'betrayal' on health-care vote 121 109 230 Turkey, Greece agree to enhance military ties 62 51 113 Taliban kidnap gang chief released early 85 73 158
165 Ousted Kyrgyzstan leader refuses to admit defeat 57 54 111 Asian leaders urged to pressure Burma at Asean summit 19 23 42 Sri Lanka holds polls to elect new parliament 16 18 34 Sudan's vice-president Salva Kiir boycotts elections 15 24 39 Totals 2,000 2,000 4,000
166 APPENDIX D: SAMPLE STIMULUS MATERIALS
TEXT SOURCE TEMPLATES
GRAPHICAL SOURCE TEMPLATES
167
TEXT STORY TEMPLATES: BUSINESS SECTION
168
GRAPHICAL STORY TEMPLATES: BUSINESS SECTION
169
TEXT STORY TEMPLATES: ENTERTAINMENT SECTION
170
GRAPHICAL STORY TEMPLATES: ENTERTAINMENT SECTION
171
TEXT STORY TEMPLATES: ENVIRONMENT SECTION
GRAPHICAL STORY TEMPLATES: ENVIRONMENT SECTION
172
TEXT STORY TEMPLATES: LIFE & STYLE SECTION
173
GRAPHICAL STORY TEMPLATES: LIFE & STYLE SECTION
174
TEXT STORY TEMPLATES: SPORTS SECTION
175
GRAPHICAL STORY TEMPLATES: SPORTS SECTION
176
TEXT STORY TEMPLATES: US POLITICS SECTION
GRAPHICAL STORY TEMPLATES: US POLITICS SECTION
177
TEXT STORY TEMPLATES: WORLD SECTION
178
GRAPHICAL STORY TEMPLATES: WORLD SECTION
179
180 APPENDIX E: LOGISTIC REGRESSIONS FOR LEFT AND RIGHT NEWS SOURCES
Generalized Linear Mixed Models It is possible that one may raise objections to clustering sources in an ordered fashion, from left – neutral – right. To avoid any potential biases produced by such an ordering, two binary logistic regressions were computed, assessing the selection of either a right leaning source, or a left leaning source. Two binary variables were constructed – one denoting the selection of a right- leaning source (1 = right-leaning source was selected, 0 = else), and the other denoting the selection of a left-leaning source (1= left source was selected, 0 else). These dependent variables were used in two generalized linear mixed models (Bates, Maechler, & Bolker, 2010) to assess which factors influence the selection of a right- or left-leaning news source. Predictor variables were the same as described previously in Chapter 3: gender, news type (hard or soft), polarization, party id, visual treatment, trial, previous source selection, and total news interest. As participants engaged in multiple (four) trials, it is appropriate to construct a mixed model, accounting for the participant level variance; as such, participant was included as a random factor.
RESULTS: RIGHT-LEANING SOURCE SELECTION
Trial 1: Selection of right-leaning news source
To first obtain a baseline from which to assess trials 2-4, a model was generated to predict the selection of a right-leaning source in the first trial; results are presented below in table E.1. Three logistic regressions were computed: Model 1 accounted for party identification, Model 2 accounted for political ideology, and Model 3 included both political measures. In all three models, the selection of a right-leaning source in the first trial is significantly and positively associated with a hard news category (Model 1: β = 0.80, Z = 3.29, p <0.01; Model 2: β = 0.78, Z = 3.24, p <0.01; Model 3: β = 0.78, Z = 3.20, p <0.01), a 122% increase in odds above soft news. Model 2, which only included ideology, being conservative had a significant positive affect (β = 0.41, Z
181 = 1.87, p =0.06), a 50% increase in odds above the moderate baseline. In both Models 1 and 3, which accounted for party identification, being Democrat was significantly negatively associated with selecting a right-leaning news source (Model 1: β = -0.60, Z = -2.15, p <0.05; Model 3: β = -0.67, Z = -2.18, p <0.05), a 151% decease in odds below moderates.
