Does the Type of News Aggregation Personalization Influence Information-Seeking Behavior?

THE AGGREGATION EFFECT: DOES THE TYPE OF NEWS AGGREGATION PERSONALIZATION INFLUENCE INFORMATION-SEEKING BEHAVIOR?

LAUREN D. FUREY

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2016

To my kind and courageous dad, who inspired me to always follow my dreams

ACKNOWLEDGMENTS

Pursing my Ph.D. has been one of the most challenging and rewarding experiences of my life. I would not have made it through this program without the love, support, kindness, and guidance of my family, teachers, and friends. I am incredibly lucky to have such wonderful people in my life, and I would like to thank everyone who helped me reach my dream of becoming a journalism professor.

First, I would like to pay special tribute to my wife, Heather, for her unwavering love and support through two challenging graduate degrees. She has been there every day to share her confidence during stressful moments, and she always found time, despite the demanding workload, to fit in adventures and make life fun.

I am also incredibly thankful for the love and encouragement of our two families.

My mom has been an ever-present inspiration, and I am so honored that I’ve had the opportunity to, quite literally, follow in her footsteps. My brother, David, has taught me how to overcome obstacles with grace and strength, and I am so happy my sister, Liz, could be my partner in crime the past few years as we conquered our graduate programs together. And finally, I would like to thank my amazing in-laws and the

Schoenbecks for their constant encouragement through lots of homemade cookies, cards, and care packages.

Deepest gratitude is due to my advisor, Norm Lewis, for being my pillar the past three years. He not only guided me through the course of completing this dissertation but also through the first and hardest class of my doctoral program. Dr. Lewis’ expertise has helped me become a better scholar, writer, and teacher. In addition, he is one of the most selfless people I have ever met, and I will strive to my hardest to reflect his kindness in my interactions with students.

I would also like to thank my other committee members, Spiro Kiousis, Tamir

Sorek, Michael Weigold, and Sriram Kalyanaraman for their guidance, which has pushed me to make my past, present, and future research more informed, rigorous, and both theoretically and practically significant. I am humbled and honored that I have gotten the opportunity to work with such amazing scholars, who are in equal measure wonderful people.

Last but certainly not least, I would like to acknowledge all of the amazing friends

I have made during my tenure at the University of Florida. Not only were they an incredible support system over the past three years, but they are also some of the best friends I have ever had. I am so grateful that I will get to call these people friends for life.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 9

LIST OF FIGURES ...... 10

ABSTRACT ...... 13

CHAPTER

1 INTRODUCTION ...... 14

Conceptualizing News Aggregators ...... 14 Study Purpose ...... 16 Effects of Selective Exposure from Aggregators ...... 19 Increased Polarization ...... 19 Amplified Narcissism ...... 22 Hindered Democracy ...... 23 Current Status of Research ...... 26 Academic Contribution ...... 28

2 LITERATURE REVIEW ...... 30

Theoretical Framework ...... 30 Conceptualizing Attitudes ...... 30 Rationale for ELM ...... 32 Two Processing Routes...... 35 Factors Increasing Motivation to Process ...... 38 Effects of News Aggregation ...... 39 Conceptualizing Personalization ...... 39 Effects of Personalization on Attitudes ...... 41 Effects of Personalization on Information-Seeking Behavior ...... 42 Conceptualizing Different Types of Aggregators ...... 45 Effects of Different Types of Aggregators ...... 47 Factors Influencing People’s Responses to News Aggregators ...... 48 Cognitive Elaboration ...... 48 Perceived Credibility ...... 50 Effects of Different Types of News ...... 50 Conceptualizing Different Types of News ...... 50 Responses to Different Types of News ...... 52 Factors Influencing People’s Responses to Different Types of News ...... 53 Interaction of Different Types of Aggregators and Different Types of News ...... 54

3 METHODS ...... 56

Overview ...... 56 Rationale for Experimental Method ...... 56 Limitations of Experiments ...... 59 Participants ...... 60 Procedure ...... 61 Stimulus Materials ...... 62 Type of News Manipulations ...... 62 Aggregation Manipulations ...... 63 Manipulation Checks...... 65 Dependent Variables ...... 65 Attitude toward the Content ...... 66 Information-Seeking Behavior ...... 66 Intervening Variables ...... 67 Cognitive Elaboration ...... 67 Perceived Credibility ...... 68 Demographic Variables ...... 68

4 RESULTS ...... 69

Descriptive Analysis of Data ...... 69 Preparing Measures for Analysis ...... 72 Manipulation Checks...... 74 Hypothesis Testing ...... 75 Type of Aggregator Results ...... 75 Intervening Variable Results for Type of Aggregator ...... 78 Type of News Results ...... 80 Intervening Variable Results for Type of News ...... 82 Interaction of Type of Aggregator and Type of News Results ...... 84 Demographic Variable Results ...... 87

5 DISCUSSION ...... 93

Findings and Practical Implications ...... 93 Effects of News Aggregation ...... 93 Effects for Different Types of News ...... 97 Interaction Effects for Types of Aggregators and Types of News ...... 98 Implications for the News Bubble ...... 100 Findings and Theoretical Implications ...... 102 Factors Affecting People’s Responses to Different Types of Aggregators ..... 102 Factors Affecting People’s Responses to Different Types of News ...... 104 Study Limitations ...... 106 Suggestions for Future Research ...... 107 Conclusion ...... 109

APPENDIX

A EXPERIMENT INFORMED CONSENT ...... 110

B GOOGLE NEWS STIMULUS MATERIALS ...... 112

C FACEBOOK STIMULUS MATERIALS ...... 122

D INSHORTS STIMULUS MATERIALS ...... 132

E GAINESVILLE SUN STIMULUS MATERIALS ...... 142

F NEWS STORY STIMULUS MATERIALS ...... 144

G EXPERIMENT QUESTIONNAIRE ...... 154

H INFORMATION-SEEKING BEHAVIOR CODE BOOK ...... 161

LIST OF REFERENCES ...... 163

BIOGRAPHICAL SKETCH ...... 178

LIST OF TABLES

Table page

4-1 Frequency of Participants by Age ...... 70

4-2 Frequency of Participants by Family Household Income ...... 70

4-3 Frequency of Participants by Religious Affiliation ...... 70

4-4 Frequency of Participants by Political Affiliation ...... 70

4-5 Frequency of Participants by Race/Ethnicity ...... 71

4-6 Frequency of Participants by Education Level ...... 71

4-7 Frequency of Participants by Major ...... 71

4-8 List of News Subject Areas ...... 74

4-9 Regression Analysis Summary for Cognitive Elaboration Predicting Attitude ..... 79

4-10 Regression Analysis Summary for Perceived Credibility Predicting Attitude ...... 80

4-11 Mean Scores and Standard Deviations for Interaction Results...... 87

4-12 Regression Analysis Summary for Gender ...... 88

4-13 Regression Analysis Summary for Age ...... 88

4-14 Regression Analysis Summary for Race/Ethnicity ...... 89

4-15 Regression Analysis Summary for Family Household Income ...... 89

4-16 Regression Analysis Summary for Religious Affiliation ...... 90

4-17 Regression Analysis Summary for Political Affiliation ...... 90

4-18 Regression Analysis Summary for Education Level ...... 91

4-19 Regression Analysis Summary for Major ...... 91

4-20 Summary of Hypothesis Results ...... 92

LIST OF FIGURES

Figure page

4-1 Attitude Results for Types of Aggregators...... 76

4-2 Number of Subjects Read Results for Types of Aggregators...... 77

4-3 Cognitive Elaboration Results for Types of Aggregators ...... 78

4-4 Perceived Credibility Results for Types of Aggregators...... 80

4-5 Attitude Results for Types of News...... 81

4-6 Time Spent in Seconds on Related Information Results for Types of News...... 82

4-7 Number of Subjects Read Results for Types of News...... 82

4-8 Cognitive Elaboration Results for Types of News...... 83

4-9 Perceived Credibility Results for Types of News...... 84

4-10 Attitude Results for the Interaction of Types of Aggregators and Types of News...... 85

4-11 Time Spent in Seconds on Related Information Results for the Interaction of Types of Aggregators and Types of News...... 86

4-12 Number of Subjects Read Results for the Interaction of Types of Aggregators and Types of News...... 86

B-1 Google News Stimulus Materials (Traffic and Weather)...... 112

B-2 Google News Stimulus Materials (Jobs and Unemployment)...... 113

B-3 Google News Stimulus Materials (Crime and Public Safety)...... 114

B-4 Google News Stimulus Materials (National Politics)...... 115

B-5 Google News Stimulus Materials (Technology and Science)...... 116

B-6 Google News Stimulus Materials (Music, Movies, and TV Shows)...... 117

B-7 Google News Stimulus Materials (Celebrities)...... 118

B-8 Google News Stimulus Materials (Sports)...... 119

B-9 Google News Stimulus Materials (Cooking)...... 120

B-10 Google News Stimulus Materials (Health and Fitness)...... 121

C-1 Facebook Stimulus Materials (Traffic and Weather)...... 122

C-2 Facebook Stimulus Materials (Jobs and Unemployment)...... 123

C-3 Facebook Stimulus Materials (Crime and Public Safety)...... 124

C-4 Facebook Stimulus Materials (National Politics)...... 125

C-5 Facebook Stimulus Materials (Technology and Science)...... 126

C-6 Facebook Stimulus Materials (Music, Movies, and TV Shows)...... 127

C-7 Facebook Stimulus Materials (Celebrities)...... 128

C-8 Facebook Stimulus Materials (Sports)...... 129

C-9 Facebook Stimulus Materials (Cooking)...... 130

C-10 Facebook Stimulus Materials (Health and Fitness)...... 131

D-1 Inshorts Stimulus Materials (Traffic and Weather)...... 132

D-2 Inshorts Stimulus Materials (Jobs and Unemployment)...... 133

D-3 Inshorts Stimulus Materials (Crime and Public Safety)...... 134

D-4 Inshorts Stimulus Materials (National Politics)...... 135

D-5 Inshorts Stimulus Materials (Technology and Science)...... 136

D-6 Inshorts Stimulus Materials (Music, Movies, and TV Shows)...... 137

D-7 Inshorts Stimulus Materials (Celebrities)...... 138

D-8 Inshorts Stimulus Materials (Sports)...... 139

D-9 Inshorts Stimulus Materials (Cooking)...... 140

D-10 Inshorts Stimulus Materials (Health and Fitness)...... 141

E-1 Gainesville Sun Stimulus Materials (Civic Affairs)...... 142

E-2 Gainesville Sun Stimulus Materials (Entertainment)...... 143

F-1 Traffic and Weather News Story...... 144

F-2 Jobs and Unemployment News Story...... 145

F-3 Crime and Public Safety News Story...... 146

F-4 National Politics News Story...... 147

F-5 Technology and Science News Story...... 148

F-6 Music, Movies, and TV Shows News Story...... 149

F-7 Celebrities News Story...... 150

F-8 Sports News Story...... 151

F-9 Cooking News Story...... 152

F-10 Fitness and Health News Story...... 153

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

THE AGGREGATION EFFECT: DOES THE TYPE OF NEWS AGGREGATION PERSONALIZATION INFLUENCE INFORMATION-SEEKING BEHAVIOR?

Lauren D. Furey

August 2016

Chair: Norman Lewis Major: Mass Communication

Aggregation websites and apps are popular outlets for news delivery because of their ability to curate and personalize news content. Yet as readers continue to use aggregators — and those aggregators learn more about users and their interests — concerns have developed that aggregators will lead users, especially young adults, to narrow their information-seeking behavior. To test whether these concerns are valid, a 4

(type of aggregator: an aggregator that uses an algorithm to provide personalized news recommendations like Google News, an aggregator that uses on social connections to provide personalized news recommendations like Facebook, an aggregator that uses editors to provide personalized news recommendations like inshorts, and no aggregation) x 2 (type of news: civic affairs or entertainment) experiment was conducted. Results revealed that young adults hold a positive attitude and higher perceptions of credibility toward popular aggregators like Google News and Facebook, but these aggregators did not have a narrowing impact on information-seeking behavior, which dampens concerns of selective exposure and the news bubble.

CHAPTER 1 INTRODUCTION

Conceptualizing News Aggregators

News aggregators are platforms that find news from multiple sources and organize the material by subject using an algorithm or human judgment (American

Press Institute, 2015). These platforms include Web-based portals, such as Yahoo!

News and Google News, news aggregating apps (e.g. Flipboard), as well as social media sites like Facebook and Twitter (American Press Institute, 2015). Interest in using platforms like these for news has skyrocketed (Ingram, 2015). For instance, Apple announced a news-aggregating app in 2015 called “the best mobile reading experience ever” (Greenburg, 2015). Google News receives millions of page views per day, making it one of the most popular news websites in the world (Liu, Dolan, & Pedersen, 2010;

Ingram, 2015), and 40 percent of all U.S. adults read news on Facebook (Pew

Research Center, 2014).

Aggregation websites and apps are popular outlets for news delivery because of their ability to curate and personalize news content. Aggregators curate content by compiling information from various Internet sources about an array of topics, giving convenient access to continuously updated news coverage from multiple online news sources (Kavanaugh et al., 2014). Furthermore, news aggregators provide personalized content by curating headlines and news stories based on the preferences of individual newsreaders. Aggregators are designed to understand the needs and interests of users

(Xiao & Benbasat, 2007), usually by analyzing past reading behavior (Linden, 2008), in order to provide personalized news recommendations suited to each user’s individualized tastes (Hosanagar, Fleder, Lee, & Buja, 2013). For example, Google

News’ algorithm chooses news stories for its users as well as prioritizes them to show headlines that would elicit the highest degree of interest. Therefore, in comparison to a static front page with the same stories, aggregators create millions of individualized homepages per day, “each one targeting just one person — you” (Linden, 2008).

Research has consistently found that aggregators’ ability to curate and match content to a user’s individualized self is well liked (Kalyanaraman & Wojdynski, 2015), especially because it increases convenience by helping users navigate the avalanches of information available online (Holton & Chyi, 2012; Sunstein, 2001; Xiao & Benbasat,

2007). As Eli Pariser (2011) explained it: “It’s one thing to search through five channels.

It’s another to search through five hundred. And when the number hits five thousand — well, the method’s useless” (p. 22). Because of the way aggregators operate, newsreaders are no longer plagued with the task of choosing what to read from infinite options because the aggregator handles all of that for them. Aggregators affected news reading by changing digital news delivery from a pull technology into a push technology

(Pariser, 2011).

Pull technologies require effort from users in order to pull information from the interface, but push technologies push information onto their users. For example, television is a push technology. Once viewers are on a desired news channel, the broadcast station will tell them information about a variety of topics. Alternatively, the

Internet typically acts as a pull technology because a user has to put an address or search terms into the Web browser, then he or she also has to choose from all the options provided to determine what to read. Traditionally, users had to curate their own media, but now aggregators provide a personalized list of headlines. Therefore, users

no longer have to figure out what stories would interest them. Instead, they can be more passive during their news reading experiences (Pariser, 2011).

In fact, the longer people use aggregators that employ some degree of machine learning or feedback loops, the more tailored the experience becomes. For instance, some aggregating sites and apps, like Apple News and News360, connect with users’

Facebook and Twitter pages or track click behavior associated with individual users’ accounts to learn more about them and their specific topics of interest over time (Henry,

2011; Williams, 2015). This concept is similar to selecting the “thumbs up” or “thumbs down” icons on the music app Pandora, a feature that allows Pandora to learn what types of music individual users like and dislike. For news aggregators, the goal is “to customize the news it delivers to you so you’re never presented with a story that’s uninteresting” (Henry, 2011). Yet as readers continue to use aggregators — and those aggregators learn more about users and their interests — concerns have developed that aggregators will lead users to narrow their information-seeking behavior.

Study Purpose

While the Internet makes a wide variety of sources, information, and opinions accessible to online newsreaders (Sunstein, 2001), by encouraging users to focus their attention on individualized interests and needs, there is fear that aggregators cause greater selective exposure. Selective exposure is defined as a tendency to seek out information that reinforces existing opinions (Beam, 2014). In other words, selective exposure refers to the idea that people prefer to read attitude-consistent information over material that challenges their beliefs (Beam, 2014; Festinger, 1957, 1964). People are more likely to favor attitude-confirming news because it reinforces confidence in their pre-existing attitudes and opinions in comparison to dissonant content, which can

cause uncertainty and psychological discomfort (Beam & Kosicki, 2014; Festinger,

1957).

As a result, selective exposure acts as a driver of people’s information-seeking behavior. Webster and Wakshlag (1985) described selective exposure as “an act of choice in which an individual selects from a range of possible activities or messages” (p.

37). This occurs because individuals’ predispositions to act in certain ways produce motivations that, in turn, drive their choices for media exposure. For example, someone who is a passionate sports fan would likely choose to read the sports section out of a newspaper, tune into sports radio, visit ESPN’s website and/or watch ESPN on cable

(Dutta-Bergman, 2004). Similarly, selective exposure explains why conservative

Republicans are more likely to watch Fox News, listen to conservative radio, and use conservative political news sites, while liberal Democrats are more likely view news outlets that correspond with their political beliefs, like MSNBC (Garrett, 2009a; Stroud,

2007).

Cass Sunstein (2001) said this kind of filtering is natural, but because aggregators allow users to more easily ignore counter-attitudinal media in comparison to other journalism formats, they could lead to even greater selective exposure (Beam,

2014). Pariser (2011) argued that aggregators block cues that prompt newsreaders to learn new information outside of their pre-existing interests and beliefs. This is different from news organizations that seek to provide a broad array of information and viewpoints and thus offer a greater chance for consumers to encounter information counter to their opinion. For example, a reader may have strong opinions in favor of the death penalty but might pass over a story about an inmate exonerated after death and

at least be reminded about a differing point of view. Similarly, television viewers might come across opposing information while browsing through channels. However, if the goal of aggregators is to provide a personalized news-reading experience (Henry,

2011), counter-attitudinal information may be missing from aggregated newsfeeds

(Pariser, 2011).

If aggregators do lead users to increased selective exposure, aggregators could also cause them to develop a more narrowed view of public life built on a closed loop of self-reinforcing information, or if this is not the case, by attracting users with personalization and helping them navigate the information overload often encountered online, aggregators also offer the potential to engage more people in news more quickly and more often, thus producing a public more literate in civic affairs. This question is one of the most important of the day for journalism, and beginning to answer it will help know whether news aggregation is a solution to a problem (lack of civic engagement) or whether aggregators will require some kind of balancing mechanism in order to avoid a closed system that disconnects citizens from democracy. As a result, this study seeks to discover whether the personalized experience provided by news aggregators lead users to narrow their information-seeking behavior to similar topics.

