EXPOSURE AND SHARING EFFECTS 1

Issues, Involvement, and Influence:

Effects of Selective Exposure and Sharing on Polarization and Participation

Benjamin K. Johnsona* Rachel L. Neob Marieke E. M. Heijnenc Lotte Smitsc Caitrina van Veenc

aUniversity of Florida, Department of Advertising 1885 Stadium Road Gainesville, FL 32611, USA [email protected]

bUniversity of Hawaii at Manoa, School of Communications 2550 Campus Road Honolulu, HI 96822, USA

cVrije Universiteit Amsterdam, Department of Communication Science De Boelelaan 1081 1081 HV Amsterdam, Netherlands

*Corresponding author.

Author Note: No competing interests exist.

Note: This is a post-print copy for research purposes only.

It is an uncorrected version of the accepted manuscript before proofing and formatting.

Please consult the final published version for citation, quotation, and pagination.

Updated 2 October 2019

Please cite as:

Johnson, B. K., Neo, R. L., Heijnen, M. E. M., Smits, L., & van Veen, C. (in press). Issues, involvement, and influence: Effects of selective exposure and sharing on polarization and participation. Computers in Human Behavior. doi:10.1016/j.chb.2019.09.031 EXPOSURE AND SHARING EFFECTS 2

Abstract

Although research has amply demonstrated that people exhibit confirmatory biases associated with exposure and information sharing on social media, there is a lack of research attempting to parse out the respective effects of selective exposure and sharing on political outcomes, especially in non-U.S. contexts. In this experiment, we tested the extent of confirmation bias in

Dutch Facebook users’ selection and sharing of opinionated news about three political issues.

The relative contributions of selecting versus sharing pro-attitudinal (and counter-attitudinal) messages were assessed for their influences on attitude polarization and political participation.

Value- and impression- involvement were considered as moderating factors. Findings indicate that a confirmation bias is much more consistently observed in selective sharing than in selective exposure. Second, pro-attitudinal selective sharing is a more robust predictor of political outcomes than pro-attitudinal selective exposure. Third, the effects of selective sharing and exposure on political outcomes depend more on value involvement than impression relevant involvement. Finally, between-topic differences were evident for the extent of confirmation bias and its effects on political outcomes.

Keywords: selective exposure, selective sharing, polarization, online news, online participation, involvement, experiment

EXPOSURE AND SHARING EFFECTS 3

1. Introduction

The existence of a confirmation bias in the selection and sharing of news and information is well-documented (Hart et al., 2009; Knobloch-Westerwick, 2015). Selective exposure, a tendency of people to choose and spend more time reading attitude-consistent information, can yield effects on outcomes such as attitude polarization, feelings toward outgroups, voting intentions, and political participation intentions (Feezell, 2016; Garrett et al., 2014; Wojcieszak,

Bimber, Feldman, & Stroud, 2016). In addition to attitude-consistent selective exposure, people may also engage in attitude-consistent selective sharing. The widespread adoption of social network sites such as Facebook and Twitter have created new platforms for partisan sharing of news and political messages (Beam, Hutchens, & Hmielowski, 2016; Oeldorf-Hirsch & Sundar,

2015; Weeks, Lane, Kim, Lee, & Kwak, 2017).

Despite increasing scholarly attention devoted to understanding how selective exposure or selective sharing on social media influence political outcomes (e.g., Shin & Thorson, 2017;

Weeks et al., 2017), existing research on this topic still has several key limitations. First, most studies have only examined either selective exposure or selective sharing as focal variables, but not both. Some scholars have argued that selective sharing is a more overtly clarion reflection of partisan leanings than selective exposure (Shin & Thorson, 2017) and that it involves greater behavioral commitment (Lane et al., 2019). Our study thus tests the effects of (a) attitudinal stance on selective exposure versus selective sharing and (b) how both selective exposure and selective sharing on social media affect political outcomes among the same individuals. This allows us to assess which of these two confirmation biases is a stronger predictor of political outcomes.

Second, few studies on selective exposure or sharing have accounted for the moderating EXPOSURE AND SHARING EFFECTS 4 role of involvement (Liao & Fu, 2013; Wheeless, 1974). Research has shown that involvement levels affect partisan-based political behaviors (Perloff, 1989). However, not everyone exhibits deep-seated levels of value involvement toward any given political issue or exhibits similar levels of concern about others’ evaluations of their political preferences.