Table E.1: Selection of Right-leaning News Source in Trial 1 Logistic Regression Results Variable Model 1: Model 2: Model 3: Party ID Ideology Both (Intercept) -2.290* -2.684 -2.401 * (0.510) (0.507) (0.521) Female 0.172 0.187 0.191 (0.184) (0.184) (0.185) Polarization 0.094 -0.097 0.031 (0.293) (0.285) (0.297) Graphical 0.032 0.025 0.039 treatment (0.182) (0.181) (0.182) Hard news 0.796* 0.784* 0.776 * (0.242) (0.242) (0.242) Republican -0.160 -0.230 (0.272) (0.300) Democrat -0.597* -0.672 * (0.277) (0.308) Conservative 0.410 + 0.246 (0.219) (0.268) Liberal 0.113 0.334 (0.250) (0.280) N 890 890 890 AIC 798.130 801.047 800.190 BIC 932.284 935.201 972.674 Log Likelihood -371.065 -372.523 -364.095
As hypothesized, political orientation significantly affects selection of a right- leaning news source in the first trial, as is shown with the negative coefficient for being Democrat, and the positive coefficients for being conservative. It also seems the behavior of Independents in this sample is more comparable to what we might expect of
182 Republicans than Democrats. As this model provides a baseline measure of right- leaning source selection, we then turn to models predicting choice in trials 2-4 to see which effects carry throughout the rest of the experiment.
Trials 2-4: Selection of right-leaning news source
Main effects. A main-effects only model was produced, assessing the selection of a right-leaning news source across the remaining three trials. Again, three models were computed, which included (i) political ideology, (ii) political party identification, and (iii) both factors. The fits and coefficients for all other variables are comparable between the three models. By assessing main effects only, the only variables significantly affecting the selection of a right-leaning news source include: being conservative (Model 2: β = 0.28, Z = 2.17, p <0.05; Model 3: β = 0.30, Z = 1.85, p =0.06), and the previous choice (Model 1: β = 1.27, Z = 10.89; Model 2: β = 1.25, Z = 10.79, p <0.0001; p <0.0001; Model 3: β = 1.25, Z = 10.79, p -<0.0001). Note that political party affiliation (e.g. the negative relationship of being Democrat) is no longer significant, and in all three models, the influence of lagged choice leads to a 256% increase in odds over selecting a right-leaning news source.
Interaction terms. The models accounting for only main effects hint at being limited in their explanation of the behavior choices in the experiment. The lagged (previous) choice accounts for much explanatory power towards the selection of the right-leaning news source, and it is likely that this lagged choice will interact with other experimental and individual characteristics, revealing more nuanced behavioral processes behind source selections. Interactions were included based on hypothesized and previously-alluded to relationships: specifically, (a) the influence of previous source selection may diminish over time (as seen through Chi-square tests of independence on adjacent trials), (b) polarization may interact with political identification, encouraging (or discouraging) polarized individuals to select news that agrees (or disagrees) with their positions, and (c) the graphical treatment may discourage the selection of hard news, as was found to be the case in the models presented in Chapter 4. Based on this, the interactions
183 introduced were between (i) political party and polarization, (ii) previous choice and trial number, and (iii) news type and treatment. Results revealed that all hypothesized interactions were significant. Table E.2 reports results of all three models, including political ideology (Model 1), political party identification (Model 2), and both party id and ideology (Model 3). Only Model 3 results are reported in the text, as the significant effects and coefficients are comparable. (Note that for non-significant effects, there is evidence of collinearity in Model 3, as there is quite a high correlation between the ideology and partisanship values). There was a significant positive relationship between being conservative and selecting a right-leaning news source (β = 0.36, Z = 1.84, p =0.07), as well as being polarized and selecting a right-leaning news source (β = 0.93, Z = 1.90, p =0.06). Simply being polarized increases the odds of selecting a right-leaning news source 154%. These results confirm initial hypothesizes about ideological agreement in media preferences. There were also significant positive effects of the graphical treatment (β = 0.57, Z = 2.34, p <0.05), hard news (β = 0.55, Z = 2.63, p <0.01), the previously selected source (β = 1.80, Z = 8.17, p <0.0001), and the last (4th) trial (β = .495, Z = 3.03, p <0.01). These results indicate that the right-leaning news source was more frequently selected (i) in the graphical treatment condition, (ii) for hard news topics, (iii) during later experiment trials, and (iv) as a consequence of the previous choice. In fact, simply having previously selected a right-leaning news source, the odds of doing so again are increased by over 500%. This is an incredibly large effect, and is again evidence that news consumers may repeatedly select news providers with whom they are already very comfortable and familiar.