The primary purpose of this study is to examine whether personalized news recommendations provided by these various types of aggregators (aggregators that use an algorithm to provide personalized news recommendations like Google News, aggregators that rely on social connections to provide personalized news recommendations like Facebook, and aggregators that use editors to provide personalized news recommendations like Wildcard and inshorts) lead users to greater

selective exposure. By studying news aggregation, this dissertation will examine whether selective exposure influences information-seeking behavior, which in turn can address a concern of selective exposure known as the news bubble.

Effects of Selective Exposure from Aggregators

Fears of a news bubble stem from a concept introduced by Pariser (2011) called the filter bubble. The filter bubble results when a website’s algorithm (like the one used by Google) guesses what users would like to see based on their search history, click behavior, and location. The Huffington Post described the filter bubble as a “figurative sphere surrounding you as you search the Internet,” and it results from any websites that use an algorithm to personalize what users see, including Google, Facebook, and

Netflix to name a few (Lazar, 2011). Pariser (2011) argued that personalized news systems hold the potential to lead to greater selective exposure, by closing people off to new information, subjects, and ideas, as well as further consequences over time, such as increased political polarization and narcissism, which could also pose problems for a functioning democracy.

Increased Polarization

The first consequence expected from aggregator-created selective exposure is: increased polarization. This fear stems from aggregators because not receiving diverse information (as is typical with an aggregated news experience) makes it difficult for users to understand the broader news environment and whether the information they see is representative. As a result, Sunstein (2001) suggested that personalization further encourages users to isolate themselves from content that does not conform to existing interests and beliefs, which can create some blind spots or the wrong impression. Pariser (2011) argued that when readers see a narrowed selection of news,

it is not uncommon for them to translate “lots of pages of similar information” into “likely to be true.” As aggregators continue to provide more personally relevant information, the likelihood that users will only see information that validates their pre-conceived opinions also increases.

According to Pariser (2011), continual exposure to attitude-consistent information can make people overconfident in their knowledge as well as amplify their opinions.

Similarly, Sunstein (2001) said personalized news systems have the potential to breed extremism. He feared that if individuals continue to bypass general interest news in favor of restricting themselves to topics and opinions that favor their interests, the world would consist of millions of people “listening to louder echoes of their own voices”

(Sunstein, 2001, p. 16). Therefore, Sunstein hypothesized that personalized portals would act as an echo chamber (a.k.a. polarization machine) (Myers, 2015; Sunstein,

2001). Echo chambers are a side effect of selective exposure. As people are consistently exposed to information that reinforces their opinions, those opinions tend to become more polarized over time (Feldman, Myers, Hmielowski, & Leiserowitz, 2014;

Sunstein, 2001).

While increased polarization is of concern to many scholars (Feldman, Myers,

Hmielowski, & Leiserowitz, 2014; Garrett, 2009a; Sunstein, 2001), some have argued the opposite occurs. For instance, Linden (2011) said “the goal of personalization and recommendations is discovery,” not fragmentation. On top of making the mounds of information available online more manageable (Holton & Chyi, 2012; Sunstein, 2001;

Xiao & Benbasat, 2007), Linden (2011) said recommendation systems help people find things they normally would have difficulty finding on their own (Hosanagar et al., 2013).

Even Sunstein (2001), who has actively written about the problems that could arise from online news personalization, agreed that technological advances provide the capability to find information, topics, and point of views that may be have formerly been too difficult to locate, which means aggregators also offer the potential to open up alternative topics and points of view.

The rising popularity of news aggregators is also thought to be a good thing because they lessen the influence of media as intermediaries as well as dampen fears of mass persuasion. Aggregators put the power of what to read in the hands of audiences versus allowing media executives to tell the public what they should read

(Chaffee & Metzger, 2001). Additionally, because the algorithm aggregators use to curate media are mechanistic and removed from human intervention and human interest, they are usually perceived as more objective than editors (Carlson, 2007). For instance, survey research from the PR firm Edelman discovered that people trust

Google News more so than any other sources of news (Epstein, 2016).

Slightly more complex are news aggregators that employ social networking.

Social media sites offer potential for a broader range of viewpoints if one’s friends and family reflect attitudinal diversity. However, another perspective is that personal networks do not offer much difference because of homophily, or the degree to which individuals are similar (Rogers, 1983), which fosters connections (McPherson, Smith-

Lovin, & Cook, 2001). Some people prefer Facebook as their news aggregator because they implicitly believe friends and family best know their interests. As a result, the

Edelman survey also discovered that 78 percent of people trust news shared by friends or family online, which is much higher than news shared by academic experts (65

percent) and journalists (44 percent) (Epstein, 2016), but homophily poses implications for the information people receive, the attitudes they form, and their behaviors

(McPherson et al., 2001). As homophily of a social tie increases, the more likely a social recommendation is to guide opinions and behaviors (Brown & Reingen, 1987).

Amplified Narcissism

Another potential consequence of aggregator-created selective exposure is increased narcissism. Narcissism is a personality trait exhibited when someone has an unrealistic sense of self and entitlement (Campbell, Rudich, & Sedikides, 2002).

Frequent users of personalized news aggregators may exhibit this attribute because aggregation allows users to fulfill personal motivations over finding information on range of topics (Papacharissi, 2008; Sunstein, 2001). Sunstein (2001) argued that on top of isolating users from diverse political perspectives, personalized news also risks accentuating divides between different social groups (e.g. young and old, rich and poor, as well as divisions among religious groups, differing races, and nationalities) because they encourage users to only be interested in material like themselves.

As a result, Tewksbury (2005) said aggregators could cause social fragmentation or create isolated groups of people who know a lot about their own social worlds but little about anyone else’s. Common experiences can act as a “form of social glue,” but without these, Sunstein (2001) said, “People may find it hard to understand one another” (p. 9). As a result, he also warned that users could develop “an egocentric rather than socially concerned view” of the world around them (Bennett, 1996, p. 39). As

Pariser (2001) explained it, the filter bubble can create the impression that users’ narrow self-interests are all that matter. Therefore, users’ information-seeking behavior

could become narcissistically motivated, instead of based on learning value

(Papacharissi, 2008).

Such fears are magnified for young adults because they are already prone to narcissism (Kwon & Wen, 2010; Twenge, Konrath, Foster, Campbell, & Bushman,

2008). Additionally, young adults rely on aggregators, especially Facebook, at a much higher rate than older populations (American Press Institute, 2015; Mitchell, Jurkowitz,

& Olmstead, 2014). However, if young adults are relying on Facebook for news, research also suggests that social media facilitates communication across distances and with people of various backgrounds who may not have otherwise been able to communicate (Hosanagar et al., 2013; Van Alstyne & Brynjolfsson, 2005), which, alternatively, could prevent instead of encourage social fragmentation.

The Media Insight Project, as part of the American Press Institute and the

Associated Press-NORC Center for Public Affairs Research, also found evidence that rebutted the notion that young adults have become narrow minded and “newsless.”

While their survey did reveal that young adults prefer not to visit news sites directly, often opting to use social media sites to find news, this form of information-seeking behavior may widen awareness rather than narrow it. For instance, the survey found that young adults regularly follow five or more news topics that include a mix of civic affairs, entertainment, and practical news. Furthermore, 70 percent reported seeing diverse opinions while reading news on their social media feeds, and 25 percent said they often investigate opinions different from their own (American Press Institute, 2015).

Hindered Democracy

No matter the aggregator, however, Sunstein (2001) was afraid an even greater problem could result because of personalized news portals’ potential to increase

polarization and narcissism: a hindered democracy. Democracy is “a government in which the supreme power is vested in the people and exercised by them directly or indirectly through a system of representation usually involving periodically held free elections” (Merriam-Webster). In order for people to be informed well enough to make these decisions, they need to be exposed to diverse information.

However, Sunstein (2001) argued that personalization interferes with this by draining people of their interest in reading diverse information, thereby fostering selective exposure and threatening people’s capacity to govern themselves. Even before advances in technology, people still read information suited to their tastes. For example, sports enthusiasts read sports magazines, dog lovers read dog magazines and liberal and conservatives read content related to their point of view. However, the difference now is that general-interest news sources may be supplemented or supplanted by personalized news aggregators that promote pleasure-seeking and entertainment at the expense of civic affairs, which is problematic for democracy because it displaces the public’s ability and desire to self-govern (Katz, 1996).

As a result, aggregators may also impede the public sphere (Pariser, 2011). The public sphere is a term used in sociology to represent a place Jürgen Habermas (1973) conceptualized. This place was somewhere everyone could go and express their opinions freely. The public sphere was open to all, regardless of status, and it was somewhere public transformation could occur independently of economic and political institutions. While only a few would attend, the important thing was that everyone was welcome, and everyone’s ideas could be heard. The only judgments made were based on the arguments used in the debate, not the person offering the opinion. Therefore, for

Habermas, the public sphere provided “a realm of our social life in which something approaching public opinion can be formed” (Pusey, 1993, p. 89).

Public opinion can be formed through communication of “generalizable interests that transcend the particular interests of competing groups and individuals” (Pusey,

1993, p. 90). While reaching a consensus was desirable, the most important part of the public sphere was that people were uninhibitedly discussing diverse information, therefore upholding democracy (Papacharissi, 2008). A classic example of the public sphere was the coffee houses of the 1700s, where people would meet for public discussion (Kovach & Rosenstiel, 2014). Some thought mass media would play an important role in upholding the public sphere because they could inform public opinion by allowing communication between people who could not meet and talk directly.

However, Habermas argued that because newspapers are commercialized, their objective is to help corporate interests publish their public relations efforts, not fostering democratic discourse (Habermas, 2004; Papacharissi, 2008).

When the Internet developed, some thought it would revitalize the public sphere because it allows for “unlimited and unregulated discourse that operates beyond geographic boundaries” (Papacharissi, 2008, p. 3). Some researchers have supported this perspective (Blumler & Gurevitch, 2001; Castells, 2012; Shah, Cho, Eveland, &

Kwak, 2005). For example, Shah et al. (2005) discovered that reading online news enhanced interpersonal political discussion and online civic messaging, thus leading the authors to conclude that the Internet enhances civic participation. Additionally, Castells

(2012) discussed the role of technology and the Internet in supporting social movements. Social media allowed the protests surrounding the Arab uprisings to move

into other countries. People participated in the Occupy Wall Street movement through social media (even when they could not physically occupy Wall Street in New York), and the #BlackLivesMatter movement may have been sparked in Ferguson, Missouri, but the Internet has allowed people across multiple borders to get involved and voice their opinions on the movement.

However, others were not convinced. Habermas (2004) thought the Internet, too, would become commercialized because politicians and people with power have the ability to connect and interact with the public directly online. Also, while a lot of differing viewpoints and perspectives are available online, that does not mean people will pay attention to material that counters pre-existing viewpoints. Ultimately, by providing a space where people can act on their own desires (Papacharissi, 2008), there is potential for the Internet to dilute the function of the public sphere (Papacharissi, 2008;

Swanson, 2000).

Current Status of Research

Although increased polarization, amplified narcissism, and a hindered democracy are potential concerns from aggregator-created selective exposure, as the previous discussion illustrates, there is also evidence that personalized news systems can enhance discovery, social interaction, and public discourse (Blumler & Gurevitch, 2001;

Castells, 2012; Hosanagar et al., 2013; Linden, 2011; Shah et al., 2005; Van Alstyne &

Brynjolfsson, 2005). In order to further evaluate these contrasting viewpoints, it is first important to determine if personalized news systems lead to greater selective exposure.

This can be resolved by examining whether aggregators lead users to narrow their information-seeking behavior (Clay, Barber, & Shook, 2013).

While concerns about selective exposure are common (Pariser, 2011; Sunstein,

2001), only a few studies have addressed the effects of aggregated newsfeeds on information-seeking behavior (Beam, 2014; Beam & Kosicki, 2014; Kalyanaraman &

Sundar, 2006), and these studies also reveal contradictory results. For example, in their analysis of secondary survey data from the Pew Research Center, Beam and Kosicki

(2014) concluded that aggregators do not appear to cause increased selective exposure because a correlation existed between personalized news use and increased exposure to diverse news. More specifically, users of personalized news systems indicated viewing more news sources and categories of news than nonusers.

Alternatively, when Kalyanaraman and Sundar (2006) examined the effects of personalized news on news-reading behavior, they affirmed the notion that personalized news can lead to selective exposure. Kalyanaraman and Sundar (2006) concluded this by tracking participants’ information-seeking behavioral responses online after they read personalized news stories on the MyYahoo! online news portal. They found that participants in a high personalization condition, where all of the news recommendations matched their interests, spent more time on the MyYahoo! Page (in lieu of visiting of alternative websites) than participants in the medium condition, where some of the news recommendations matched user interests, and the low condition, where none of the recommendations matched user interests.

Similarly, Beam (2014) concluded that personalized news recommendation systems promote news selectivity because participants exposed to news pages containing personalized news recommendations read significantly less counter- attitudinal information than those who saw a generic condition. In this case, participants

who customized news for themselves viewed the fewest number of news stories from counter-attitudinal sources, participants who received computer-generated recommendations read an increased number of dissonant sources, and those in the generic condition saw the most.

Beam’s (2014) study introduced the role of the recommender (whether the headlines were customized by the individual user or personalized by an algorithm) in shaping people’s information-seeking behavioral responses to personalized news, and follow-up research has aimed to examine whether the presence of a recommendation source influences users’ selection of news. Related to this, Yang (2016) discovered that when a recommendation (which stories were most viewed) was signaled to participants, they spent less time exploring outside topics and more time reading the stories suggested by the recommendation agent. Similarly, Turcotte, York, Irving, Scholl, and

Pingree (2015) found that readers, who received news stories recommended by a

Facebook friend, indicated they were more likely to return to the same type of content in the future than those who viewed stories directly from a news outlet.

Academic Contribution

These findings illustrate that aggregators hold the potential to influence users’ information-seeking behavior (Beam, 2014; Kalyanaraman & Sundar, 2006; Turcotte et al., 2015; Yang, 2016). However, the effect of different types of aggregators has yet to be fully explored in research. Beam (2014) did examine the difference between computer-generated personalization and user-generated customization, but there are additional ways aggregators can provide personalized content, including social recommendations of content through social media sites like Facebook as well as editor- gatekeepers recommendations through sites like the Drudge Report (Sundar & Nass,

2001; Turcotte et al., 2015). To fill this gap, this study will examine whether the different ways aggregators provide personalized content lead users to narrow their information- seeking behavior to topics similar to what is personalized for them.

Another objective of this study is to examine the interaction of the different types of news aggregators with two different types of news: civic affairs news and entertainment news. Variations in results may arise depending on the types of news people read, which could explain conflicting findings from research examining the effects of aggregation on information-seeking behavior (Beam, 2014; Beam & Kosicki,

2014). This should also help shed light on whether aggregators promote pleasure- seeking and entertainment news at the expense of civic affairs news (Sunstein, 2001).

And finally, this study seeks to expand the Elaboration Likelihood Model by examining if personalized messages and different news types lead users to engage in more effortful thinking and if different types of aggregators provide different cues to users that prompt changes in information-seeking behavior.

CHAPTER 2 LITERATURE REVIEW

Theoretical Framework

In order to effectively study whether aggregators lead users to narrow their information-seeking behavior, it is first important to understand the role of theory in research. Baran and Davis (2012) defined theory as “any organized set of concepts, explanations, and principles of some aspect of human experience” (p. 11), and the purpose of theory is to guide the path and focus of research. Griffin (1994) said theory

“brings clarity to an otherwise jumbled situation; it draws order out of chaos. … (It) synthesizes the data, focuses our attention on what’s crucial, and helps us ignore that which makes little difference” (p. 34). In other words, theory helps researchers figure out how best to approach a study by establishing the structure for research and focusing their gaze on potential hypotheses that could result (Baran & Davis, 2012).

The theoretical framework that provides the foundation for this research study is the Elaboration Likelihood Model (ELM) (Petty & Cacioppo, 1986). The cornerstone of

ELM, a theoretical approach that explains how communication affects attitudes, is its focus on how people cognitively process information. Scholars in this tradition believe it is important to understand people’s modes of processing information in order to accurately assess the impact of communication on attitudes (Perloff, 2014).

Conceptualizing Attitudes

Eagly and Chaiken (1993) described attitudes as “a psychological tendency that is expressed by evaluating a particular entity with some degree of favor or disfavor” (p.

1). Likewise, Fishbein and Ajzen (1975) said attitudes are “a learned disposition to respond in a consistently favorable or unfavorable manner with respect to a given

object” (p. 6). The common theme that comes from definitions like these is that attitudes are people’s general evaluations or judgments of other people, objects, and issues.

People develop their attitudes through a variety of socialization experiences and/or through communication (Perloff, 2014; Petty & Cacioppo, 1986).

Another important aspect about attitudes and the reason they are discussed in this particular study is: Attitudes have the potential to influence people’s behaviors

(Petty & Cacioppo, 1986; Perloff, 2014), like information-seeking behavior. Although this is not always the case, a consistency between attitudes and behavior often exists because attitudes help people organize their world so they can figure out what is going on and how best to respond to it (Petty & Cacioppo, 1986; Perloff, 2014). People use attitudes to determine what they like or dislike and what they are for or against, so they can plan their behaviors accordingly (Sherif, 1967). For example, growing up in a household with parents who identify as Republican could influence an individual to hold positive attitudes toward Republican political candidates and negative attitudes toward

Democrats. Those attitudes, in turn, also hold the potential to impact behavior (for instance, voting).

Some attitudes people hold are strong, but others are weak, which means they are more susceptible to influence. This is why mass media can mold or change attitudes

(Perloff, 2014). In fact, communicators spend millions of dollars each year on media campaigns that attempt to change attitudes, through the transmission of a message, about political candidates, consumer products, health practices, charitably causes, etc.