Third, most studies on selective exposure (Bakshy, Messing, & Adamic, 2015) or sharing on social media (e.g., Weeks et al., 2017) have been conducted in the United States. The United

States has a unique two-party political system with an electorate that is heavily polarized along party lines (Garrett et al., 2014). It is imperative to conduct more experimental research outside of the U.S. in order to provide stronger evidence for these two aforementioned confirmation biases, especially in democracies such as the Netherlands with parliamentary systems involving a fragmented diversity of political parties that must typically work together in coalition to form a functioning government (Pellikaan, de Lange, & van der Meer, 2018). In the Netherlands, specifically, there have been well-designed tests of partisan selective exposure using panel data

(e.g., Bos, Kruikemeier, & de Vreese, 2016), a quasi-experimental design (Trilling, van

Klingeren, & Tsfati, 2017), and experiments testing the effects of frames on selective exposure

(Brenes Peralta, Wojcieszak, Lelkes, & de Vreese, 2017; Hameleers, Bos, & de Vreese, 2018).

However, there is little to no evidence to date on selective sharing in Dutch contexts, and more research is needed on the process of—and potential for—polarization in multi-party systems such as the Netherlands.

Fourth, most studies have examined selective exposure exclusively in the context of highly polarized topics (e.g., Westerwick, Johnson, & Knobloch-Westerwick, 2017). In our study, we use several topics which vary in their existing polarization and salience (cf. Y. M.

Kim, 2009). EXPOSURE AND SHARING EFFECTS 5

To bridge these research gaps outlined above, this study reports the results of an experiment in which compares the relative influence of selective exposure and sharing (to pro- and counter-attitudinal political content on Facebook) on political participation and opinion polarization. In particular, we investigate these processes in the contexts of three political topics chosen for their varying relevance and salience for a Dutch sample: the relationship between

Ukraine and the EU; the entry of refugees into the Netherlands; and equal pay for men and women. Moreover, we investigate two forms of attitudinal involvement—value-involvement and impression-involvement—as moderators not only of selectivity, but also of selectivity’s effects on polarization and participation.

2. Literature review

2.1. The effect of information stance on selective exposure

Numerous studies have shown that individuals’ attitudinal stances will guide the composition and degree of their information exposure (Garrett, 2009a; Hart et al., 2009;

Knobloch-Westerwick, 2015). Notably, confirmation bias describes the tendency where people will gravitate toward information that aligns with their existing views (Lord, Ross, & Lepper,

1979). The psychological processes underlying confirmation bias can be explained by the theory of cognitive dissonance (Festinger, 1957), which posits that people strive to minimize psychological discomfort arising from performing actions that are inconsistent with their values and beliefs. One such action is that of counter-attitudinal political news consumption (Jeong, Zo,

Lee, & Ceran, 2019). Although people do not avoid counter-attitudinal political information entirely (Garrett, Carnahan, & Lynch, 2013), they nonetheless spend less time reading counter- attitudinal than pro-attitudinal news stories (e.g., Frey, 1986; Garrett, 2009a).

Some scholars have argued that the control afforded by online platforms facilitates the EXPOSURE AND SHARING EFFECTS 6 selective consumption of pro-attitudinal political information (Garrett, 2009b). Likewise, the sheer volume of content made available by digital platforms may heighten the ability, desire, or necessity for user selectivity in news use (S. Lee, Lindsey, & Kim, 2017). Indeed, research has consistently demonstrated robust support for confirmation bias in online contexts such as social media (e.g., Westerwick et al., 2017).

The term selective exposure is often used to refer to an observed confirmation bias in information exposure, where pro-attitudinal content is selected at a higher rate (Stroud, 2008) or even exclusively, in some formulations. We take a broader definition of selective exposure, following Knobloch-Westerwick (2015), such that it refers to any systematic pattern in media use. This accounts for a broader set of motivations for media use (including accuracy, impression management, novelty, entertainment, etc.) as well as the possibility that some individuals, under certain situations, may exercise a selective preference for counter-attitudinal messages (e.g., if that information provides more utility; Knobloch-Westerwick & Kleinman, 2012). However, our starting point for this investigation is a general expectation that a confirmation bias will be evident in exposure to political information presented in a social media context.

H1: People will spend more time reading pro-attitudinal than counter-attitudinal news

stories on social media.

2.2. The effect of selective exposure on political participation and opinion polarization

Next, we can expect pro- and counter-attitudinal political information consumption to have effects on variables such as opinion polarization and political participation. Research has demonstrated a robust positive link between pro-attitudinal news consumption and political participation (Knobloch-Westerwick & Johnson, 2014; Stroud, 2010). And, the more people attend to attitude-reinforcing political information, the more they will exhibit strong and even EXPOSURE AND SHARING EFFECTS 7 extreme attitudes. Such confidence emboldens people to take political action, so that attitude- affirming news on social media is likely to foster both online and offline political participation

(Beam, Hutchens, & Hmielowski, 2018; Feezell, 2016; Y. Kim & Chen, 2016).