184 Table E.2: Selection of Right-leaning Source in Trial 2-4 Mixed Model Logistic Regression Results Estimate Model 1: Model 2: Model 3: Both Ideology Partisanship (Intercept) -2.855 -3.008 -3.154 (0.334) (0.395) (0.403) Female -0.016 -0.037 -0.014 (0.127) (0.127) (0.128) Polarization 0.256 0.966 * 0.932 + (0.203) (0.486) (0.490) Conservative 0.338 * __ 0.364 + (0.154) (0.198) Liberal 0.229 __ 0.230 (0.169) (0.185) Republican __ 0.379 0.246 (0.366) (0.375) Democrat __ 0.557 0.570 (0.354) (0.360) Graphical Treatment 0.593 * 0.575 * 0.577 * (0.247) (0.246) (0.247) Previous choice 1.808 ** 1.812 ** 1.803 ** (0.220) (0.220) (0.221) Trial 3 0.212 0.214 0.212 (0.166) (0.166) (0.166) Trial 4 0.493 ** 0.496 ** 0.495 ** (0.163) (0.163) (0.163) Hard News 0.561 ** 0.550 ** 0.551 ** (0.209) (0.210) (0.210) Previous Choice * Trial 3 -1.211** -1.217 *** -1.216 ** (0.306) (0.306) (0.307) Previous Choice * Trial 4 -1.418 ** -1.429 *** -1.432 ** (0.313) (0.313) (0.314) Treatment * Hard News -0.739 ** -0.741 ** -0.744 ** (0.270) (0.277) (0.277) Polarization * Republican __ -0.583 -0.623 (0.581) (0.587) Polarization * Democrat __ -1.015 + -1.052 + (0.582) (0.585) N 2456 observations, 906 subjects AIC 2309 2314 2313 BIC 2390 2407 2418 Log Likelihood -1140 -1141 -1139 Random effects Std. Dev 0.841 0.840 0.841
185 As hypothesized, polarization also had a negative effect when interacted with party: being a polarized Democrat had a significant negative relationship on the selection of a right-leaning news source (β = -1.05, Z = -1.80, p =0.07). A partial effects plot of this interaction can been seen in Figure 3.3. Further, there is also a negative effect when interacting trial with the previously selected source (Trial 3: β = - 1.22, Z = -3.96, p <0.0001; Trial 4: β = -1.43, Z = -4.56, p <0.0001). This indicates that the influence of previous choice diminishes over time, meaning that those who select Fox early in the experiment are less likely to do so later, and that individuals who did not select Fox early in the experiment are more likely to select Fox News later on. This can be seen in the partial effects interaction plot in Figure 3.4. Finally, as is supported by results in Chapter 2, there is a significant negative interaction between the graphical treatment condition and hard news on selecting a right-leaning source (β = -0.74, Z = - 2.78, p <0.01), indicating that indeed, when news is presented in a graphical format, a right-leaning news source is more likely to be selected for soft content.
Figure E.1. Interaction between polarization, party id, and source on selecting a rightward source. Y-axis is probability of selecting a right source; x axis is polarization (0=low, 0=high). Polarized Republicans and Independents are more likely to select a right-leaning news source; Democrats less so.
186 Selection of Right-Leaning Source: Interaction between Trial and Previous Choice 0.30 0.25 0.20
PrevChoice Fox 0.15 Selection of Right-leaning source of Right-leaning Selection
PrevChoice Not Fox 0.10 Previous Source Selection Source Previous
2 3 4
Trial Number
Figure E.2. Interaction between trial order, previous choice, and source This interaction plot represents the diminishing influence of the previous source selection through the experiment. Specifically, individuals selecting a right-leaning news source in trial 1 are highly likely to do so again in trial 2; yet, this diminishes for trial 3, and especially for trial 4. Similarly, those individuals who do not select a right-leaning news source in earlier trials are more likely to do so later in trial 4.