(Perloff, 2014; Petty, Briñol, & Priester, 2009). These campaigns also aim to influence viewer behaviors, such as convince them to vote for the promoted candidate, purchase

the promoted product, adopt the promoted health behavior, or donate to the promoted cause (Petty et al., 2009). However, not all communication-induced attitude changes occur based on overt persuasion attempts. News attention also has the potential to influence people’s attitudes (Kiousis & McCombs, 2004). For example, research has discovered that mass media attention to political issues provides criteria for how people evaluate political figures (Iyengar, 1990; Iyengar & Kinder, 1987; Iyengar, Peters, &

Kinder, 1982; Kiousis & McCombs, 2004; Krosnick & Kinder, 1990).

Rationale for ELM

Several scholars have tried to understand how and when communication successfully leads to attitude change and, as a result, developed theories that have evolved over time and that explain the diverse effects media messages can have on audiences (Petty & Cacioppo, 1986; Perloff, 2014). At first, in the 1920s and ’30s, social scientists believed the effects of mass media were powerful. However, empirical research eventually concluded that the effects of media are not so direct. Instead, research found that media were more successful at reinforcing pre-existing attitudes than creating news ones (Petty et al., 2009). In addition, while media are not all that effective at telling the public what to think, one theory concluded that they are successful at telling people what to think about. This particular theory is known as agenda setting (McCombs & Shaw, 1972).

Agenda setting theory says that mass media can impact audiences in three different ways. The first level of agenda setting argues that media influence what topics the public think about. Therefore, mass media coverage does not necessarily reflect reality but create one because the more media cover a topic, the higher the topic’s salience will be (Weaver, 1984). This aspect of agenda setting focuses on issue

prominence. However, the second level of agenda setting theorizes that mass media influence how people think about these topics (Conway, 2013), or in other words, their attitudes and opinions of them. By highlighting certain aspects of a topic and downplaying or ignoring others, second-level agenda setting posits that journalists can alter how people perceive and evaluate publicized issues (McCombs & Ghanem, 2001;

Valkenburg, Semetko, & DeVreese, 1999). There is also a third level of agenda setting, called network agenda setting, which explains that the co-occurrence of certain topics or attributes of topics in the media can cause those same objects to co-occur in people’s minds, meaning the media can encourage people to think about certain topics or issues together (Guo, Vu, & McCombs, 2012).

Traditionally, researchers use agenda setting theory in order to explain the power mass media can have on public opinion formation (Zoch & Molleda, 2006). However, scholars have also adopted agenda setting to examine mass media’s effects on attitudes (Kiousis & McCombs, 2004). Second-level agenda setting theory could then be applied to this study in order to explain that if mass media provide a personalized experience, cutting people off from diverse information, then media could alter how people perceive and evaluate the world around them. However, the problem with using agenda setting in this case is that it does not take into account how people cognitively process the information they receive. Agenda setting takes a top down approach looking at how mass media affect audiences, but changing the point of view of research to focus on the audience’s perspective is becoming increasingly important in the new media environment because now individuals can filter and customize their own newsreading experiences as well.

Alternatively, there are multiple theories of persuasion that take into account how people process information. One of the first persuasion theories was the Yale attitude change approach (Hovland, Janis, & Kelley, 1953). Because previous research concluded that media were not typically successful at directly influencing audience attitudes, Hovland aimed to uncover potential intervening variables that influenced the relationship between media messages and attitude change (Petty et al., 2009). As a result, he explained that for attitude change to occur, individuals have to attend to, comprehend, learn, accept, and retain the information from a message. The more people comprehend and learn about a message, the more likely they are to have favorable attitudes toward it (Chaiken, Wood, & Eagly, 1996). The problem with the Yale attitude change approach, however, is that it assumes audiences are passive when more evidence exists to see them as active (Perloff, 2014).

The cognitive response approach makes up for this limitation because it suggests that audiences have to respond positively to the message in order to experience attitude change (Brock, 1967; Greenwald, 1968; Petty, Ostrom, & Brock,

1981). This perspective explains that message recipients either have favorable thoughts toward a message (called pro-arguments) or negative ones (counter arguments), but persuasion can only occur when recipients generate pro-arguments (Perloff, 2014). The cognitive response approach advanced understanding of the effects of persuasive messages. However, it assumes people think carefully about a message when at times people tend to be more passive while reading. Sometimes people use mental shortcuts to help them decide whether to adopt an advocated position instead of thoughtful consideration of any arguments. By taking into account there are multiple ways people

process information, the Elaboration Likelihood Model fills this gap (Perloff, 2014; Petty

& Cacioppo, 1986; Petty et al., 2009).

Two Processing Routes

ELM (Petty & Cacioppo, 1981a; Petty & Cacioppo, 1986) states the likelihood of attitude change depends on how much recipients elaborate on a given message.

Elaboration is the extent to which people thoughtfully and carefully consider the information or argument contained in a message (Perloff, 2014; Petty & Cacioppo,

1986). If a message encourages an individual to engage in deeper thinking, then

“elaboration likelihood” is said to be high. People can fall anywhere on a continuum from no thought about the message to complete elaboration of every aspect, but where they fall depends on which cognitive processing route they use (Petty & Cacioppo, 1986;

Petty et al., 2009).

ELM is considered a dual-process model because it identifies two distinct paths recipients use to process information (Perloff, 2014). The central route is when people thoughtfully and carefully consider the information presented in the message, while the peripheral route is when people engage in more automatic or routinized thinking. Which route individuals use depends on their motivation and ability to process the given information (Petty & Cacioppo, 1986), and with each route comes different mechanisms for how communication can lead to attitude change (Perloff, 2014).

The central route is when people engage in deep, systematic, and effortful analysis of a message, and it occurs when motivation and ability to process information is high. In other words, the central route is when people carefully and thoughtfully consider the accuracy of a message. Central-route processing involves allocating cognitive resources toward the content such as attending to the appeal of the message,

accessing from memory any pre-existing attitudes relevant to the information discussed, and scrutinizing the information in the message in order to develop an overall evaluation or attitude toward the content. Sometimes this type of processing is objective, meaning attitudes develop based on the strength of the information and arguments presented in the given content, or sometimes central-route processing is biased, which means the evaluations develop based on an individual’s pre-existing attitudes (Petty & Cacioppo,

1986).

Alternatively, the peripheral route is when people engage in automatic thinking.

Peripheral-route processing occurs because people are not capable of carefully dissecting every message they see nor are they motivated to do so. Therefore, instead of using the merits of an argument to decide whether to adopt an advocated position, individuals using the peripheral route rely on cues or cognitive shortcuts (such as the source is an expert, attractive, or the number of arguments used) to tell them whether to adopt a positive or negative attitude toward the message. When motivation or ability to process information is low, attitudes are likely to change by associating the information presented with one or more of these peripheral cues. For example, an individual may make an inference about the correctness of the message or desirability of an advocated position based on positive or negative characteristics of the message source, such as the source seems like an expert or the source does not seem credible (Petty &

Cacioppo, 1986).

These differences between the central and peripheral routes explain how people cognitively process messages and what aspects of those messages hold the potential to trigger initial attitude changes, but these two routes also have different long-term

impacts on both attitudes and behavior. Because the cues that signal recipients to adopt a favorable attitude within the peripheral route may only be accessed once, they tend to lose their salience over time. Therefore, the peripheral route is less likely to have an enduring impact on attitudes, and it also tends to be less prescient of behavior.

Alternatively, because the central route requires considerably more cognitive work than the peripheral route, attitudes that develop during this type of processing tend to be more enduring. People who engage in central-route processing are more easily able to recall the same attitudes developed over time, which also means they are more likely to act upon them (Petty & Cacioppo, 1986).

ELM is often compared another dual-process model of persuasion introduced by

Chaiken (1980) called the Heuristic Systematic Model (HSM). HSM is similar to ELM in that both pose two routes of persuasion, and which route people use depends on their motivation and ability to process information. The two routes suggested by HSM are called the systematic route, which is when people rely on issue-relevant arguments to decide whether to adopt a position in a message (similar to ELM’s central route), and the heuristic route, which is when people rely on cues or cognitive shortcuts (like the peripheral route). The major difference between ELM and this particular model is that

HSM suggests that the two routes to persuasion are not mutually exclusive. According to HSM, people can rely on arguments as well as cognitive shortcuts to help them decided how to evaluate a message. While these two models are complementary to each other, ELM is favored more in research because it accounts for numerous source, message, and recipient factors that influence an individual’s processing and persuasion

(Perloff, 2014).

Factors Increasing Motivation to Process

According to ELM, several factors determine which processing route individuals take. Some of these factors are situational, and some are related to the individual doing the processing. A difference between individuals that governs how they process information is need for cognition (Petty & Cacioppo, 1986). Need for cognition (NFC) is

“the tendency for an individual to engage in and enjoy thinking” (Cacioppo & Petty,

1982, p. 116). Individuals high in NFC tend to actively seek out information and enjoy effortful thinking, while individuals low in NFC are less motivated to engage in the cognitive effort required to process information (Cacioppo, Petty, Feinstein, & Jarvis,

1996; Cacioppo, Petty, Kao, & Rodriguez, 1986). As a result, individuals high in NFC are more likely to develop attitudes toward a message by scrutinizing the information presented (central-route processing), and individuals low in NFC are more likely to employ the use of heuristics or mental shortcuts to make decisions (peripheral-route processing) (Cacioppo & Petty, 1982; Cacioppo et al., 1986; Cacioppo, Petty, & Morris,

1983; Cacioppo et al., 1996).

As for the situational factors, there are several different elements that can influence how people process messages. For instance, distractions that occur while individuals examine a message may cause them to take the peripheral route over the central route while repeated presentations of a message provide greater opportunities to consider the information, instead encouraging individuals to take the central route over the peripheral one. However, one situational factor that influences elaboration that is of particular interest in this study is personal relevance of the message (Petty &

Cacioppo, 1986). Personal relevance is the extent to which a message has personal meaning or intrinsic importance to an individual (Petty & Cacioppo, 1986; Sherif &

Hovland, 1961; Serif, Kelly, Rodgers, Sarup, & Tittler, 1973). Personal relevance has been found to encourage individuals to signal central-route processing over the peripheral route because as personal relevance increases, people become more motivated to engage with message (Petty & Cacioppo, 1979; Petty & Cacioppo, 1986).

Therefore, the same principle could apply to aggregated news because it is personalized.

Effects of News Aggregation

Conceptualizing Personalization

Although scholars in multiple disciplines refer to personalization by a variety of names, including customization, tailoring, and matching, the fundamental meaning of the concept is reflecting the self (Kalyanaraman & Sundar, 2006; Kalyanaraman &

Wojdynski, 2015; Petty, Barden, & Wheeler, 2002). There is a distinction between personalization (the degree to which something is tailored by a system or interface) and customization (when the user tailors something for themselves) (Sundar & Marathe,

2010). However, an object is personalized or customized when it is one closely tied to an individual’s distinctiveness (Franke & Schreier, 2008; Kalyanaraman & Sundar,

2006). Distinctiveness refers to people’s tendency to differentiate themselves from others in order to establish themselves as unique (Breakwell, 1986; Vignoles,

Chryssochoou, & Breakwell, 2000). People establish their distinctiveness by making decisions related to their individualized interests and tastes, such as choosing what clothes they like to wear, what food they like to eat, and how to style their hair (Fiske,

Kitayama, Markus, & Nisbett, 1998). Therefore, anything that accentuates a person’s tastes could be perceived as personalized (Vignoles et al., 2000).

Personalization and customization have been explored in a number of disciplines and research settings. For example, health communication researchers have studied personalization by tailoring health education materials to one specific person by matching information and healthy behavior strategies toward a person’s specific health goals and current characteristics (weight, height, any medical conditions, etc.) (Kreuter,

Bull, Clark, & Oswald, 1999; Pilling & Brannon, 2007). Scholars studying e-commerce and marketing have operationalized personalization through products that offer customizable characteristics (Valenzuela, Dhar, & Zettelmeyer, 2009; Wind &

Rangaswamy, 2001; Zhang & Wedel, 2009), and information and computer science researchers have examined customization through Web interfaces that can be modified by individuals according to their tastes (Kobsa, Koenemann, & Pohl, 2001). As for journalism research, the concepts have been explored through personalized news

(Beam, 2014; Kalyanaraman & Sundar, 2006; Liang, Lai, & Ku, 2007; Sela, Lavie, Inbar,

Oppenheim, & Meyer, 2015).

News is a malleable concept that has evolved over time. Traditionally news was defined as any information the public needs to know. Then it was expanded to include any events out of the ordinary. Furthermore, due to economic and rating pressures, instead of just covering news the public needs to know (civic affairs news), news also consists of stories that entertain rather than inform (Scott & Gobetz, 1992).

Personalized news, then, is any news story depicting content matched to some aspect of the self. A news story is personalized when it is intended for an individual reader and is matched to the reader’s individualized interests and needs (Kalyanaraman & Sundar,

2006; Kalyanaraman & Wojdynski, 2015; Petty et al., 2002; Sela et al., 2015). News

aggregators generate personalized news by compiling headlines and news stories based on the search history, click behavior, and geographic location of individual users

(Liu et al., 2010; Xiao & Benbasat, 2007).

Effects of Personalization on Attitudes

When messages are closely linked to the self, they can have persuasive impacts on people’s attitudes. Because personalization represents an individual, it can cause a sense of congruence to develop between the user and the personalized object, which in turn increases the user’s favorability toward the object (Kalyanaraman & Sundar, 2006).

As a result, research has consistently found that people exhibit more positive attitudes toward personalized messages, interfaces, and products in comparison to objects that are not personalized (Kalyanaraman & Sundar, 2006; Petty, Wheeler, & Bizer, 2000;

Petty et al., 2002).

For example, in a health communication research study about weight-loss education, Kreuter et al. (1999) found that participants who received personalized health education materials exhibited more favorable attitudes toward the information presented than those who received standard printed materials from the American Heart

Association. Similarly, Blom and Monk (2003) concluded that participants given the opportunity to customize their computers and mobile phones developed more positive associations toward their devices. Both Saadeghvaziri and Hosseini (2011) as well as

Xu (2006) found that personalization improved people’s attitudes toward mobile advertisements, and research has also suggested that consumers generally enjoy buying customizable products and hold positive evaluations toward those products

(Goldsmith & Freiden, 2004). From a news perspective, Kalyanaraman and Sundar

(2006) examined the psychological appeal of personalized news content by evaluating

participant attitudes toward the MyYahoo! news portal. They found that the highly personalized portal yielded the most favorable attitudes toward the website while the low personalization condition fostered the least favorability.

Follow-up research studies have produced consistent findings. For example,

Sela et al. (2015) assessed attitudes toward personalized versus non-personalized news by exposing participants to a group of news stories over the course of six experimental sessions. Participants either saw a series of non-personalized stories each day (standard condition), a series of stories personalized for users based on interests they indicated during a pre-questionnaire (personalized condition), or a series of stories personalized according the pre-questionnaire and then updated half way through the experiment to be adaptive to participants’ changing interests over time

(adaptive condition). Similarly, Liang et al. (2007) analyzed user satisfaction with personalized news by designing three websites, one showing headlines chosen by an editor, one that displayed personalized news recommendations according to participants’ indicated interests, and one that used an algorithm to provide personalized news options based on past reading behavior. For both studies, overall results revealed that users exhibited more favorable attitudes toward the personalized news conditions than the non-personalized ones (Liang et al., 2007; Sela et al., 2015).

Effects of Personalization on Information-Seeking Behavior

Personalization also holds the potential to cause changes in behavior. As previously mentioned, a consistency between attitudes and behavior often exists because attitudes help people figure out how best to respond to communication and situations they are exposed to (Petty & Cacioppo, 1986; Perloff, 2014; Sherif, 1967). As

a result, studies examining personalization have uncovered connections between the attitudes elicited and behavior.

For instance, in regards to health, research revealed that personalized health education materials helped recipients quit smoking (Strecher et al., 1994), eat healthier

(Brug, Steenhaus, Van Assema, & de Vries, 1996; Campbell et al., 1994), increase physical activity (Bull, Kreuter, & Scharff, 1999; Kreuter & Strecher, 1996), get mammograms (Skinner, Strecher, & Hospers, 1994), test their cholesterol (Kreuter &

Strecher, 1996), as well as change inaccurate perceptions about cancer and strokes

(Kreuter & Strecher, 1995). Education scholarship found that personalized instruction, such as using word math problems that exhibit personal pronouns and personalized examples, drew more positive attitudes and were associated with higher achievement on tests (Chen & Liu, 2011; López & Sullivan, 1992), and according to research, personalized recruitment emails help increase survey response rates (Heerwegh, 2005;

Heerwegh, Vanhove, Matthijs, & Loosveldt, 2005; Joinson & Reips, 2004; Pearson &

Levine, 2003).

While most of these behaviors can be seen as positive, some are concerned that personalized news could have negative consequences on behavior such as narrowing information-seeking behavior (Beam, 2014). These fears stem from research on selective exposure, which generally finds that people prefer to read attitude-consistent information over information that counters pre-existing opinions and beliefs (Frey, 1986;

Garrett, 2009a, 2009b; Hart, et al., 2009; Iyengar & Hahn, 2009; Knobloch-Westerwick

& Meng, 2009; Sears & Freedman, 1967; Stroud, 2010; Sweeney & Gruber, 1984).

Research also suggests that personalized news provided by news aggregators pre-

disposes readers to selective exposure because alternative information is not as readily visible (Beam, 2014; Pariser, 2011; Sunstein, 2001).

While this is a common concern (Pariser, 2011; Sunstein, 2001), only a few studies have addressed the effects of personalized news on information-seeking behavior (Beam, 2014; Beam & Kosicki, 2014; Kalyanaraman & Sundar, 2006). For example, Kalyanaraman and Sundar (2006) examined the effects of personalized news on news-reading behavior by tracking participants’ information-seeking behavior online after they read personalized news stories. They found that participants in the high personalization condition spent more time on the MyYahoo! page in lieu of visiting other websites, which the authors suggested supports the notion that personalized news leads to increased selective exposure. Similarly, Beam (2014) concluded that personalized news recommendation systems promote news selectivity because in his study, participants exposed to news pages containing personalized news recommendations read significantly less counter-attitudinal information than those who saw a generic condition.