Social media provide ease of access to content, coupled with the presence of social peers, making it an important context for interacting with news and political information. As such, social media appear to be especially consequential for political participation (Bode, Vraga,

Borah, & Shah, 2014; Boulianne, 2015). Furthermore, consuming attitude-affirming news increases the salience of a person’s political identity, thereby causing a person to think and behave in ways that signify their party affiliation (Stroud, 2010). For instance, selective exposure causes people to develop more polarized opinions on controversial issues that align with their party’s values (Y. Kim, 2015; Stroud, 2010; Westerwick, Johnson, & Knobloch-Westerwick,

2017). From this, we can expect that accessing pro-attitudinal news in a social media context will increase participation and opinion polarization. We hypothesize that:

H2: The amount of time spent reading pro-attitudinal news stories will be positively

associated with (a) online political participation, (b) offline participation, and (c) opinion

polarization.

By contrast, counter-attitudinal information consumption can cause people to challenge the views espoused by pro-attitudinal information (Garrett et al., 2014). People might gain a better understanding of their political opponents’ issue positions through exposure to counter- attitudinal political information (Mutz, 2002). As such, counter-attitudinal information consumption might make people hold ambivalent issue attitudes and fewer intentions to engage in participatory political activities (Mutz, 2002).

In some circumstances, exposure to counter-attitudinal information via social media EXPOSURE AND SHARING EFFECTS 8 might increase participation intentions (Y. Kim & Chen, 2016), especially if incivility is present

(Hwang, Kim, & Huh, 2014). And, some research on the relationship between counter-attitudinal information exposure and opinion polarization shows null or nuanced results, such that exposure to ideologically diverse content online might not influence polarization outcomes (J. Lee, Choi,

Kim, & Kim, 2014) or might yield a mix of polarization and depolarization effects, depending on the context and particular political issue (Y. Kim, 2015).

Yet, evidence generally shows that contact with counter-attitudinal information diminishes political behavior (Matthes, 2012) and weakens (i.e., depolarizes) attitudes

(Westerwick et al., 2017). In the social media context, recent surveys with Americans found that counter-attitudinal discussion on social media was linked to less political participation (Lu,

Heatherly, & Lee, 2016) and counter-attitudinal news exposure on Facebook produced depolarization over time (Beam et al., 2018). To that end, the evidence suggests that counter- attitudinal political information exposure will reduce the degree to which issue opinions are polarized along partisan lines. The following hypothesis is proposed:

H3: The amount of time spent reading counter-attitudinal news stories will be negatively

associated with (a) online political participation, (b) offline participation, and (c) opinion

polarization.

2.3. The effect of information stance on selective sharing

The presence of an expressive goal has been shown to enhance confirmation bias in message exposure (Smith, Fabrigar, Powell, & Estrada, 2007). This need to present one’s identity and opinions are even more pertinent when it comes to selective sharing, as the sharing is often directed at a large audience (if not public) and plays a role in individual self-presentation

(Coppini et al., 2017). In the context of social media, news sharing encompasses outwardly EXPOSURE AND SHARING EFFECTS 9 expressive activities such as recommending, posting, or forwarding various forms of news and political information to the members of one’s social network (Kümpel, Karnowski, & Keyling,

2015), plus other interactions such as comments or likes which signal engagement with news.

Network analyses of social media data have consistently shown that people selectively share attitude-affirming political information such as hyperlinked blogposts (Adamic & Glance,

2005) or tweets (Barbera, Jost, Nagler, Tucker, & Bonneau, 2015; Colleoni, Rozza, &

Arvidsson, 2014). However, such big data analyses have certain limitations. For instance, big data analyses are often restricted to publicly available data, potentially yielding unrepresentative samples (Garrett, 2013). Thus, it is equally important to corroborate findings from analyses of big data with research carried out using other types of methodologies such as surveys or experiments (Garrett, 2013). Furthermore, survey and experimental designs allow for greater inferences about mental processes and causal steps, and have unique strengths with regard to selective exposure research (Clay, Barber, & Shook, 2013). As such, this study uses an online experiment embedded within a web-based behavior-tracking study of social media news use to examine whether people are more likely to share pro-attitudinal social media news items over counter-attitudinal news items.