187 RESULTS: LEFT-LEANING SOURCES
TRIAL 1: SELECTION OF LEFT-LEANING NEWS SOURCE As was done with the right-leaning news source, initial models predict the baseline selection of a left-leaning source in trial 1. Results are shown in table E.3. This time, four models were computed: party identification (M1), political ideology (M2), both variables (M3), and a comparison model to M3 which excludes CNN, under the basis that CNN may be perceived as having less of a left skew than the other two sources. Significant factors that affected the selection of a left-leaning news source in the first trial included party (Democrat), and the interaction between education and Democrat. There was a marginally significant effect of education on Models 1 and 3 (B=-0.20, Z=-1.66, p =0.097), though education was not significant in either Model 2 (using ideology) or Model 4 (which discounted CNN as a left-leaning news source). Democrats have a mean higher level of education – more Democrats received
4-year college and/ or post-graduate degrees – than Republicans (t = -2.10(846.97), p
=0.04) and Independents (t = 2.23(249.16), p = 0.03) (Dem mean = 3.47, Rep mean = 3.26, Indpt mean = 3.16). Based on the education and party difference, as well as that The New York Times and Huffington Post have large Internet followers, we may expect that education levels to cause within-Democrat differences between those who select these identified left-leaning sources, which in fact, we see to be the case. Whereas on average Democrats may be no more likely to select a left-leaning source, the highly educated Democrats are more likely to select one of these sources.
Democrat 150 Republican Independent 100
50
0 No high High School Some college 2 Yr College 4 Yr College Post- school graduate
Figure E.3: Relationship between education and political party. Y-axis represents the total N. Table E.3 Trial 1: Selection of left-leaning source
188 Model 1: Model 2: Model 3: Model 4: Party ID Ideology Both Both, no CNN (Intercept) 0.80 0.25 0.88 0.15 (0.54) (0.39) (0.54) (0.57) Female 0.02 0.01 0.00 -0.10 (0.14) (0.14) (0.14) (0.15) Polarization -0.16 -0.12 -0.13 0.14 (0.22) (0.21) (0.22) (0.24) Education -0.20 + 0.01 -0.20 + -0.19 (0.12) (0.05) (0.12) (0.13) Republican -0.54 -0.45 -0.58 (0.50) (0.52) (0.55) Democrat -1.03 * -1.04 * -1.08 * (0.50) (0.50) (0.53) Graphical treatment -0.04 -0.06 -0.04 -0.05 (0.14) (0.13) (0.14) (0.15) Hard News -0.19 -0.18 -0.19 -0.08 (0.15) (0.15) (0.15) (0.16) Education * 0.16 0.16 0.13 Republican (0.14) (0.14) (0.15) Education * 0.32 * 0.33 * 0.31 * Democrat (0.14) (0.14) (0.15) Liberal -0.11 -0.14 -0.15 (0.18) (0.20) (0.21) Conservative -0.19 -0.21 -0.16 (0.16) (0.20) (0.22) N 890 890 890 890 AIC 1241.84 1242.74 1244.44 1112.28 BIC 1433.49 1396.06 1474.42 1342.26 log L -580.92 -589.37 -574.22 -508.14
189
Figure E.4: Relationship between education and political party. Y-axis represents the total N.
TRIALS 2-4: SELECTION OF LEFT-LEANING NEWS SOURCE
Main effects. Generalized linear mixed models were computed which account for the selection of a left-leaning news source across the remaining three trials. The fits and coefficients between all three models are comparable. With a main-effects only model, only one factor has a significant positive effect -- previous choice (Model 1: β = 0.785, Z = 9.49, p <0.001; Model 2: β = 0.785, Z = 9.48, p <0.001; Model 3: β = 0.784, Z = 9.48, p <0.001), indicating a 119% increase in odds of selecting a leftward source if it was previously selected. Recall that we were unable to identify which characteristics make an individual most likely to select a left-leaning news source in the first trial; yet, the prior selection of a left-leaning news source is highly significant in predicting whether or not a left-leaning source will be selected again. It stands to reason that a more nuanced model should be able to assess how this variable interacts with other individual and experimental factors.