Because research consistently indicates that people prefer products, messages, and experiences that are closely tied with themselves in comparison to those that are not (Blom & Monk, 2003; Goldsmith & Freiden, 2004; Kalyanaraman & Sundar, 2006;

Kreuter et al., 1999; Liang et al., 2007; Petty et al., 2000; Petty et al., 2002;

Saadeghvaziri & Hosseini, 2011; Sela et al., 2015; Xu, 2006), there is enough evidence to anticipate the same effect in this study. Additionally, because of the findings from studies related to the effects of personalized news on information-seeking behavior

(Beam, 2014; Kalyanaraman & Sundar, 2006) as well as a long history of research on

selective exposure indicating that people prefer to view information that supports their own interests and perspectives (Frey, 1986; Garrett, 2009a, 2009b; Hart et al., 2009;

Iyengar & Hahn, 2009; Knobloch-Westerwick & Meng, 2009; Sears & Freedman, 1967;

Stroud, 2010; Sweeney & Gruber, 1984), it is also reasonable to suspect that aggregated news would have a narrowing impact on users’ information-seeking behavior. However, variations in results may arise depending on the type of aggregator

(or source of aggregation) people use.

Conceptualizing Different Types of Aggregators

A source is commonly conceptualized in research as the originator of communication (Rogers & Kincaid, 1981; Severin & Tankard, 1988; Shannon & Weaver,

1949). This is different from the dictionary definition, which describes a source as “a person, book, etc., that gives information” (Merriam-Webster). The problem with the dictionary’s definition is that it leaves the source of a message open for interpretation. A source can refer to the sender of the message (an editor, reporter, friend, etc.) or the platform or channel through which a reader receives a message (such as a newspaper, magazine, computer, or mobile phone) (Sundar & Nass, 2001). For this study, a source is conceptualized as the originator of the message, but the focus is on different types of online news aggregators that provide personalized news recommendations to users.

Based on a typology from Sundar and Nass (2001), the first news source is the gatekeeper. This is when editors and journalists perform the filtering function of news

(Rosten, 1937; Schramm, 1949; Sundar & Nass, 2001). Readers typically see this source through bylines (if the gatekeeper is a reporter) or in the form of a masthead or company logo (if the gatekeeper is the editorial staff) (Sundar & Nass, 2001). However, there are also aggregators that use editors to curate and personalize news content. For

example, the Drudge Report uses editors to curate links from news organizations targeted toward a conservative audience (Levitt & Rosch, 2006). Similarly, Wildcard is a news aggregating app that use editors to curate and summarize content, which is then personalized by connecting with individual users’ Twitter accounts (Ciobanu, 2015).

The second news source consists of any technologies (such as news websites and apps) that provide news (Sundar & Nass, 2001). While technology would typically be considered as a channel through which sources deliver news more so than an actual news source itself, some news aggregators are able to dictate the type of content delivered, which gives technology the opportunity to act as both the sender as well as the channel (Liu et al., 2010; Sundar & Nass, 2001). More specifically, a lot of aggregators, like Google News, use an algorithm to analyze an individual’s search history, click behavior, and geographic location to provide a list of personalized content recommendations. Google News also allows users to customize their news feeds by picking subject areas of interest like world news, entertainment, and/or sports under the

“Personalize Google News” tab) (Liu et al., 2010).

This leads to the third news source because receivers are now considered a source of communication. In addition to receiving news from gatekeepers or online interfaces, the Internet allows users to choose what to read for themselves

(customization) and their friends (Sundar & Nass, 2001). Individuals can exert control over the content recommendations they receive either by choosing what stories to read for themselves or by selecting subject areas of interest on online news websites and aggregators to help facilitate the process (Liu et al., 2010). Similarly, users frequently share news with their friends and family via social media like Facebook, which means

that other users can act as sources of information (Sundar & Nass, 2001; Turcotte et al.,

2015). Although less is known about how its algorithm compiles information from users’ friends, groups, and the pages they like (Oremus, 2016), Facebook also allows users to engage some customization by adding or subtract “friends” and engaging in behaviors

(such as liking news organizations) that send signals to the algorithm.

Effects of Different Types of Aggregators

Sources like these can provide cues that guide how people think about a message and whether they should accept or reject it (Petty & Cacioppo, 1984). In terms of which sources would provide cues to adopt a positive attitude toward a message, aggregation that uses a social recommendation is expected to have the greatest impact because the closeness (homophily) that exists between individuals and their friends and family is one mass media cannot match. Friends and family are likely to understand a person and his or her interests better than an aggregator or gatekeeper will. Thus, interpersonal communication is usually trusted more than is media (Katz & Lazarsfeld,

1955), and the same principle applies when it comes to social interaction in an online environment. For example, in their study of social versus gatekeeper recommendations,

Turcotte et al. (2015) found that readers indicated higher levels of trust for news stories recommended by a friend through social media than story recommendations from a news outlet.

Furthermore, aggregators using an algorithm instead of an editor will likely be perceived as more credible because the algorithms they use are removed from human intervention (Carlson, 2007). Public trust toward news organizations is declining

(Gronke & Cook, 2007, Pew Research Center, 2012). Aggregators, however, are a trusted alternative because they provide content recommendations like news

organizations do, but the algorithms they use are seen as unbiased. Additionally, because the recommendations are based on factors such as past reading behavior, perceptions of personal relevance are likely to increase because the personalization is closer to what individuals would customize for themselves in comparison to what a human editor could determine (Carlson, 2007).

As a result, aggregators that use social recommendations to provide personalized news are expected to draw the most favorable attitudes as well as trigger the most narrowing impacts on information-seeking behavior.

Hypothesis 1: Aggregators that employ the use of social recommendations lead users to hold a more positive attitude toward the news content than other types of aggregators.

Hypothesis 2: Aggregators that employ the use of social recommendations are more likely than other types of aggregators to lead users to search for information on similar topics.

Factors Influencing People’s Responses to News Aggregators

Cognitive Elaboration

While academic literature supports the notion that personalized messages, including those provided by new aggregators, have a tendency to prompt more favorable attitudes as well as induce behavior, what is not quite clear is why personalization has such a strong impact on attitudes and behavior. Reasons suspected for a consistency in research findings related to personalization goes back to the roots of ELM, as research has shown that tailored messages first lead to greater elaboration before inducing positive attitudes (Kalyanaraman & Sundar, 2006; Kreuter et al., 1999;

Petty & Cacioppo, 1979; Petty, Cacioppo, & Haugtvedt, 1992; Petty et al., 2000; Tam &

Ho, 2005).

As previously mentioned, cognitive elaboration is the extent to which people thoughtfully consider the information contained in a message (Perloff, 2014; Petty &

Cacioppo, 1986). When messages recipients engage in deeper, more elaborate thinking, they are using central-route processing, which, according to ELM, has more enduring effects on both attitudes and behaviors (Petty & Cacioppo, 1986).

Personalization is a contributor to greater elaboration because as personal relevance increases, people become more motivated to engage with the message (Petty &

Cacioppo, 1979; Petty & Cacioppo, 1986). As a result, research suggests that personalized messages induce deeper cognitive processing as long as no other variable pre-disposes readers to choosing one route over the other (Kreuter et al., 1999;

Petty & Cacioppo, 1979; Petty et al., 1992; Petty et al., 2000; Tam & Ho, 2005).

For example, Kreuter et al. (1999) found that tailored health messages significantly increased participants’ chances of thoughtfully considering the information presented. Those thoughts were also associated with more positive attitudes toward the weight loss education materials. Similarly, in their study about ring tone downloads,

Tam and Ho (2005) discovered that participants given a list of personalized ringtone recommendations based on their favorite singers spent more time considering the ring tones suggested (by listening to the options provided) than were participants who received a randomized list of ringtone recommendations. The researchers also found that participants who experienced heightened elaboration were more likely to accept the top recommendation for their free ringtone offered during the experiment. As a result, cognitive elaboration is expected to trigger people’s responses to aggregated news in this case as well.

Hypothesis 3: Greater cognitive elaboration leads users to hold a more positive attitude toward the different types of aggregators.

Perceived Credibility

Another factor that could influence an aggregator’s likelihood of having an effect on attitudes is the perceived credibility of a message (Meyer, 1988). According to ELM, credibility is a major driver of attitudes and behavior because people usually desire to hold accurate attitudes in order to avoid wrongful decisions (Festinger, 1950; Petty &

Cacioppo, 1986; Petty et al., 2000). This is why when it comes to processing information, credibility is a factor that influences attitudes no matter whether elaboration likelihood is high or low. When people are motivated and able to process information through the central route, an attitude is likely to be shaped by the merits of the arguments. Alternatively, when people are less motivated, triggering the peripheral route, an attitude might be based on a cue, such as “the source seems like an expert” or

“there are a lot of arguments in this message, so it must be credible” (Petty & Cacioppo,

1986). Therefore, perceived credibility is also expected to be a driver of people’s responses to aggregated news.

Hypothesis 4: Greater perceived credibility leads users to hold a more positive attitude toward the different types of aggregators.

Effects of Different Types of News

Conceptualizing Different Types of News

While certain types of aggregators are anticipated to draw more favorable attitudes as well as some behavioral implications (with the help of these intervening factors), variations in results may arise depending on the types of news people read.

When people read news, they are not always exposed to the same news topics or subject areas, and a story that is entertaining versus a story that provides learning value

could solicit different attitudinal and behavioral responses. As previously mentioned, whether someone engages in central-route processing depends on an individual’s motivation to do so (Petty & Cacioppo, 1986), and according to uses and gratifications, different motivations drive how individuals use mass media. Therefore, individual motivations influence the different styles of news people read (Blumler, 1979).

Uses and gratifications is a perspective that focuses on individual media use, and it asserts that people use media for many different purposes (Ko, Cho, & Roberts, 2005;

Severin & Tankard, 1997). More specifically, uses and gratifications explains that audiences actively decide what news to read or view by basing their choices on needs, motivations, and goals (Blumler, 1979; Katz, Blumler, & Gurevitch, 1974; Ko et al.,

2005; Lin, 1999; Rubin, 1994). Furthermore, audience members are aware of their motivations, and they choose media that hold the potential to gratify those needs (Katz et al., 1974). These psychological considerations include: utility (use or value), selectivity (pre-existing interests and opinions), and intentionality (individual motivations)

(Bauer, 1964; Blumler, 1979).

As for these individual motivations, there are several that drive news-reading behavior, but through a series of typologies, research has narrowed these motives to four categories (Blumler, 1979; Katz, Gurevitch, & Hass, 1973; McQuail, Blumler, &

Brown, 1972). These categories are: surveillance (information about society and the wider world around them), diversion (relief from boredom or stress in everyday lives), personal identity (related to their life), and personal relationships (their social situations)

(Becker, 1979; Blumler, 1979; Katz, Blumler, & Gurevitch, 1973; McLeod & Becker,

1981). These motivations not only drive what news topics people read, but they also

pose different possibilities for how media can affect audiences’ cognitions, attitudes, and behaviors (Blumler, 1979).

The most common driver of news-reading behavior is the surveillance gratification. Surveillance news encompasses topics that are cognitively oriented, including news about politics, current events, and topics that depict information about the world or an individual’s surroundings. Second, news that fulfills the diversion function consists of topics that provide entertainment value, such as news about TV shows, movies, music, and sports. Third are news topics that appeal to readers’ personal identities by reflecting the self, personal interests, opinions, and values. And fourth are news stories appealing to people’s personal relationships (Blumler, 1979;

Katz et al., 1973; McGuire, 1974; McQuail et al., 1972; McQuail, 2010).

Responses to Different Types of News

Just as personalized news has been found to lead to greater elaboration over non-personalized content (Kreuter et al., 1999; Petty & Cacioppo, 1979; Petty et al.,

1992; Petty et al., 2000; Tam & Ho, 2005), research also suggests that news fulfilling surveillance and diversion functions signal differing levels of cognitive processing.

Surveillance gratifications lead newsreaders to engage in more effortful thinking because this motivation helps them gain knowledge and achieve their learning goals

(Blumler, 1979; Eveland, 2001). The diversion function, on the other hand, does not evoke high levels of learning or elaboration because individuals using this gratification are more motivated by entertainment than learning value (Gantz, 1978; Katz et al.,

1973; McLeod & McDonald, 1985; Neuman, 1976; Perse, 1990). As a result, news stories that trigger diversion motivations would likely signal peripheral-route processing over the central route.

Furthermore, because surveillance gratifications lead to greater cognitive processing, as ELM stipulates, news fulfilling surveillance motivations could also have greater impacts on attitudes and behavior than news fulfilling diversion motivations

(Petty & Cacioppo, 1986). Consistent with ELM, research has suggested that news stories that meet surveillance gratifications spark greater interest and higher favorability from readers because they are more involved in the news-reading process. This is different from diversion news, which is more relaxing and passive. In turn, research also discovered that surveillance news leads to certain behavioral responses, such as increasing readers’ likelihood to vote (Becker, 1976; Garramone, 1985; Kaye &

Johnson, 2002). In terms of information-seeking behavior, research concluded that readers who are more involved with their news content, which the surveillance gratification motivates readers to do, are more likely to seek out additional related information (Kaye & Johnson, 2002; Tan, 1980).

As a result of these findings (Becker, 1976; Garramone, 1985; Kaye & Johnson,

2002; Tan, 1980), news stories fulfilling surveillance gratifications (civic affairs news) are anticipated to have a greater impact on attitudes and information-seeking behavior than are news stories fulfilling diversion gratifications (entertainment news).

Hypothesis 5: Civic affairs news leads users to hold a more positive attitude toward the news content than entertainment news.

Hypothesis 6: Civic affairs news is more likely than is entertainment news to lead users to search for information on similar topics.

Factors Influencing People’s Responses to Different Types of News

Surveillance motivations have been found to lead to greater cognitive elaboration in comparison to content that fulfills diversion gratifications (Blumler, 1979; Eveland,

2001), which means, as specified by ELM, that both cognitive elaboration and perceived

credibility could have an effect on responses to different types of news. As previously mentioned, credibility is an important factor that guides individuals’ processing and attitudes because people usually desire to hold accurate attitudes (Festinger, 1950;

Petty & Cacioppo, 1986; Petty et al., 2000). Therefore, in addition to cognitive elaboration, perceived credibility, too, could have an impact on results, thus prompting the following hypotheses:

Hypothesis 7: Greater cognitive elaboration leads users to hold a more positive attitude toward the different types of news.

Hypothesis 8: Greater perceived credibility leads users to hold a more positive attitude toward the different types of news.

Interaction of Different Types of Aggregators and Different Types of News

As for the interaction of the different types of aggregators with different types of news (civic affairs or entertainment), evidence cited earlier supports an expectation that civic affairs news aggregated using social recommendations would have the greatest impact. Furthermore, research also suggests that under high elaboration conditions (as is the case with surveillance news), the source can aid the confidence with which people hold certain attitudes. More specifically, when people engage in central-route processing, the credibility of the source can help support attitudes developed based on the content contained in the message (Tormala, Briñol, & Petty, 2007). Because users are likely to trust news from friends or family over an aggregator, editors, and journalists

(Epstein, 2016), surveillance news shared by social recommendations should then have the biggest impact on attitudes and information-seeking behavior, as specified by the following hypotheses:

Hypothesis 9: Aggregators that employ the use of social recommendations to share civic affairs news lead users to hold the most positive attitude.

Hypothesis 10: Aggregators that employ the use of social recommendations to share civic affairs news are most likely to lead users to search for information on similar topics.

CHAPTER 3 METHODS

Overview

To test the hypotheses in this study, a factorial-designed experiment was conducted. A factorial-designed experiment is when each level of one treatment variable is combined with each level of the other. It allows an experiment to test the effects of each independent variable separately as well as the effects of the variables combined (Keppel & Wickens, 2004). This experiment was a 4 (type of aggregator: an aggregator that uses an algorithm to provide personalized news recommendations like

Google News, an aggregator that uses on social connections to provide personalized news recommendations like Facebook, an aggregator that use editors to provide personalized news recommendations like inshorts, and no aggregation) x 2 (type of news: civic affairs or entertainment) used to determine which aggregators and news types had the greatest impacts on attitude and information-seeking behavior.

Rationale for Experimental Method

An experiment was chosen as the method for this study because of its ability to determine causation (Keppel & Wickens, 2004; Shadish, Cook, & Campbell, 2002). In other words, experiments can determine whether treatment variables actually cause or produce an effect or change in the measured dependent variables (Shadish et al.,

2002), thus allowing this study to test whether different types of aggregators providing news related to civic affairs or entertainment caused changes in attitude and information-seeking behavior. This is different from surveys, which are generally used for correlation rather than causation. A correlation is an association between variables found by examining relationships between two or more variables (Wheeler, 2015).

Finding these changes, however, does not mean a cause and effect relationship exists

(Shadish et al., 2002). According to John Stuart Mill (1906), causal relationships only occur when the following three specifications are met: (1) the cause must precede the effect, (2) the cause must be related to the effect, and (3) any alternative explanations for the effect, other than the cause, must be eliminated.

Experiments can determine causation by accomplishing all three of these objectives. First, after deliberately varying the independent or treatment variables (in this case, the type of aggregator and type of news) and exposing the different conditions to participants, researchers can then assess whether the manipulations caused changes in the dependent variables afterward (such as attitude and information- seeking behavior) (Shadish et al., 2002). Second, researchers are able to conclude the treatment and changes in the dependent variables are related by comparing the results from the treatment groups to each other as well as to a control group, which is a group of participants who do not see an experimental manipulation. For example, in this case, some participants saw a condition depicting a news website instead of an aggregator. If there are differences in the results between the treatments and control group, then it can be concluded that the treatment was causal (Keppel & Wickens, 2004; Shadish et al., 2002). And third, in order to reduce the possibility of alternative explanations, it is important to identify and deal with any additional variables that also hold potential to cause an impact (Shadish et al., 2002).

These are called nuisance variables because they can cause variations in the dependent variables, even though they are not of interest to include as a treatment in a given study. If nuisance variables have an effect on the dependent variables, they may

have a confounding effect, which means they alter the apparent effects of the treatment.

In order to reduce the likelihood of confounding effects from occurring, steps such as random assignment and using a between-subjects design help reduce differences between participants in each condition as well as help suppress nuisance variables that could alter comparisons between conditions (Keppel & Wickens, 2004). The degree to which these steps are successful increases the internal validity of the study so the manipulation of the independent variables can be attributed for the found effects

(Moutinho & Hutcheson, 2011).