Interaction terms. The main-effects-only model predicting left-leaning source selections is limited in explanatory power, particularly as we have limited evidence with which to predict which individuals are more likely to select a left-leaning source in the first trial. As with the right-leaning source selections, the lagged (previous) choice accounts for much explanatory power towards the selection of the right-leaning news
190 source, and it is likely that this lagged choice will interact with other experimental and individual characteristics, revealing more nuanced processes related to behavioral source selections. As such, additional models were computed, accounting for potential mediating relationships between variables, specifically that: (a) the influence of previous choice may diminish over time, (b) the graphical treatment may strengthen or diminish the influence of previous choice (as was previously shown with Chi-square tests of independence on adjacent trials), and (c) a particular political party or ideology may be more strongly influenced by a previous selection to a left-leaning source. Based on this, the interactions included were between: (i) previous choice and political party, (ii) previous choice and political ideology, (iii) previous choice and visual treatment, and (iv) previous choice and trial. Results of these full models are in Table E.4. Indeed, by including the interaction terms, a more comprehensive explanation of participants’ behavior emerged. While again, three models were constructed and are presented for reference in table E.4, only results of Model 3 (including both ideology and party identification) are discussed in the text. By accounting for interaction terms, being conservative emerged as a marginally significant negative predictor of selecting a left-leaning news source (β = -0.32, Z = -1.79, p =0.07). There was also a significant negative relationship between the graphical treatment condition and selecting a left- leaning news source (β = -0.25, Z = -2.15, p <0.05). (Contrast this with the positive effect that graphical display had on selecting a right-leaning news source.) Trial 4 also emerged as a significant positive predictor of selecting a left-leaning news source (β = 0.38, Z = 2.63, p <0.01), indicating these sources may be more frequently selected later.
191 Table E.4. Selection of a left-leaning source: Including Interactions Model 1: Model 2: Model 3: Partisanship Ideology Both (Intercept) -0.132 -0.124 -0.017 (0.285) (0.272) (0.291) Female 0.059 0.060 0.061 (0.084) (0.084) (0.085) Hard News -0.100 -0.109 -0.112 (0.094) (0.094) (0.094) Polarization -0.142 -0.123 -0.148 (0.134) (0.131) (0.137) Republican -0.045 -- 0.121 (0.182) (0.207) Democrat -0.282 -- -0.263 (0.180) (0.194) Conservative -- -0.116 -0.319 + (0.136) (0.179) Liberal -- -0.320 * -0.192 (0.152) (0.165) Previous Choice -0.027 -0.603 -0.162 (0.343) (0.414) (0.352) Graphical treatment -0.245* -0.261 * -0.249 * (0.115) (0.115) (0.116) Trial 3 0.208 0.203 0.206 (0.139) (0.139) (0.139) Trial 4 0.379 ** 0.372 ** 0.378 ** (0.143) (0.143) (0.144) Republican * Previous choice 0.224 -- -0.033 (0.252) (0.287) Democrat * Previous Choice 0.636 * -- 0.526 + (0.250) (0.273) Conservative * Previous -- 0.274 0.544 * Choice (0.193) (0.251) Liberal * Previous Choice -- 0.716 *** 0.500 * (0.216) (0.236) Graphical Treatment * 0.420 * 0.437 ** 0.424 * Previous Choice (0.166) (0.166) (0.167) Trial 3 * Previous Choice -0.236 -0.244 -0.243 (0.201) (0.201) (0.202) Trial 4 * Previous Choice -0.509 * -0.526 * -0.519 * (0.205) (0.205) (0.205) N 2456 observations, 906 subjects AIC 3305 3302 3304 BIC 3397 3395 3420 Log Likelihood -1636 -1635 -1632 Random effects Std. Dev 0.00002 0.00001 0.00002
With respect to the interaction terms, as hypothesized, there exists a significant positive interaction between previous choice and visual display (β = 0.42, Z = 2.54, p <0.05),
192 indicating that when presented with content in the graphical condition, individuals were much more likely to be influenced by their previous choice and again select a left- leaning news source. A partial effects plot of this interaction can be viewed in Figure 3.5, which demonstrates that the previously selected news choice has less of an effect with a text-only news display. There also emerged a significant negative interaction between previous choice and trial 4, indicating a participant’s previous choice becomes less influential over time, and actually has an inverse effect in the last trial (β = -0.52, Z = -2.53, p <0.05). View Figure 3.6 for details. Finally, there were significant interactions with previous choice and political party – Democrats were significantly more likely to be influenced by their previous selection, and were more likely to repeatedly select a left-leaning news source (β = 0.53, Z = 1.92, p =0.05). This relationship is presented in Figure 3.7. Further, there were also effects of previous choice and ideology: both liberals and conservatives were more likely to select a left-leaning news source, when contrasted with Independents as a baseline (Liberals: β = 0.50, Z = 2.12, p <0.05; Conservatives: (β = 0.54, Z = 2.17, p <0.05). A plot depicting the partials effects of this interaction is in Figure 3.8, and compares results of Models 2 (Ideology only) with Model 3 (Ideology and Party ID). Recall that the classification of Independents produced a relatively small category, and it is likely that the small N and potentially higher variability in this group produces some unexpected effects.