The most effective way to increase internal validity and reduce the differences between participants in each condition is through random assignment, or assigning each participant to a condition at random by flipping a coin or randomly drawing numbers (Keppel & Wickens, 2004). The participants chosen to take part in any given experiment will vary in factors such as political affiliation, age, background, and experience. Randomization helps reduce the chance these differences between participants will have an impact because there is an equal chance of assigning people with varying characteristics to each group (Keppel & Wickens, 2004; Shadish et al.,

2002). For example, if some participants in this study had experience using aggregators and were, therefore, more familiar with reading personalized news, that could have had an impact on results. However, through random assignment, participants with a higher familiarity were just as likely to be assigned to the aggregation treatments as the control group. As a result, randomization increases the likelihood that differences among participants are roughly equivalent across all of the different conditions (Keppel &

Wickens, 2004), which in turn boosts the probability that the differences measured at

the end of the study are due to the treatment and not to individual differences between participants (Shadish et al., 2002), thus increasing internal validity.

Another way to reduce the likelihood of nuisance variables is employing the use of a between-subjects experimental design. A between-subjects design is when participants in an experiment only see one of the different conditions. This is different from a within-subjects design, where each participant is exposed to all of the conditions.

Using a within-subjects design reduces the likelihood that individual differences between participants will have an impact on results because the same people participate in each group. However, a within-subjects design poses a nuisance variable that is not a consideration when using a between-subjects design: the order in which the conditions are presented. Differences in scores for the dependent measures could arise based on the order in which participants receive the various treatments. For example, a participant could become tired or bored after the first condition, causing a drop in performance when exposed to additional ones. Alternatively, a participant could become better at the task or at following instructions over time, thus improving performance

(Keppel & Wickens, 2004). This study employed the use of random assignment and a between-subjects design in order to reduce the possibility of both individual differences between participants and nuisance variables altering comparisons between conditions.

Limitations of Experiments

While determining whether a causal relationship exists is a benefit of experiments, one limitation of experiments as well as other methods is they cannot explain why the relationship exists. However, experiments can offer explanation by uncovering links in the chain between an independent and dependent variable (Shadish et al. 2002). This study added to the ability of the experiment proposed in this chapter

by measuring other variables, such as cognitive elaboration and perceived credibility, that could potentially influence participants’ information-seeking behavior and attitude responses to the different types of aggregators and different types of news.

Another concern about experiments is their external validity or generalizability.

Experiments can only measure the effects of variables that are manipulated usually during one particular instance in a restricted experimental setting (Shadish et al., 2002).

They also tend to rely on a convenience sample, meaning the sample consists of participants from the population of interest, but they were not obtained through a random sample (Keppel & Wickens, 2004; Shadish et al., 2002). Therefore, experiments are often limited to the sample, and findings may not be generalizable to a larger population (Moutinho & Hutcheson, 2011; Shadish et al., 2002).

Participants

College students were the target population for this study not only because they are young adults who are used to receiving news recommendations from aggregators but also because they already experience a constricted worldview as a result of being on a residential college campus, itself a type of bubble, which means they are more susceptible to limiting their news-reading habits after exposure to personalized aggregated news (American Press Institute, 2015; Raine & Purcell, 2010). Although this study did not use a random sample, participants were a convenience sample of undergraduate students from the University of Florida recruited from a variety of courses in order to inject some homogeneity into the sample. Additionally, in order to increase the likelihood of participation, extra credit was awarded at their instructors’ discretion. No other incentives were offered for participation.

In order to determine the number of participants needed for this experiment, a power analysis for an ANOVA (the primary statistical test used in this study) was conducted in G*Power using an alpha of .05, a power of .80, and a medium effect size

(.25). Based on these assumptions, G*Power suggested a sample size of 240 participants (30 per condition) (Faul, Erdfelder, Buchner, & Lang, 2013). However, extra participants were recruited just in case some responses needed to be eliminated from final data analysis (for instance, in case someone decided not to finish the study). In total, 280 students (35 per condition) participated.

Procedure

After approval by the UF Institutional Review Board 02 governing the social sciences, the experiment was administered in the College of Journalism and

Communication research lab at the University of Florida between March 31 and April

22, 2016. Upon entering the lab, participants sat down in front of a computer screen with a Qualtrics survey open on the desktop containing both the stimulus materials for each condition and a questionnaire. As this was a between-subject experiment, participants only saw one of the conditions. Which one participants were exposed to was randomly determined by Qualtrics. Therefore, the experimenter was unaware of which condition participants saw.

After reading the informed consent on the first page of the survey (available in

Appendix A), the experimenter gave instructions to the participants and asked them to provide their honest opinions. Then participants were taken through one of each of the following experimental manipulations before seeing questions for the dependent variables, intervening variables, manipulations checks, and some demographic questions. To avoid affecting the results, the experimenter tried to look busy in the front

of the lab but noted that students took their participation seriously because there was little to no talking between participants during experimental sessions, and they stayed off their cell phones.

Stimulus Materials

Type of News Manipulations

The first experimental manipulation signaled to participants was the type of news manipulations: civic affairs news or entertainment news. Subjects in each condition

(except the control groups) were asked: “Looking at the following list of topics, which ones would you normally be interested in reading when you are using news to learn about current events and what is going on in the world around you?” Or they were asked: “Looking at the following list of topics, which ones would you normally be interested in reading when you are using news for entertainment?” Qualtrics randomly decided which of these two questions participants saw.

Following these questions, there was a list of news topics. For each topic, participants indicated their degree of interest by ranking them in order of 1 through 5, where “1” meant “most likely to read” and “5” meant “least likely to read.” Participants were only allowed to enter each number once. The topic options for the informative news conditions were: traffic and weather, jobs and unemployment rates, crime and public safety, national politics, and technology and science. The topics for the entertainment news conditions were: music, movies, and TV shows, celebrities, sports, cooking, and fitness and health. These topics were chosen because young adults say they typically follow them on a regular basis (American Press Institute, 2015).

Aggregation Manipulations

In the treatment conditions, after participants ranked the news topics in order of preference, they were randomly shown a web page designed to mimic one of the following aggregators: Google News, Facebook, or inshorts. While literature conceptualizes the different news sources as editors and reporters, technology, and other users (Sundar & Nass, 2001), the aggregators chosen to represent each of these sources was based on an environmental scan of the types of aggregators that existed as of 2016 on the Internet.

Google News and Facebook were chosen as the algorithm- and social- recommendation aggregators because they are among the most popular news websites in the world (Ingram, 2012; Pew Research Center, 2014), and although it is not as well known, inshorts was chosen for the editor-gatekeeper aggregator because it is an aggregating website, not app (unlike Wildcard), and it is directed toward a broader audience in comparison to sites like The Drudge Report (Ciobanu, 2015; Levitt &

Rosch, 2006). Despite the difficulty of finding an editor-gatekeeper aggregator (because not many exist yet), they still posed an important type of aggregator to include in this study because they may have more influence in the future. Therefore, they served as an important contrast to the other types of aggregation.

To further imitate how these websites look and operate, each of the aggregating web pages contained five headlines, all pertaining to the subject area that elicited the highest degree of interest for each participant. The story and headlines shown remained consistent across all the treatment groups. After seeing one of these sites, participants were then instructed to click the first headline shown and read the designated story (the stimulus), which was displayed as if it came from The Gainesville Sun. The Gainesville

Sun was chosen as the site to display each story as well as for the control group because it is a news website students at the University of Florida would likely be referred to. For the control group, instead of having the participants indicate their interests, they were taken directly to The Gainesville Sun stimulus site, where all five of either the civic affairs or entertainment news headlines were shown. In this case, participants were instructed to choose and read one of the headlines. To see what the website designs looked like for each of the aggregators and the control group, see

Appendices B through E.

Next, to account for the personalization aspect of online news aggregators, the stories and headlines shown targeted the population’s age group and location in order to increase personal relevance. For the civic affairs news conditions, the news stories participants read pertained to the following topics: parking issues in Gainesville, industries likely to hire young college graduates, crime and public safety issues at the

University of Florida, expectations for the presidential general election in Florida, and privacy issues with mobile technology sourcing UF experts. For the entertainment news conditions, the topics were: a review of the reality TV show Gainesville, a nearby celebrity appearance, Florida Gators sports, dorm room cooking tips, and fitness plans for college students. The same stories were shown in the control groups in order to examine how the different types of aggregators affected attitude and information- seeking behavior in comparison to a single news source, regardless of which topic participants chose.

In order to decrease the likelihood that other factors had an influence on participants’ responses, the topic was the only thing manipulated. The stories were

similar in length and were laid out in the same manner with a headline, byline, photo, photo caption, and text. In addition, since the stories were made to appear as if they came from The Gainesville Sun’s website, the top of each page contained a set of menu bar tabs, and advertisements appeared alongside the stories. To see the specific stories used for each news topic, see Appendix F.

Manipulation Checks

Manipulation checks were used to check the efficacy of the treatments.

Manipulation checks assess whether the independent variables (in this case, type of news and type of aggregator) were manipulated successfully (O’Keefe, 2003). In order to check the type of news manipulations, participants were asked their satisfaction with their given news story in terms of its entertainment value or information value. This was assessed by asking participants to indicate whether they believed their given story was more entertaining or informative on a scale of 1 through 7, where “1” described

“entertaining” and “7” described “informative.” For the type of aggregator manipulation check, participants were asked to fill in the blank for the following question: “What was the name of the website that referred you to the story you just read?” This manipulation check was adopted from Sundar and Nass (2001) and served as a screening device to accept or reject a participant before conducting final data analysis.

Dependent Variables

Another important factor to consider in research, regardless of methodology, is construct validity. Construct validity is the extent to which a variable accurately represents the different constructs (Shadish et al., 2002). To help increase construct validity in this study, each variable measured (attitude, information-seeking behavior, cognitive elaboration, and perceived credibility) was operationalized in a manner well

documented in research with high internal reliability scores. Reliability refers to the extent to which measures are internally consistent. If a scale is not internally consistent, it could mean that some items are not assessing the same concept as the rest of the measures (Kline, 1998).

Attitude toward the Content

Attitude toward the content was measured by asking participants to rate how well a series of 12 adjectives (appealing, useful, positive, good, favorable, attractive, exciting, pleasant, likeable, high quality, interesting, and sophisticated) described the news story they read on a 7-point Likert scale, where “1” described “very poorly” and “7” described “very well.” These items were adapted from Sundar and Kalyanaraman’s

(2004) website perception scale because they have been applied to several studies examining human-computer interaction and personalization (Kalyanaraman & Sundar,

2006; Wojdynski, 2014; Wojdynski & Kalyanaraman, 2015). Also, the scale typically yields high construct reliability scores with alphas ranging from .89 to .96 (Sundar &

Kalyanaraman, 2004; Wojdynski & Kalyanaraman, 2015).

Information-Seeking Behavior

After reading their designated stories, participants were asked to spend five minutes browsing the Web in order to track information-seeking behavior. In order to prevent participants from using that time to check their email or social media sites like

Facebook, they were asked to limit their browsing to Google News. Information-seeking behavior was measured (using user-tracking software) by recording the browsing behavior of participants during the experimental sessions. The time spent reading news stories was tracked and categorized as related or unrelated to what they read in terms of subject area in order to examine whether aggregated news encourages participants

to isolate their information seeking to similar topics. The subject areas included: traffic and weather, jobs and unemployment rates, crime and public safety, national politics, technology and science, music, movies, and TV shows, celebrities, sports, cooking, and fitness and health.

Intervening Variables

Cognitive Elaboration

For the purposes of testing ELM (Petty & Cacioppo, 1986), how much participants elaborated on their given news story was measured using a thought-listing procedure that entailed asking participants to record the thoughts they had while reading their designated news stories. Participants were given the following instructions:

We are interested in what went through your mind during the news story you just read. You might have had thoughts related to the news story and some not related at all. Just simply indicate the thoughts you had while reading in the following questions.

Then, participants were taken through three fill-in-the-blank questions asking them to indicate the first, second, and third thoughts they had while reading. After the third question, participants were asked if they had any additional thoughts while reading (yes or no). If yes, they were taken through additional fill-in-the-blank questions until they indicated they had no more thoughts to share or they reached the maximum of 12 thoughts. This method of measuring cognitive elaboration was adopted from Brock

(1967), Greenwald (1968), and Petty and Cacioppo (1981b) because it is a well- documented method used in research to test ELM, and it records an individual’s thought processes (Petty and Cacioppo, 1981b).

Furthermore, to assess whether they thoughtfully considered the message of the story or whether their thoughts branched off into other topics, participants were also

asked to rate how related or unrelated their thoughts were to the story they read. More specifically, after each fill-in-the-blank question, participants were asked to describe each of their thoughts on a scale of 1 through 7, where “1” described “very unrelated” and “7” described “very related” to their given news story. This method of evaluating thoughts recorded during the thought-listing procedure was adopted from Cacioppo,

Harkins, and Petty (1981) and Kreuter et al. (1999).

Perceived Credibility

Perceived credibility was measured by asking participants to rate their level of agreement on a scale of 1 through 7, where “1” described “strongly disagree” and “7” described “strongly agree,” with six statements. Examples of the statements include: “I found the information presented in the story to be accurate,” “I found the information presented in the story to be believable,” and “I found the information presented in the story to be trustworthy.” These items were adopted from Bucy (2004) because they have been applied to previous online communication and personalization research

(Kalyanaraman & Sundar, 2006; Magee & Kalyanaraman, 2010; Wojdynski &

Kalyanaraman, 2015) and typically achieve strong internal reliability scores with alphas ranging from .83 to .97 (Bucy, 2004; Wojdynski & Kalyanaraman, 2015).

Demographic Variables

Demographic variables were also measured to determine the makeup of the participant pool as well as whether these factors had any effects on the dependent variables. For the demographic questions in this study, participants were asked to indicate their gender, age, race/ethnicity, family household income, religious affiliation, political affiliation, current education level, and academic major. For more detail on each of these measures, see the full questionnaire in Appendix D.

CHAPTER 4 RESULTS

Descriptive Analysis of Data

After the experiment was conducted, first, the data were cleaned. While a total of

280 students (35 per condition) participated in the experiment, some had to be eliminated from data analysis because they either did not identify the correct aggregator during the manipulation check (n = 2), the user-tracking software did not record properly during their experimental sessions (n = 9), or they decided not to finish the study (n =

18). After removing these participants from the dataset, the conditions were then evened out by randomly deleting the number of participants needed to create a balanced experiment (n = 11). A balanced experiment is when all conditions have the same number of participants, which is desirable because it allows for the comparisons between treatments to be equal. This was created by having an online random number service generate numbers between 1 and 30. The responses associated with the numbers given were then deleted until the conditions were even. This left a total of 240 participants (30 per condition) for final data analysis.

Of the finalized sample (N = 240), participants were primarily female (n = 190,

79.2%). Most were between the ages of 18 and 21 (n = 210, 87.5%, Mdn = 20). A plurality of the participants were white (n = 149, 62.1%) and indicated having high family household incomes of $150,000 or more (n = 60, 25.0%). As for religion and political affiliation, most participants identified as Christians (n = 147, 61.3%), and a plurality were Democrats (n = 93, 38.8%). And finally, in terms of education, the participant pool primarily consisted of juniors (n = 90, 37.5%) and advertising majors (n = 64, 26.7%).

Please see Tables 4-1 through 4-7 for the full breakdown.

Table 4-1. Frequency of Participants by Age Age n Percent (%) 20 85 35.4 21 49 20.4 19 47 19.6 18 29 12.1 22 15 6.3 23 8 3.3 24 2 0.8 25 2 0.8 29 2 0.8 30 1 0.4

Table 4-2. Frequency of Participants by Family Household Income Family Household Income n Percent (%) $150,000 or more 60 25.0 $100,000 to $149,999 45 18.8 $75,000 to $99,999 39 16.3 $50,000 to $74,999 35 14.6 $25,000 to $34,999 24 10.0 Less than $25,000 19 7.9 $35,000 to $49,999 18 7.5

Table 4-3. Frequency of Participants by Religious Affiliation Religious Affiliation n Percent (%) Christian 147 61.3 Nothing in Particular 40 16.7 Jewish 20 8.3 Agnostic 17 7.1 Atheist 5 2.1 Hindu 5 2.1 Buddhist 3 1.3 Asatruar 1 0.4 Muslim 1 0.4 Spiritualist 1 0.4

Table 4-4. Frequency of Participants by Political Affiliation Religious Affiliation n Percent (%) Democrat 93 38.8 Republican 77 32.1 Independent 58 24.2 No Party Affiliation 6 2.5 Libertarian 3 1.3 Not Registered to Vote 2 0.8 Green Party 1 0.4

Table 4-5. Frequency of Participants by Race/Ethnicity Race/Ethnicity n Percent (%) White 149 62.1 Hispanic or Latino 49 20.4 Asian or Asian American 16 6.7 Black or African American 15 6.3 Multiracial or Multiethnic 11 4.6

Table 4-6. Frequency of Participants by Education Level Education Level n Percent (%) Junior 90 37.5 Sophomore 77 32.1 Freshman 46 19.2 Senior 27 11.3

Table 4-7. Frequency of Participants by Major Age n Percent (%) Advertising 64 26.7 Public Relations 57 23.8 Telecommunications 38 15.8 Business Administration 10 4.2 Journalism 10 4.2 Political Science 10 4.2 Marketing 7 2.9 Sport Management 6 2.5 English 5 2.1 Digital Arts and Sciences 4 1.7 Economics 4 1.7 Psychology 4 1.7 Sociology 3 1.3 Criminology 2 0.8 Undeclared 2 0.8 Accounting 1 0.4 Agriculture Education 1 0.4 Anthropology 1 0.4 The Classics 1 0.4 Computer Engineering 1 0.4 Family, Youth, and Community Sciences 1 0.4 Finance 1 0.4 French 1 0.4 Health Science 1 0.4 History 1 0.4 Information Systems Management 1 0.4 International Studies 1 0.4 Music 1 0.4 Natural Resource Conservation 1 0.4

These demographics are typical considering the student population at the

University of Florida because there is a greater representation of women in the student body (55.0%), a majority of students are white (58.8%), and undergraduate students, in general, are usually under the age of 25 (Haynie, 2014; National Association for College

Admission Counseling, 2016). In addition, although young adults in the United States are normally less religious than older generations, most still identify as Christians (78%)

(Pew Research Center, 2010), and they are more likely to align themselves with the

Democratic Party (Jones, 2014). In terms of education, while most were juniors and advertising majors, there were strong representations of freshmen, sophomores, and seniors as well as a diversity of majors.

Preparing Measures for Analysis

In order to prepare the rest of the data for analysis, first, the scales (attitude and perceived credibility) were tested for internal reliability. This was completed by checking which scale items led to a suitable Cronbach’s alpha score. Both attitude and perceived credibility revealed high reliability scores, with alphas of .91 each. After assessing reliability, the items for each scale were averaged to form a single measure for final data analysis. Similarly, participants’ self-ratings of how related or unrelated each of the thoughts they recorded during the thought-listing procedure were averaged to create an overall cognitive elaboration score. For example, if a participant had four thoughts while reading and rated them as 7, 6, 5, and 7, then those four numbers would be averaged to create a cognitive elaboration score of 6.25.

Next, for information-seeking behavior, the stories participants read were examined using a content analysis. Judgment was involved in determining whether a

story was about, as an example, jobs or politics, so an additional coder was recruited in order to assess intercoder reliability before coding the entire sample. Ten percent of the data were randomly selected from each condition (using a random number generator) and then double coded. Krippendorff’s alpha was used to measure reliability, and the scores were as follows: 1 for time spent on similar topics, .98 for the subjects of each story, and .98 for the total number of subjects.

In order to assess the total time spent on similar topics, first, the subject area of each story was coded after examining the headlines and screen shots of the stories each participant read. The subject areas included: traffic and weather, jobs and unemployment rates, crime and public safety, national politics, technology and science, music, movies, and TV shows, celebrities, sports, cooking, and fitness and health. If a participant read a story or stories similar to the initial subject area, the number of seconds reading each one was recorded. Then all of the times were added together to create a “total time spent on similar topics” measure for each participant.

In the process of evaluating whether information seeking matched the manipulated topic, variance was also examined in how many different topics participants explored. As a result, the subject area for each story read (not just the ones similar to what participants chose for the stimulus materials) was also coded. Because some of the subject areas participants read were not included in the initial 10 subject areas from the stimulus materials, the possible subject areas to code were expanded for this part of the content analysis. These subject areas (listed in Table 4-8) were adopted from the American Press Institute (2015) because they were cited as a range of civic affairs and entertainment news topics young adults typically like to read.

Table 4-8. List of News Subject Areas New Type News Subject Area Civic Affairs News Business and the economy Crime and public safety Foreign or international news Health care and medical information Information about my city, town, or neighborhood National politics and government Religion and faith Schools and education Science and technology Social issues like abortion, race, and gay rights The environment and natural disasters Traffic and weather Entertainment News Celebrities or pop culture Food and cooking Health and fitness Local restaurants or entertainment Music, TV, and movies Sports Style, beauty, and fashion The arts and culture

Then the total number of different subject areas each participant read was summed. For example, if someone read two political stories, a sports story, and a cooking story, the number 3 would be entered because they read three separate subjects. The full code book used to analyze information-seeking behavior is available in Appendix G.

Manipulation Checks

While the type of aggregator manipulation check served as a screening device to accept or reject a participant before final data analysis, the type of news manipulation check needed to be verified using an independent samples t tests. An independent samples t test compares the means of two samples. It requires the independent variable to contain two discrete groups, and the dependent variable must be measured using an interval or ratio scale (Cronk, 2006). In this case, the test was used to compare how

participants from the civic affairs and entertainment news condition perceived their given story on a scale from entertaining to informative. Results revealed that participants in the entertainment news conditions perceived their given story as more entertaining than informative (M = 2.95, SD = 1.34), and those in the civic affairs news conditions perceived their given story as more informative than entertaining (M = 5.40,

SD = 1.32). These differences were statistically significant (t (238) = 14.25, p < .001).

Thus, the manipulations worked properly.

Hypothesis Testing

Type of Aggregator Results

In order to test the first hypothesis in this study, a factorial (or two-way) ANOVA was conducted. A factorial ANOVA was used because it is a powerful statistical test that can assess the effects of each treatment on a single dependent variable and the interaction effects of those independent variables on the dependent variable at the same time (Cronk, 2006; Pallant, 2010). In this case, a 4 (type of aggregator: Google

News, Facebook, inshorts, or no aggregation) x 2 (type of news: civic affairs or entertainment) factorial ANOVA was used to determine whether aggregators using social recommendations (like Facebook) led users to hold a more positive attitude toward the news content than other types of aggregators, as predicted by hypothesis 1.

Results for this main effect revealed that participants held the most positive attitude toward Google News (M = 5.38, SD = 0.87), followed by Facebook (M = 5.25,

SD = 0.79), the control group (M = 4.43, SD = 1.03), and then inshorts, the editor- determined aggregation (M = 4.22, SD = 0.97). These findings were statistically significant (F (3, 232) = 23.79, p < .001) with a large effect size (eta squared = .235)

(Cohen, 1988). Post-hoc comparisons using the Tukey HSD test indicted that the mean

scores for Google News and Facebook did not significantly differ (p = .86), and neither did the scores for inshorts and the control group (p = .59). However, Google News did differ significantly from the control condition (p < .001) and inshorts (p < .001), as did

Facebook from the control group (p < .001) and inshorts (p < .001). Because both

Google News and Facebook achieved similar positive attitude scores in comparison to the other conditions, the results lend partial support to hypothesis 1. See Figure 4-1.

1 Google News Facebook inshorts Control Group

Figure 4-1. Attitude Results for Types of Aggregators.

A 4 x 2 factorial ANOVA was also used to explore hypothesis 2, which postulated that aggregators employing the use of social recommendations were more likely than other types to lead users to search for information on similar topics. The mean scores suggested that participants in the Google News condition spent the most time browsing the Web for similar information (M = 71.25, SD = 113.57). Facebook (M = 53.97, SD =

73.53) and the editor-gatekeeper aggregator inshorts (M = 50.47, SD = 126.02) fell in the middle, while those in the control condition spent the least amount of time reading related news (M = 33.82, SD = 72.66). However, these differences were not statistically significant (F (3, 232) = 1.46, p = .23), in part because the standard deviations were so large. The standard deviations for time spent on related information were large because

of the variance in the data. More specifically, some participants spent no time on related information, and some spent upwards of seven to 10 minutes reading similar news.

Therefore, the mean scores alone are insufficient to reflect the range of behavior, and this evidence was insufficient to reject the null hypothesis, so H2 was rejected.

However, the results showed a statistically significant relationship between the total number of news subjects participants read and their information-seeking behavior.

A 4 x 2 factorial ANOVA showed that participants in the Facebook condition read the greatest number of news subjects (M = 2.68, SD = 1.42), followed by Google News (M =

2.27, SD = 1.29), the control condition (M = 2.08, SD = 0.10), and then inshorts (M =

1.75, SD = 1.00). In this case, the results were statistically significant (F (3, 232) = 6.40, p < .001) with a medium effect size (eta squared = .076) (Cohen, 1988). Post-hoc comparisons revealed that statistically significant differences occurred between

Facebook and the control group (p = .03) and even more so between Facebook and inshorts (p < .001), which suggested that it was editor-determined, not social, aggregation that had the most narrowing impact on information-seeking behavior.

Therefore, H2 was not supported. See Figure 4-2.

2.5

1.5

0.5

0 Google News Facebook inshorts Control Group

Figure 4-2. Number of Subjects Read Results for Types of Aggregators.

Intervening Variable Results for Type of Aggregator

In order to test hypothesis 3, which stated that increased cognitive elaboration would lead users to hold a more positive attitude toward the different types of aggregators, a couple of statistical techniques were required. First, a one-way ANOVA, a statistical test used to compare the mean responses for a continuous dependent variable when a categorical independent variable has three or more groups (Cronk,

2006), was performed examining whether the types of aggregators led to varying levels of cognitive elaboration. Results revealed that Google News (M = 5.64, SD = 1.26),

Facebook (M = 5.87, SD = 1.10), inshorts (the editor-gatekeeper aggregator) (M = 5.55,

SD = 1.37), and the control group (M = 5.86, SD = 1.09) all yielded similar cognitive elaboration scores (F (3, 236) = 1.05, p = .37). See Figure 4-3.

1 Google News Facebook inshorts Control Group

Figure 4-3. Cognitive Elaboration Results for Types of Aggregators

Next, a simple linear regression, which allows the prediction of one continuous variable from another continuous variable, was conducted in order to examine whether cognitive elaboration was predictive of participants’ attitudinal responses toward the news content (Cronk, 2006). The regression equation was significant (F (1, 238) =

10.69, p < .001) with an R2 of 0.43, which means 43 percent of the variation in attitude

can be explained by cognitive elaboration (Cronk, 2006). See Table 4-9. This lends partial support to hypothesis 3 because while the different recommendation sources did not affect how much participants thoughtfully considered the information contained in the message, heightened cognitive elaboration did lead participants to hold a more positive attitude toward the news content.

Table 4-9. Regression Analysis Summary for Cognitive Elaboration Predicting Attitude B SE B β t p Cognitive Elaboration .178 .055 .207 3.270 .001

Similarly, to test hypothesis 4, which suggested that increased perceptions of credibility would lead users to hold a more positive attitude toward the different types of aggregators, a one-way ANOVA and simple linear regression were conducted. The one- way ANOVA results mimicked the findings for hypothesis 1, as participants found

Google News to be the most credible (M = 5.91, SD = 0.86), followed by Facebook (M =

5.78, SD = 0.68), the control group (M = 5.55, SD = 0.93), and then inshorts (M = 5.47,

SD = 1.02). These results were statistically significant (F (3, 236) = 3.23, p = .02) but with a small effect size (eta squared = .039) (Cohen, 1988). According to post-hoc comparisons using the Tukey HSD test, the significant difference occurred between

Google News and inshorts (p = .03), which suggests that aggregators using algorithms to provide personalized news are perceived as more credible than editor-created aggregation.

In addition, the simple linear regression, which was performed to test whether increased perceived credibility led to increased positive attitude, was statistically significant (F (1, 238) = 45.23, p < .001) with an R2 of 0.16, meaning 16 percent of the variation in attitude can be explained by participants’ perceptions of credibility (Cronk,

2006). Overall then, these results support hypothesis 4 because not only did the different types of aggregators lead to varying levels of perceived credibility, but heightened perceptions of credibility also led users to hold more positive attitude toward the news content. See Figure 4-4 and Table 4-10.

6 5.9 5.8 5.7 5.6 5.5 5.4 5.3 5.2 Google News Facebook inshorts Control Group

Figure 4-4. Perceived Credibility Results for Types of Aggregators.

Table 4-10. Regression Analysis Summary for Perceived Credibility Predicting Attitude B SE B β t p Perceived Credibility .466 .069 .400 6.725 < .001

Type of News Results

To test hypothesis 5, which posited that civic affairs news would lead users to hold a more positive attitude toward the news content than entertainment news, a 4 x 2 factorial ANOVA was conducted. Results for this main effect revealed that the mean attitude results for the civic affairs news conditions (M = 4.84, SD = 0.94) and entertainment news condition (M = 4.80, SD = 1.15) were similar (F (1, 232) = .07, p =

.80), which indicates that the type of news did not affect participants’ attitude responses.

Therefore, hypothesis 5 was not supported. See Figure 4-5.

1 Civic Affairs Entertainment Figure 4-5. Attitude Results for Types of News.

On the other hand, the factorial ANOVA results for hypothesis 6, which stated that civic affairs news is more likely than entertainment news to lead users to search for information on similar topics, revealed that participants in the civic affairs news conditions spent more time searching for similar information during their five minutes on

Google News (M = 68.63, SD = 106.63) than those in the entertainment news conditions (M = 36.12, SD = 89.55). These findings were statistically significant (F (1,

232) = 6.53, p = .01) with a small effect size (eta squared = .027) (Cohen, 1988). As a result, hypothesis 6 was supported.

Further analysis (to check whether the different types of news changed the number of news subjects participants read) revealed that the difference between civic affairs news (M = 2.13, SD = 1.26) and entertainment news (M = 2.26, SD = 1.20) in this case were small (F (1, 232) = .66, p = .42). This suggests that different types of news led to differences in time spent on related information but not in the number of subjects read. See Figure 4-6 and Figure 4-7.

80 70 60 50 40 30 20 10 0 Civic Affairs Entertainment

Figure 4-6. Time Spent in Seconds on Related Information Results for Types of News.

2.5

1.5

0.5

0 Civic Affairs Entertainment

Figure 4-7. Number of Subjects Read Results for Types of News.

Intervening Variable Results for Type of News

As was the case with the different types of aggregators, cognitive elaboration was proposed to aid participants’ attitudinal responses to the different types of news in hypothesis 7. To test whether increased cognitive elaboration led participants to hold more positive attitudes toward the different news types, first, an independent samples t test was conducted. The mean cognitive elaboration scores for civic affairs news (M =

5.74, SD = 1.25) and entertainment news (M = 5.72, SD = 1.78) were nearly identical (t

(238) = .49, p = .93), suggesting that the news type did not affect participants’ average cognitive elaboration scores. See Figure 4-8.

1 Civic Affairs Entertainment

Figure 4-8. Cognitive Elaboration Results for Types of News.

However, because a simple linear regression already indicated (under hypothesis 3) that heightened cognitive elaboration increased positive attitudes (F (3,

236) = 3.23, p = .02), hypothesis 7 was partially supported. Although the different types of news did not affect how much participants thoughtfully considered the information contained in the message, heightened cognitive elaboration did lead participants to hold a more positive attitude toward the news content.

Hypothesis 8, which predicted that increased perceptions of credibility would lead users to hold more positive attitudes toward the different types of news, revealed findings similar to H7 because the difference between the civic affairs (M = 5.71, SD =

0.89) and entertainment news (M = 5.64, SD = 0.90) conditions was also small (t (238)

= .18, p = .53). This means that participants did not view one news type as more or less credible than the other. See Figure 4-9.

1 Civic Affairs Entertainment

Figure 4-9. Perceived Credibility Results for Types of News.

However, as was the case with hypothesis 7, a significant simple linear regression was already found for perceived credibility under hypothesis 4 (F (1, 238) =

45.23, p < .001). This means hypothesis 8 was partially supported as well because even though different news types did not lead to differing perceived credibility scores, increased perceptions of credibility were predictive of a more positive attitude toward the news content.

Interaction of Type of Aggregator and Type of News Results

As for the interaction effects in this study, first, hypothesis 9 posited that aggregators using social recommendations to share civic affairs news would lead users to hold the most positive attitude toward the news content. Echoing the results from hypothesis 1, Google News and Facebook solicited more positive attitudes over the control group and inshorts (the editor-gatekeeper aggregator), but participants’ attitudinal responses remained fairly similar regardless of whether civic affairs or entertainment news was shared. As a result, there was no significant interaction effect

in this case (F (3, 232) = .17, p = .55), and hypothesis 9 was not supported. See Figure

4-10, and Table 4-11 provides the mean scores for each interaction effect in this study.

4 Civic Affairs Entertainment 3

1 Google News Facebook inshorts Control Group

Figure 4-10. Attitude Results for the Interaction of Types of Aggregators and Types of News.

Similarly, hypothesis 10 predicted that aggregators using social ties to share civic affairs news would be the most likely to lead users to search for information on similar topics. In this case, a 4 x 2 factorial ANOVA was run to examine the interaction effect for the types of aggregators and types of news on time spent seeking related information. In the case of all four aggregators, when the sources shared civic affairs news, participants would spend considerably more time on similar information (about a minute to 90 seconds on average) in comparison to sharing entertainment news, in which case participants typically spent anywhere from 15 to 45 seconds. Therefore, there was not a significant interaction effect (F (3, 232) = .356, p = .79), and H10 was not supported.

Another 4 x 2 factorial ANOVA was conducted for hypothesis 10 looking at the total number of subjects participants read. Results in this case revealed findings

consistent with hypothesis 2 in that Facebook led participants to read the greatest number of subjects, followed by Google News, the control group, and then inshorts.

However, like hypothesis 9, the mean scores did not change much when different types of news were shared. Therefore, these results were not statistically significant either (F

(3, 232) = .997, p = .40), and H10 was not supported. See Figure 4-11 and Figure 4-12.

120

100

60 Civic Affairs Entertainment 40

0 Google News Facebook inshorts Control Group

Figure 4-11. Time Spent in Seconds on Related Information Results for the Interaction of Types of Aggregators and Types of News.

3 Civic Affairs Entertainment 2

0 Google News Facebook inshorts Control Group

Figure 4-12. Number of Subjects Read Results for the Interaction of Types of Aggregators and Types of News.

Table 4-11. Mean Scores and Standard Deviations for Interaction Results Time Spent Related Number of Subjects Attitude in Seconds Read Group M SD M SD M SD Google News Civic Affairs 5.26 0.77 95.77 134.67 2.10 1.24 Entertainment 5.50 0.95 46.73 82.85 2.43 1.33 Facebook Civic Affairs 5.31 0.66 61.63 73.46 2.63 1.65 Entertainment 5.19 0.90 46.30 74.03 2.73 1.17 inshorts Civic Affairs 4.33 0.99 62.97 117.32 1.90 1.24 Entertainment 4.10 0.96 37.97 134.98 1.60 0.68 Gainesville Sun Civic Affairs 4.43 0.85 54.17 91.11 1.90 0.61 Entertainment 4.43 1.20 13.47 39.80 2.27 1.26

Demographic Variable Results

Although no hypotheses were offered for the demographic variables, testing whether they influenced the dependent variables (attitude, time spent on related content, and number of subjects read) was important in order to determine whether they may have had an influence beyond the experimental conditions. A series of regressions were used to assess the effects of each of the demographic variables on the dependent variables. Then if a variable did have an impact, factorial ANOVAs were run to see if they had any further effect on the results from hypothesis testing.

Starting first with gender, three separate simple linear regressions revealed that gender was not predictive of a more positive attitude (F (1, 238) = .30, p = .59, with an

R2 of 0.001), and it was not indicative how much time participants spent browsing

Google News for information related to their stimulus materials (F (1, 238) = .70, p =

.40, with an R2 of 0.003). However, a significant regression equation did find that gender was predictive of the number of different subjects participants read (F (1, 238) = 6.02, p

= .02, with an R2 of 0.03). See Table 4-12.