193
Figure E.5: Influence of the graphical layout on subsequent source selections. Partial effects plot demonstrating that previous choice is most influential in the graphical condition.
Selection of Left-Leaning Source Interaction between Trial and Previous Choice 0.54 0.52
Prev Choice Left 0.50 0.48 Selection of a Left-leaning source of a Left-leaning Selection 0.46
PrevChoice Not Left Previous Source Selected Source Previous 0.44
2 3 4 Trial Number Figure E.6: Probability of selecting a left-leaning news source based on previous choice and trial The y-axis represents the mean probability of selecting a left-leaning news source, and the x-axis represents trial number. It is clear that between the first two trials, a participant is most likely to repeat their past behavior, whether it means selecting – or not selecting – a left-leaning source. This effect weakens in trial 3 and completely reverses in trial 4.
194
Figure E.7: Influence of party identification and previous choice on left-leaning source selections Y-axis represents the mean probably of selecting a left-leaning news source, and the x-axis represents the previous choice (non-left or left-leaning source). Democrats are more likely to repeatedly select a left- leaning source; s contrasted with Republicans and Independents.
Figure E.8 Standard errors for prior trial and significant variables Participants’ prior trial interacted significant with four other variables; plots of the standard errors for previous choice are displayed. In each box, the two rows represent the previous choice: the top row indicates that a left-leaning source was previously selected, and the bottom row represents a left-leaning source was not selected. There are no significant effects for being Republican, female, or polarized. The remaining factors: visual treatment, Democrat, trial, and ideology all interact with the previously selected.
195 196 APPENDIX F: LOGISTIC REGRESSIONS FOR LEFT AND RIGHT NEWS SOURCES
As discussed in Chapter 2, Opinion is not distinctly hard news, nor is it soft. For reference, Table F.1 reports results of the news selection models used in Chapter 2, though with excluding the Opinion category. As described, the inclusion of this category does not change results in any significant nor practically meaningful way. Additionally, two models are computed predicting the selection of the Opinion category only – in Trial 1 (Model 3), and in Trials 2-4 (Model 4). Being female is negative associated with selecting the Opinion category in the first news trial (β=-0.64, Z=-2.50, p<0.05). No other variables were significant in predicting the selection of Opinion in Trial 1. When looking at trials 2-4, the same trends evident in other selection models were also present: the previously selected news choice has a significant effect on predicting the selection of the opinion category (β=2.27, Z=5.94, p<0.001), experiment trials are also influential (Trial 3: 0.75, Z=5.07, p <0.001; Trial 4: β=0.68, Z=4.42, p<0.001), as well as the interaction between trial and previous choice (Trial 3 & previous choice: β=-2.34, Z=-4.84, p <0.001; Trial 4 & previous choice: β =-2.51, Z=- 5.38, p <0.001). Finally, in addition, there was a significant effect of gender and previous choice (β=1.53, Z=-3.32, p<0.001), indicating that the males who previously selected opinion were much more likely to repeatedly do so. Graphs of these plots can be seen in Figures F1a and F1b and F2a and F2b.