Table 4-12. Regression Analysis Summary for Gender B SE B β t p Attitude -.090 .166 -.035 -.543 .587 Time Spent Related in Seconds -13.295 15.840 -.054 -.839 .402 Number of Subjects Read .475 .194 .157 2.453 .015

While these results suggest that gender only accounted for 3 percent of the variance in the number of subjects read (Cronk, 2006), to assess whether these findings had an impact on the rest of the results, a 4 x 2 x 2 factorial ANOVA was run, including gender as a factor. Ultimately, no interaction effect was found when gender was coupled with the types of aggregators (F (3, 224) = .97, p = .41), types of news (F (1,

224) = 1.77, p = .19), or both (F (3, 224) = .36 p = .79). Therefore, it can be concluded that gender did not have any further effect on results.

As for age, no significant regression equations were found, suggesting that age was not predictive of more positive attitudes (F (1, 238) = 1.532, p = .22, with an R2 of

0.06), it did not lead to changes in how much time participants spent on related information (F (1, 238) = 0.240, p = .63, with an R2 of 0.001), nor did it lead to changes in the number of different news subjects participants read (F (1, 238) = 2.505, p = .12, with an R2 of 0.10). See Table 4-13. This suggests that age did not have an impact on results either, which is not surprising given the small age range represented in this study.

Table 4-13. Regression Analysis Summary for Age B SE B β t p Attitude -.050 .040 -.080 -1.238 .217 Time Spent Related in Seconds -1.877 3.826 -.032 -.490 .625 Number of Subjects Read -.074 .047 -.102 -1.583 .115

Next, like age, a series of simple linear regressions indicated that race and ethnicity did not predict a more favorable attitude (F (1, 238) = .58, p = .45, with an R2 of

0.002). They did not change the amount of time participants spent reading information related to their stimulus materials (F (1, 238) = .58, p = .45, with an R2 of 0.002) or the number of news subjects they read (F (1, 238) = .22, p = .64, with an R2 of 0.001).

Therefore, it can also be concluded that race and ethnicity did not have an impact on results. See Table 4-14.

Table 4-14. Regression Analysis Summary for Race/Ethnicity B SE B β t p Attitude -.034 .045 -.049 -.761 .447 Time Spent Related in Seconds -3.917 4.302 .059 .911 .363 Number of Subjects Read -.025 .053 -.031 -.471 .638

Family household income did not have an effect on results either because simple linear regression results revealed that attitude scores did not increase or decrease with changes in income (F (1, 238) = 2.78, p = .10, with an R2 of 0.01). In addition, income was not predictive of how much time participants spent reading similar information (F (1,

238) = 2.95, p = .09, with an R2 of 0.01) or the number of subjects they read (F (1, 238)

= .00, p = .99, with an R2 of 0.000). See Table 4-15.

Table 4-15. Regression Analysis Summary for Family Household Income B SE B β t p Attitude .058 .035 .107 1.667 .097 Time Spent Related in Seconds 5.677 3.307 .111 1.717 .087 Number of Subjects Read .001 .041 .001 .016 .988

Similar results were found while testing the effects of religious affiliation, in that religion was not indicative of how much time participants spent reading information related to their stimulus material (F (1, 238) = 3.36, p = .07, with an R2 of 0.01), nor did it change the number of different subject areas they read (F (1, 238) = 1.86, p = .17, with an R2 of 0.01). However, religious affiliation was predictive of participants’ attitudinal responses (F (1, 238) = 4.36, p = .04, with an R2 of 0.02). See Table 4-16.

Table 4-16. Regression Analysis Summary for Religious Affiliation B SE B β t p Attitude -.042 .020 -.134 -2.088 .038 Time Spent Related in Seconds -3.520 1.921 -.118 -1.832 .068 Number of Subjects Read -.032 .024 -.088 -1.365 .174

While these results suggest that religion only accounted for 2 percent of the variance in participants’ attitudes (Cronk, 2006), religious affiliation was still run as an additional factor in a factorial ANOVA to assess whether it had any further impact on results. Ultimately, no significant interactions were revealed when religion was coupled with the types of aggregators (F (14, 198) = .85, p = .62), types of news (F (6, 198) =

.85, p = .53), or both (F (6, 198) = 1.14, p = .34). Therefore, it can be concluded that religious affiliation did not have any further effect on results.

Alternatively, no significant regression equations were found for political affiliation. Political party was not predictive of more favorable attitudes (F (1, 238) = .67, p = .42, with an R2 of 0.003), the time participants spent reading similar information (F

(1, 238) = 2.14, p = .15, with an R2 of 0.01), or the number of news subjects they read

(F (1, 238) = .47, p = .50, with an R2 of 0.002), which means political affiliation did not have an impact on results. See Table 4-17.

Table 4-17. Regression Analysis Summary for Political Affiliation B SE B β t p Attitude .061 .074 .053 .816 .415 Time Spent Related in Seconds 10.323 7.052 .094 1.464 .145 Number of Subjects Read -.060 .087 -.044 -.683 .496

Finally, for education, results were consistent across education level and majors.

More specifically, year in school did not lead to variations in attitudes (F (1, 238) = .20, p

= .65, with an R2 of 0.001), time spent on related information (F (1, 238) = .17, p = .68, with an R2 of 0.001), or number of news subjects read (F (1, 238) = .34, p = .56, with an

R2 of 0.001), meaning education level did not have an effect on results either. See

Table 4-18.

Table 4-18. Regression Analysis Summary for Education Level B SE B β t p Attitude -.033 .073 -.029 -.449 .654 Time Spent Related in Seconds 2.869 6.986 .027 .411 .682 Number of Subjects Read -.050 .086 -.038 -.579 .563

In addition, no significant regression equations were found for major when crossed with attitude (F (1, 238) = .91, p = .34, with an R2 of 0.004), time spent on related content (F (1, 238) = .003, p = .96, with an R2 of 0.000), or the number of subjects read (F (1, 238) = 1.39, p = .24, with an R2 of 0.01). See Table 4-19. Overall then, these findings indicate that the results in this study are attributable to the independent variables rather than to any demographic variables. For a complete summary of results, see Table 4-20.

Table 4-19. Regression Analysis Summary for Major B SE B β t p Attitude .006 .006 .062 .952 .342 Time Spent Related in Seconds -.034 .602 -.004 -.056 .955 Number of Subjects Read .009 .007 .076 1.177 .240

Table 4-20. Summary of Hypothesis Results Hypothesis Result H1: Aggregators that employ the use of social recommendations Partially Supported lead users to hold a more positive attitude toward the news content than other types of aggregators. H2: Aggregators that employ the use of social recommendations Not Supported are more likely than other types of aggregators to lead users to search for information on similar topics. H3: Greater cognitive elaboration leads users to hold a more Partially Supported positive attitude toward the different types of aggregators. H4: Greater perceived credibility leads users to hold a more Supported positive attitude toward the different types of aggregators. H5: Civic affairs news leads users to hold a more positive attitude Not Supported toward the news content than entertainment news. H6: Civic affairs news is more likely than is entertainment news to Supported lead users to search for information on similar topics. H7: Greater cognitive elaboration leads users to hold a more Partially Supported positive attitude toward the different types of news. H8: Greater perceived credibility leads users to hold a more Partially Supported positive attitude toward the different types of news. H9: Aggregators that employ the use of social recommendations Not Supported to share civic affairs news lead users to hold the most positive attitude. H10: Aggregators that employ the use of social recommendations Not Supported to share civic affairs news are most likely to lead users to search for information on similar topics.

CHAPTER 5 DISCUSSION

Findings and Practical Implications

The primary purpose of this dissertation was to examine whether the different types of news aggregators (aggregators that use an algorithm to provide personalized news recommendations like Google News, aggregators that rely on social connections to provide personalized news recommendations like Facebook, and aggregators that use editors to provide personalized news recommendations like inshorts) lead users to narrow their information-seeking behavior. News aggregators have been suspected of doing this because their personalization aspect offers the potential to close people off from information outside of their pre-existing beliefs, which could also lead to further problems over time such as increased political polarization, narcissism, and a hindered democracy (Sunstein, 2001; Pariser, 2011).

Effects of News Aggregation

To examine whether these concerns are valid or whether aggregation, alternatively, enhances discovery of information, which could also produce an interactive public more literate in civic affairs (American Press Institute, 2015; Linden,

2011), this study first sought to determine if aggregation affected attitudes. Attitudes were an important measure in this study because people’s general evaluations of favor or disfavor toward other people, objects, and issues hold the potential to influence people’s behavior (Fishbein & Ajzen, 1975; Perloff, 2014; Petty & Cacioppo, 1986), including information-seeking behavior. Based on research indicating that people tend to trust interpersonal communication more than media (Katz & Lazarsfeld, 1955), the first hypothesis in this study suggested that aggregators using social recommendations

(like Facebook) would lead users to hold a more positive attitude toward the news content than other types of aggregators.

The results were statistically significant in partial support of hypothesis 1, indicating that both Google News and Facebook elicited the most positive attitudes, while inshorts (the editor-gatekeeper aggregator) and the control group earned the lowest. Even though these results are not exactly what was anticipated for hypothesis 1, they are consistent with the literature because not only has research suggested that readers indicate a higher level of trust for news stories recommended by friends through social media in comparison to news recommended by journalists (Epstein, 2016;

Turcotte et al., 2015), but it has also found that people trust aggregators like Google

News over journalists because the algorithms they use are removed from human intervention and are therefore seen as unbiased (Carlson, 2007).

Because Google News also allows users to customize what they see for themselves (by selecting subject areas of interest like world news, entertainment, and/or sports under the “Personalize Google News” tab) (Liu et al., 2010), this could have been another factor that led to these attitude results. Giving users a sense of agency (control) typically encourages their investment in the process, which is why research has discovered that allowing users to exert some control over the content recommendations they receive motivates greater attention and engagement with the content. Additionally, because individuals know their own personal identities better than anyone else will, users will likely not only trust their own decision making over other news providers, but personalization that involves user input also typically triggers a greater sense of personal relevance (Sundar, 2008; Sundar & Marathe, 2010).

In Facebook’s case, less is known about how its algorithm compiles information from users’ friends, groups, and the pages they like (Oremus, 2016). However, it would be reasonable to suspect, given an announcement in June 2016 that Facebook was changing its newsfeed service to prioritize information recommended from family and friends over posts from news organizations (Sunstein, 2016), that attitude results could have stemmed because of salience of this issue in people’s minds. The experiment was conducted before Facebook’s announcement, but if this study had been conducted afterward, users may have increased their favorable attitudes toward Facebook if they do prefer and trust news stories recommended by friends through social media in comparison to news recommended by journalists (Epstein, 2016; Turcotte et al., 2015).

In terms of understanding the effects of aggregators on information-seeking behavior, on the one side, survey research has indicated that aggregator users regularly follow a mix of civic affairs, entertainment, and practical news, incorporating diverse opinions (American Press Institute, 2015; Beam & Kosicki, 2014), yet a long history of research on selective exposure suggests the opposite. As previously mentioned, selective exposure is defined as a tendency to seek out information that reinforces existing opinions (Beam, 2014). This term was developed from a large body of research, finding that people prefer to view and read information that supports their own interests over dissonant information because it reinforces confidence in their attitudes instead of causing uncertainty or psychological discomfort (Beam, 2014; Beam &

Kosicki, 2014; Festinger, 1957, 1964; Frey, 1986; Garrett, 2009a, 2009b; Hart et al.,

2009; Iyengar & Hahn, 2009; Knobloch-Westerwick & Meng, 2009; Sears & Freedman,

1967; Stroud, 2010; Sweeney & Gruber, 1984).

Related to this, experimental research has generally found that personalized news recommendations lead to selective exposure (Beam, 2014; Kalyanaraman &

Sundar, 2006), which is why aggregators were suspected of narrowing information- seeking behavior in this study. More specifically, hypothesis 2 posited that aggregators employing the use of social recommendations were more likely than other types of aggregators to lead users to search for information on similar topics. As perceptions of personal relevance would likely increase for aggregators like Facebook (because users would expect friends and family to know their interests better than editors) as well as

Google News (because algorithms choose stories based on factors like past reading behavior, meaning recommendations are likely close to what individuals would choose for themselves), these aggregators were anticipated to have the most narrowing impact on information-seeking behavior.

Alternatively, results indicated the opposite occurred because participants in the

Facebook and Google News conditions read more news subjects than participants in the inshorts and control conditions. On one side, these findings could have resulted from participants’ short attention spans, but they also suggest that aggregation enhanced newsreading instead of narrowed it. As a result, this study offers evidence that popular aggregators, like Google News and Facebook, may not have a narrowing effect on their information-seeking behavior as suspected. Instead, these findings support literature suggesting that news aggregation promotes discovery, not fragmentation (Linden, 2011). However, what is not yet known is whether news aggregators promote pleasure seeking and entertainment at the expense of public affairs (Katz, 1996).

Effects for Different Types of News

This is why, in addition to testing the effects of different types of aggregators, type of news was another factor considered in this study. According to uses and gratifications, news stories can be broken down into multiple categories, and each category signals different levels of cognitive processing. Surveillance news, which includes news topics that depict information about the world and individual’s surroundings (such as civic affairs news), tends to lead readers to engage in more effortful thinking, while diversion news (news topics that provide entertainment value) is more likely to trigger more routinized thinking (Blumler, 1979; Eveland, 2001). As a result, research has suggested that surveillance news sparks greater interest and higher favorability from readers (Becker, 1976; Garramone, 1985; Kaye & Johnson,

2002). In addition, readers of surveillance news are more likely to seek out additional related information (Kaye & Johnson, 2002; Tan, 1980).

Echoing this research, hypothesis 5 anticipated that civic affairs news would lead users to hold a more positive attitude toward the news content than entertainment news.

However, the evidence did not support the hypothesis, for results were relatively similar between the two types of news content. While these results go against findings from previous research (Becker, 1976; Garramone, 1985; Kaye & Johnson, 2002), they suggest that the type of aggregator was the determining factor that led to more positive attitudes, no matter what type of news was shown. They could also be suggestive of a blurring line between informative and entertainment news known as “infotainment”

(Thussu, 2007). But most importantly, because the entertainment news conditions did not solicit higher favorability than the civic affairs conditions, these results provide some

evidence against claims that aggregators promote pleasure seeking and entertainment at the expense of civic affairs (Katz, 1996).

Alternatively, evidence supported hypothesis 6, which postulated that civic affairs news was more likely than was entertainment news to lead users to search for information on similar topics. Participants in the civic affairs conditions spent more time reading similar information (a little over a minute on average) than participants in the entertainment news conditions, who typically spent around 30 seconds browsing related news. Granted a statistically significant distinction between 30 and 60 seconds is not all that meaningful, but these findings are consistent with previous research (Kaye &

Johnson, 2002; Tan, 1980), In addition, given the results that aggregators expected to narrow their information-seeking behavior may instead be widening their newsreading, participants in the civic affairs conditions likely searched for additional related information because they were trying to learn more about their initial topic, not because they were trying to avoid counter-attitudinal information.

Interaction Effects for Types of Aggregators and Types of News

These explanations are even further supported by the findings for the interaction effects in this study. First, because research has suggested that under high elaboration conditions (as is typical with surveillance or civic affairs news) the source of information can aid the confidence with which people hold certain attitudes (Tormalla et al., 2007),

Hypotheses 9 suggested that aggregators employing the use of social ties to share civic affairs news would lead users to hold the most positive attitude. Echoing the results from hypothesis 1, Google News and Facebook solicited more positive attitudes over the control group and inshorts, the editor-determined aggregator. However, participants’ attitudinal responses remained fairly similar regardless of whether civic affairs or

entertainment news was shared. As was discussed for hypothesis 5, these findings suggest that the aggregators were the driver of attitudes, regardless of news type.

The results for hypothesis 10, which predicted that aggregators using social ties to share civic affairs news would be the most likely to lead users to search for information on similar topics, suggested similar conclusions because the number of subjects read did not fluctuate when aggregators shared one news type over the other.

These findings still suggest that popular aggregators could be enhancing newsreading instead of narrowing it because Facebook and Google News led participants to read a greater number of subjects over the control group and inshorts. However, these results coupled with the lack of a difference between the civic affairs and entertainment news conditions provides further evidence that the aggregators sharing the news, not the news stories themselves, were the main drivers of results in this study.

Hypothesis 10 also indicated that aggregators do not push civic affairs news out of sight because of the findings for time spent on related information. While there were no significant differences between the types of aggregators in terms of leading users to search for additional related information, in all four cases, participants spent considerably more time on similar information (about a minute to 90 seconds on average) when the sources shared civic affairs news not entertainment news, in which case participants typically spent anywhere from 15 to 45 seconds on related news.

Therefore, these interaction results provide more assurance that aggregators actually enhance engagement with news topics that provide learning value, instead of limiting newsreading to entertainment news at the expense of civic affairs.

Implications for the News Bubble

The major findings that came from hypothesis testing were that popular aggregators, like Google News and Facebook, do not hinder discovery of information, nor do they lead users to spend time reading entertainment news over information about civic affairs. This also means that aggregation may not lead to the news bubble.

Fears of a news bubble stem from Pariser’s (2011) filter bubble, where he argued that personalized news systems hold the potential to lead to greater selective exposure, by closing people off to diverse information, subjects, and ideas, as well as further consequences over time, such as increased polarization and narcissism, which could also pose problems for a functioning democracy. Because the results from this study suggest that aggregation does not close people off from diverse subjects, then there is some evidence to suggest that these greater consequences from the news bubble are not happening either.

First, political polarization and narcissism are often expected to result from personalized aggregation because, according to Sunstein (2001), if individuals bypass general interest news in favor of restricting themselves to topics and opinions that favor their interests, then personalized portals could act as an echo chamber (a.k.a. polarization machine) (Myers, 2015; Sunstein, 2001). As people are consistently exposed to information that reinforces their opinions, those opinions tend to become more polarized over time (Feldman et al., 2014; Sunstein, 2001). In addition, it could create the impression that users’ narrow self-interests are all that matter (Pariser, 2011).

However, if aggregators do indeed lead users to search for multiple, diverse subject areas, then maybe users will be exposed to information about diverse political opinions and social groups as well.

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This could also be good news for civic participation and the public sphere. In order for people to be informed well enough to make decisions that would promote a functioning democracy, they need to be exposed to diverse information (Sunstein,

2001). More specifically, according to Habermas (1973), public opinion can only be reached through communication of “generalizable interests that transcend the particular interests of competing groups and individuals” (Pusey, 1993, p. 90). While uncovering that participants in the Google News and Facebook conditions read a couple extra news subjects as well as that participants spent more time (about a minute longer on average) on civic affairs does not suggest that aggregators are a definite solution to a lack of civic engagement, it is a positive sign as this form of newsreading gains even more popularity.