197 Table F.1 Prediction News Selection: Opinion Only, and Excluding Opinion M1: Hard M2: Hard M3: Opinion M4: Opinion News, Trial 1 News Trials 2- Only Only no Opinion 4 no Opinion Trial 1 Trials 2-4 (Intercept) 1.955 -0.151 -0.229 -1.577 (0.941) (0.341) (1.682) (0.721) Education (College grad +) 0.032 -0.163 0.023 0.074 (0.160) (0.145) (0.339) (0.118) Treatment __ -0.837 ** __ -0.055 (0.291) (0.112) Polarization 0.018 0.641 ** 0.817 -0.236 (0.238) (0.223) (0.533) (0.181) Gender -0.059 0.246 -0.860 * 0.001 (0.154) (0.204) (0.344) (0.122) Age -0.036 -0.004 -0.034 -0.024 (0.020) (0.003) (0.040) (0.016) News Interest (self-report) -0.343 -0.002 -0.720 -0.211 (0.295) (0.073) (0.558) (0.226) Reported TV news use -0.015 0.038 -0.219 -0.023 (0.068) (0.044) (0.140) (0.050) Reported Web news use -0.022 -0.017 0.020 0.003 (0.057) (0.037) (0.127) (0.044) Trial 3 __ 0.259 __ 0.745** (0.197) (0.147) Trial 4 __ 0.505 * __ 0.676 ** (0.199) (0.153) Previous News Choice __ 1.851 *** __ 2.272 ** (Hard / Opinion) (0.173) (0.383) Education * Graphical __ 0.533 ** __ __ Treatment (0.204) Polarization * Gender __ -0.655 * __ __ (0.292) Trial 3 * Previous Choice __ -0.808 ** __ -2.339 ** (0.248) (0.483) Trial 4 * Previous Choice __ -1.387 *** __ -2.514 ** (0.248) (0.468) Gender * Previous Choice ______-1.526 ** (0.460) Age * Self-report interest 0.012 * __ 0.009 0.009 + (0.006) (0.012) (0.005) AIC 1078.57 2615 352.35 2158 BIC 1270.13 2712 526.45 2258 Log Likelihood -499.28 -1291 -140.17 -1062 N 888 2210; id =912 -140.17 2124 Participant Random Effects __ 0.000 __ 0.000
198 Selection of Opinion Interaction Between Gender and Previous Choice Male 0.5 0.4 0.3 Probability of Selecting Opinion of Selecting Probability
Female 0.2 0.1 Gender
PrevChoice No Opinion PrevChoice Opinion Gender Figure F1a and F1b: Interaction between gender and previous choice on opinion selection. X-axis in both figures represents the previous news choice: opinion or not opinion. Y-axis represents probability of selecting opinion. Partial effects plot in top figure is created with languageR pagckage; bottom figure shows estimated confidence intervals of the same model without random effects using the effects package.
Interaction between Gender and Opinion
PrevChoice No Opinion PrevChoice Opinion Male Female
0.3
0.25
0.2
0.15
0.1 Probability of Selecting Opinion of Selecting Probability
0.05
PrevChoice No Opinion PrevChoice Opinion Previous Choice
199 Selection of Opinion Interaction Between Trial and Previous Choice PrevChoice Opinion 0.5 0.4 0.3 Probability of Selecting Opinion of Selecting Probability 0.2
PrevChoice No Opinion 0.1 Previous Choice Previous
2 3 4 Trial Number: 2, 3, 4 Figure F2a and F2b: Interaction between trial and previous choice on opinion selection. X-axis in both figures represents experiment trial: 2, 3, or 4. Y-axis represents probability of selecting opinion. Partial effects plot in top figure is created with languageR pagckage; bottom figure shows estimated confidence intervals of the same model without random effects using the effects package.
Interaction between Trial and Opinion
2 3 4 PrevChoice No Opinion PrevChoice Opinion
0.4
0.3
0.2 Probability of Selecting Opinion of Selecting Probability 0.1
2 3 4 Trial
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