These results are also optimistic considering the population used in this study: young adults. Fears of the news bubble are magnified for young adults because they are already considered as narcissistic (in comparison to older generations) (Kwon &

Wen, 2010; Twenge et al., 2008), and they are traditionally expected to be more interested in entertainment because rely on aggregators at a much higher rate than other populations (American Press Institute, 2015; Mitchell et al., 2014). At first, results seemed to supportive of these claims because participants did hold more favorable attitudes toward Google News and Facebook, but because they did not limit their information-seeking to the content of their stimulus materials and they spent more time reading information related to civic affairs not entertainment, the findings negate claims that young people have become narrow minded and “newsless” (American Press

Institute, 2015).

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Findings and Theoretical Implications

In order to increase the explanatory power of these results as well as test the

Elaboration Likelihood Model (Petty & Cacioppo, 1986), the theoretical framework in this study, cognitive elaboration and perceived credibility were also measured as potential intervening variables in this study. ELM is a theoretical approach that explains two distinct modes recipients use to cognitively process information in order to accurately assess the impact of communication on attitudes. The central route is when people thoughtfully and carefully consider the information presented in the message, and the peripheral route is when people engage in more automatic or routinized thinking

(Perloff, 2014; Petty & Cacioppo, 1986). Which route individuals use depends on their motivation and ability to process the given information (Petty & Cacioppo, 1986), and with each route comes different mechanisms for how communication can lead to attitude change (Perloff, 2014). The theoretical implications for this study on ELM can be seen in two ways, regarding the types of aggregators and the types of news.

Factors Affecting People’s Responses to Different Types of Aggregators

Personal relevance has been found to encourage individuals to signal the central route over the peripheral route because as personal relevance increases, people become more motivated to engage with message (Petty & Cacioppo, 1979; Petty &

Cacioppo, 1986). Therefore, it was reasonable for hypothesis 3 to postulate that greater cognitive elaboration would lead users to hold a more positive attitude toward the different types of aggregators. Results indicated that Google News, Facebook, the editor-gatekeeper aggregator inshorts, and the control condition all yielded similarly high amounts of cognitive elaboration, meaning certain aggregators did not predispose users to differing processing routes as expected. However, because technically all of the

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sources displayed personally relevant and localized information, this could explain why the aggregators did not lead to differing levels of cognitive elaboration.

In addition, these results could be indicative of the Heuristic Systematic Model

(HSM), which is similar to ELM except it says that the two routes to persuasion are not mutually exclusive. According to HSM, people can rely on arguments and cognitive shortcuts at the same time to help them evaluate a message (Perloff, 2014). Therefore, it may be that the two levels of processing cancelled each other out, which is why results showed no differences in cognitive elaboration. Either way, secondary analysis for hypothesis 3 revealed that heightened cognitive elaboration did lead users to hold more a positive attitude toward the news content. Therefore, hypothesis 3 was partially supported. These results support prior research, finding that deeper processing has a greater impact on attitudes (Kreuter et al., 1999; Petty & Cacioppo, 1986; Tam & Ho,

2005). Even more importantly, they uphold ELM because personally relevant information evoked high levels of cognitive processing, and they revealed that higher levels of cognitive processing led to more positive attitudes.

Another potential driver of attitude results measured in this study was perceived credibility because, according to ELM, credibility is a factor that influences attitude no matter whether elaboration likelihood is high or low (Petty & Cacioppo, 1986). For example, when people process information using the central route, an attitude is likely to be shaped by the merits of the arguments. Alternatively, when people use the peripheral route, an attitude might be based on a cue or cognitive shortcut, such as “the source seems like an expert” or “there are a lot of arguments in this message, so it must be credible” (Petty & Cacioppo, 1986). As a result, hypothesis 4 posited that greater

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perceived credibility would lead users to hold a more positive attitude toward the different types of aggregators.

Results suggested that the different types of aggregators did lead to differing perceptions of credibility. Google News yielded the highest perceived credibility scores, followed by Facebook, the control condition, and then inshorts received the lowest.

While participants’ perceptions of credibility could have stemmed from their familiarity with the aggregators, these results do echo findings from the first hypothesis as well as literature cited earlier explaining that people often trust aggregators like Google News over human curation because the former appears to be more neutral (Carlson, 2007).

Also, greater perceived credibility led to more positive attitudes toward the news content. This then not only lends full support to hypothesis 4, but it also upholds ELM by indicating that perceptions of credibility were a major driver of people’s attitude responses to news aggregators. In addition, these results add to the explanatory power of this dissertation because they uncovered perceived credibility as a potential mediator, or link in the chain, between the type of aggregator independent variable and the attitude dependent variable (Shadish et al., 2002).

Factors Affecting People’s Responses to Different Types of News

Coupled with these findings, increased cognitive elaboration and perceived credibility were also expected to help support more positive attitudes for different types of news, as indicated by hypothesis 7 and hypothesis 8. These proposals were made because surveillance news has been found to lead to greater cognitive elaboration in comparison to diversion news (Blumler, 1979; Eveland, 2001). In addition, while one news type was not expected to be more or less credible than the other, according to

ELM, credibility is an important factor that guides individuals’ processing and attitudes

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because people usually desire to hold accurate attitudes (Festinger, 1950; Petty &

Cacioppo, 1986; Petty et al., 2000). However, no intervening effects were found because the different types of news did not lead to differing levels of cognitive elaboration nor did they change participants’ perceptions of credibility.

The absence of significant findings in both cases could be attributable to the effects of aggregation. While research suggests that surveillance news typically sparks greater cognitive processing as well as higher favorability (Becker, 1976; Garramone,

1985; Kaye & Johnson, 2002), participants likely based their responses on the type of aggregator they saw, not the type of news content. This could also explain why perceived credibility aided the relationship between the types of aggregators and attitude but did not offer the same facilitation for different types of news. According to

ELM, sources like these can provide cues that guide how people think about a message and whether they should accept or reject it (Petty & Cacioppo, 1984). Therefore, maybe the aggregators triggered a cognitive response from participants as well as certain perceptions of credibility before they even started reading their civic affairs or entertainment news stories.

However, findings for the types of aggregators already suggest that heightened cognitive elaboration led participants to exhibit more positive attitudes, which supports

ELM as well as lends partial support to hypothesis 7. In addition, because increased perceptions of credibility predicted more positive attitudes, hypothesis 8 was partially supported as well. Therefore, despite the fact the different types of news did not predispose users to take one processing route over the other nor did they lead to differing perceptions of credibility, these results still uphold ELM.

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Study Limitations

While overall results in this dissertation indicate the aggregators that use algorithms (like Google News) and social recommendations (like Facebook) to provide personalized news recommendation yielded more positive attitudes, and they heightened reading of diverse news subjects, there are some limitations to note. First, while steps were taken to make the experimental manipulations as naturalistic as possible (by designing web pages that mimicked the actual aggregators), the study did take place in a campus computer lab, which is a restricted experimental setting.

Therefore, the information-seeking behaviors observed may not represent participants’ typical behaviors with aggregators in more naturalistic settings. In addition, because participants knew their behaviors were being recorded, they might have searched for diverse news for socially desirable reasons rather than actual news interest.

Similarly, because two of the aggregators (Google News and Facebook) as well as the news website used for the control group (The Gainesville Sun) were well-known in comparison to the editor-gatekeeper aggregator used for the stimulus materials in this study (inshorts), familiarity could have had an impact on results. Inshorts was chosen for the editor-gatekeeper aggregator manipulation because it is an aggregating website, not an app (unlike Wildcard), and it is directed toward a broader audience in comparison to sites like The Drudge Report (Ciobanu, 2015; Levitt & Rosch, 2006). However, because findings were consistent that inshorts received the lowest attitude and perceived credibility scores, one explanation for these findings could be that participants did not like it or trust it as much because they were unfamiliar with it.

And finally, while the decreased number of significant results for type of news manipulations suggest that type of aggregation was the primary factor that elicited

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attitude and behavioral responses from participants in the study, maybe having more topic options, in order to further replicate a typical aggregation experience, could have led to some significant differences between civic affairs and entertainment news. While a restricted experimental setting and random assignment offer the potential to control for nuisance variables (Keppel & Wickens, 2004), it is impossible to account for everything. Therefore, this could be one of many exogenous or uncontrolled factors that may have affected the results.

Suggestions for Future Research

Future research could aim to investigate the relationship between type of news and attitudes and information-seeking behavior further in order to explore if there are any topics that lead to differences in results, even when there is a presence of aggregation. One way to do that might be to further analyze the topics participants search for after being exposed to different types of news recommended by an aggregator. For example, follow-up analyses of the screen recordings used to document information-seeking behavior could be conducted to see whether participants limited their time to primarily civic affairs news topics, entertainment news topics, or whether there are a fairly even mix of both. Findings from a study like this could also provide further evidence as to whether aggregators encourage entertainment newsreading at the expense of civic affairs.

Future research could also be done to examine the effects of different types of news aggregators on different age groups and populations. While fears of a news bubble are magnified for young adults because they are the biggest users of aggregators and social media, they are not the only ones susceptible to selective exposure and the news bubble. In fact, maybe older populations are more likely to

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experience these effects because they are not as familiar with news aggregators nor do they rely on them as their primary source of news like younger generations have become accustomed to (American Press Institute, 2015; Raine & Purcell, 2010).

And most importantly, research could be conducted related to the news bubble.

This study does shed light on the further consequences expected from the news bubble

(such as increased political polarization, increased exhibitions of narcissism, and a hindered democracy) by answering the first question in this chain of events: does aggregation lead to selective exposure? However, steps could be taken to figure out how to effectively measure whether news aggregators could lead users to become more polarized in their political opinions and/or more narcissistic. Also, research could determine whether aggregation leads to more or less civic participation. One way to accomplish these goals could be to test the effects of aggregation over time (not just in one particular instance).

In addition, a study could measure participants pre-existing attitudes (instead of their news interests) first, and then tailored news content to match those interests to see how it affects attitudes and information-seeking behavior. This study could help answer an important research question: does the news find you, or do you find the news? In other words, this study could aim to uncover whether people learn their attitudes from news stories or whether they seek out news that reinforces pre-existing beliefs. Also, by focusing on a political topic, a study like this could provide further evidence for how aggregation affects civic engagement. Outside of these studies, however, future research will always be needed while aggregators are a norm for newsreading because, as the announcement about changes to Facebook’s newsfeeds suggests (Sunstein,

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2016), companies change their algorithms over time, which could also change their impacts.

Conclusion

Overall, the findings in this dissertation indicate that popular aggregators like

Google News and Facebook do not have a narrowing impact on information-seeking behavior as initially expected. As a result, it can be concluded that aggregation does not lead to selective exposure. It, instead, engages young adults in news and enhances their interest in multiple news topics, which also means there is not enough evidence to suggest that the greater consequences feared from the news bubble are happening, especially for young adults. In addition, these results indicate that aggregators may not require some kind of balancing mechanism in order to avoid a closed system that disconnects citizens from democracy because they hold the potential to engage more people in news more quickly and more often, thus offering potential to produce a public more literate in civic affairs.

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APPENDIX A EXPERIMENT INFORMED CONSENT

Informed Consent

Protocol Title: The Aggregation Effect Please read this consent document carefully before you decide to participate in this study.

Purpose of the research study: The purpose of this study is to examine people's attitude and behavioral responses to different types of aggregators that provide personalized news recommendations and different types of news.

What you will be asked to do in the study: You will be asked to read a news story and answer a series of questions, including your thoughts about the story and basic demographic information. Additionally, you will be spending a few minutes browsing Google News.

Time required: 20-30 minutes

Risks and Benefits: There are no risks associated with your participation in this survey beyond what you may experience in everyday life. There are also no foreseeable benefits to you as the participant in regards to the outcome of this research.

Compensation: Some students will receive extra course credit toward this experiment. If your instructor has offered extra credit points, they will inform you of the amount you will receive. If you are completing this survey for extra course credit, please make sure to fill out the final page of the questionnaire with your student ID number and instructor’s name.

Confidentiality: Your identity will be kept confidential to the extent provided by law. Your responses to the questions will be anonymous and will be aggregated with other people’s responses and will not be used to identify you individually. All responses will be destroyed once the study is completed.

Voluntary participation: Your participation in this study is completely voluntary. There is no penalty for not participating.

Right to withdraw from the study: You have the right to withdraw from the study at any time without consequence.

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Agreement: By clicking next, you agree that you have read the procedure described above and voluntarily agree to participate in the study.

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APPENDIX B GOOGLE NEWS STIMULUS MATERIALS

Figure B-1. Google News Stimulus Materials (Traffic and Weather).

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Figure B-2. Google News Stimulus Materials (Jobs and Unemployment).

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Figure B-3. Google News Stimulus Materials (Crime and Public Safety).

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Figure B-4. Google News Stimulus Materials (National Politics).

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Figure B-5. Google News Stimulus Materials (Technology and Science).

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Figure B-6. Google News Stimulus Materials (Music, Movies, and TV Shows).

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Figure B-7. Google News Stimulus Materials (Celebrities).

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Figure B-8. Google News Stimulus Materials (Sports).

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Figure B-9. Google News Stimulus Materials (Cooking).

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Figure B-10. Google News Stimulus Materials (Health and Fitness).

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APPENDIX C FACEBOOK STIMULUS MATERIALS

Figure C-1. Facebook Stimulus Materials (Traffic and Weather).

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Figure C-2. Facebook Stimulus Materials (Jobs and Unemployment).

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Figure C-3. Facebook Stimulus Materials (Crime and Public Safety).

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Figure C-4. Facebook Stimulus Materials (National Politics).

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Figure C-5. Facebook Stimulus Materials (Technology and Science).

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Figure C-6. Facebook Stimulus Materials (Music, Movies, and TV Shows).

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Figure C-7. Facebook Stimulus Materials (Celebrities).

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Figure C-8. Facebook Stimulus Materials (Sports).

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Figure C-9. Facebook Stimulus Materials (Cooking).

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Figure C-10. Facebook Stimulus Materials (Health and Fitness).

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APPENDIX D INSHORTS STIMULUS MATERIALS

Figure D-1. Inshorts Stimulus Materials (Traffic and Weather).

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Figure D-2. Inshorts Stimulus Materials (Jobs and Unemployment).

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Figure D-3. Inshorts Stimulus Materials (Crime and Public Safety).

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Figure D-4. Inshorts Stimulus Materials (National Politics).

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Figure D-5. Inshorts Stimulus Materials (Technology and Science).

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Figure D-6. Inshorts Stimulus Materials (Music, Movies, and TV Shows).

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Figure D-7. Inshorts Stimulus Materials (Celebrities).

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Figure D-8. Inshorts Stimulus Materials (Sports).

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Figure D-9. Inshorts Stimulus Materials (Cooking).

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Figure D-10. Inshorts Stimulus Materials (Health and Fitness).

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APPENDIX E GAINESVILLE SUN STIMULUS MATERIALS

Figure E-1. Gainesville Sun Stimulus Materials (Civic Affairs).

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Figure E-2. Gainesville Sun Stimulus Materials (Entertainment).

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APPENDIX F NEWS STORY STIMULUS MATERIALS

Figure F-1. Traffic and Weather News Story.

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Figure F-2. Jobs and Unemployment News Story.

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Figure F-3. Crime and Public Safety News Story.

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Figure F-4. National Politics News Story.

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Figure F-5. Technology and Science News Story.

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Figure F-6. Music, Movies, and TV Shows News Story.

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Figure F-7. Celebrities News Story.

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Figure F-8. Sports News Story.

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Figure F-9. Cooking News Story.

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Figure F-10. Fitness and Health News Story.

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APPENDIX G EXPERIMENT QUESTIONNAIRE

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*Participants were only shown one of the two previous questions. Then the stimulus materials followed immediately afterward.

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APPENDIX H INFORMATION-SEEKING BEHAVIOR CODE BOOK

THE AGGREGATION EFFECT: Code Book

1. UF ID Number: ______

The UF ID number can be found on the file name of the information-seeking behavior screen recording for each participant.

2. Time Spent on Similar Topics: ______

This section is designed to measure how much time participants spent reading information related to what they chose during experimental sessions. Which topic they chose is also indicated on the file name of the information-seeking behavior screen recording for each participant.

The subject areas include: traffic and weather, jobs and unemployment rates, crime and public safety, national politics, technology and science, music, movies, and TV shows, celebrities, sports, cooking, and fitness and health.

Every time, you see a story that matches the subject area the participant initially read, write down the total number of seconds he or she spent reading the story. At the end of each recording, total the number of seconds.

3. Subjects of Each Story

This section is designed to measure the news subjects of each story participants read. Choose one category for each story based on the most dominant characteristics of the story.

Civic Affairs News: Number of Stories: Business and the economy ______Crime and public safety ______Foreign or international news ______Health care and medical information ______Information about my city, town, or neighborhood ______National politics and government ______Religion and faith ______School and education ______Science and technology ______Social issues like abortion, race, and gay rights ______The environment and natural disasters ______Traffic and weather ______

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Entertainment News: Number of Stories: Celebrities or pop culture ______Food and cooking ______Health and fitness ______Local restaurants or entertainment ______Music, TV, and movies ______Sports ______Style, beauty, and fashion ______The arts and culture ______

4. Total Number of Different Subjects: ______

After coding the subject area for each story, add together the number of different subjects each participant read. For example, if someone read two political stories, a sports story, and a cooking story, the number 3 would be entered because they read three separate subjects.

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BIOGRAPHICAL SKETCH

Lauren Furey received her Ph.D. from the College of Journalism and

Communications at the University of Florida in August 2016. Her research focuses on media literacy and effects, specifically how journalists, news content and features, like personalization, interactivity, and platform, affect audience attitudes, opinions, behavior and comprehension of news. Lauren completed her Master of Arts in journalism with a focus in business and economics reporting at Columbia University’s Graduate School of

Journalism, and she received her bachelor's in communications with a concentration in journalism at the University of North Florida. In her professional career, Lauren worked as a correspondent with the Jacksonville Business Journal and published articles in newspapers like The Florida Times-Union, Lubbock Avalanche Journal, Daily News

Egypt, Daily Star Egypt, and Pakistan Christian Post.

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