Furthermore, some scholars have argued that pro-attitudinal information sharing is a much more consistently observed phenomenon on social media platforms than selective exposure (Shin & Thorson, 2017). People do not always fully attune themselves to pro- attitudinal information (Garrett et al., 2013). There are times when they will deliberately attend to counter-attitudinal political information, e.g., because they want to develop arguments against opposing viewpoints (Knobloch-Westerwick & Kleinman, 2012). In contrast, sharing counter- attitudinal information could create conflict or misunderstanding with one’s network. Pro- EXPOSURE AND SHARING EFFECTS 10 attitudinal information sharing on social media represents an overt reflection of one’s partisan values (Shin & Thorson, 2017). Notably, Shin and Thorson (2017) found that people are more likely to re-tweet fact-checking messages that favor the in-party over those that favor the out- party as an overt statement of loyalty and support for their in-party. All in all, people may be less likely to share counter-attitudinal information than to read counter-attitudinal information.

H4: People will be more likely to share pro-attitudinal than counter-attitudinal news

stories on social media.

RQ1: Is the sharing of pro- versus counter-attitudinal news stories on social media a

stronger pattern than exposure to pro- versus counter-attitudinal news stories?

2.4 The effect of selective sharing on political participation and political polarization

Current research has mostly focused on establishing the positive relationship between generalized political information sharing and political participation (Gil de Zúñiga, Molyneux, &

Zheng, 2014; Vaccari et al., 2015). Online information sharing increases political participation by increasing issue knowledge and enabling people to co-organize political events (Kwak,

Williams, Wang, & Lee, 2005; F. Lee, Chen, & Chan, 2017; Valenzuela, 2013). Given that selective sharing of attitude-affirming information is a bold, outward expression of one’s partisan leanings (Shin & Thorson, 2017), it is likely that such selective sharing will make one’s partisan identity even more cognitively salient than selective exposure alone. Although there is some cross-sectional evidence that news sharing on social media may increase network heterogeneity

(i.e., contact with others with divergent opinions; Choi & Lee, 2015), it can foster relationships with like-minded others. News sharing can also produce feelings of certainty and efficacy

(Oeldorf-Hirsch & Sundar, 2015). It is a logical inference that the sharing of pro-attitudinal information will have stronger effects than pro-attitudinal news exposure on empowering people EXPOSURE AND SHARING EFFECTS 11 to collaborate with other in-party members on events or causes benefiting their political party.

Relatedly, the sharing of pro-attitudinal information is likely to be a more consistent predictor of opinion polarization than pro-attitudinal news consumption. As an outward display of issue stance, the sharing of pro-attitudinal information is arguably a much more effective catalyst of partisan beliefs and affiliation than selective exposure (Shin & Thorson, 2017). When people engage in such partisan-based social categorization, opinion polarization occurs as they develop extreme views about group norms toward issue positions (Hogg, 2014). Panel survey data suggest that social media use contributes to political engagement and subsequent polarization of attitudes (C. Lee, Shin, & Hong, 2018). This study proposes that:

H5: The sharing of pro-attitudinal news stories on social media will be positively

associated with (a) online political participation, (b) offline political participation, and (c)

opinion polarization.

RQ2: Is the sharing of pro-attitudinal news stories on social media a more consistently

positive predictor of (a) online political participation, (b) offline political participation,

and (c) opinion polarization than exposure to pro-attitudinal news stories?

By contrast, the effects of sharing counter-attitudinal social media news on political participation and opinion polarization are less understood. As outlined above, people are more likely to share pro-attitudinal over counter-attitudinal information (Shin & Thorson, 2017).

However, scholars have pointed out that the act of sharing counter-attitudinal political information indicates an outward openness toward embracing, or at the very least reflecting upon, alternative political views (Lane et al., 2019). This suggests that people who openly share counter-attitudinal political information are willing to openly admit that they are politically undecided, and will consequently be less likely to take concrete political action. EXPOSURE AND SHARING EFFECTS 12

Furthermore, when people are unabashedly willing to consider the validity of alternative viewpoints (Mutz, 2002), it is likely that they will not hold extreme issue positions that conform to partisan values. In addition, it takes considerably more commitment to share counter- attitudinal political information on social media than consume counter-attitudinal political information (Lane et al., 2019). It is thus plausible that sharing counter-attitudinal political information will be even more effective than consuming counter-attitudinal political information at reducing political participation and attitude extremity. As such, we propose:

H6: The sharing of counter-attitudinal news stories on social media will be negatively

associated with (a) online political participation, (b) offline political participation, and (c)

opinion polarization.

RQ3: Is the sharing of counter-attitudinal news stories on social media a more

consistently negative predictor of (a) online political participation, (b) offline political

participation, and (c) opinion polarization than exposure to counter-attitudinal news

stories?

2.5. The moderating role of involvement

The construct of involvement has played a crucial role in explaining how message processing affects judgments across many domains of media and communication research (e.g.,

Brown & Basil, 1995; Kwak, 1999; Slater & Rouner, 1992, 1996). Scholars have identified three main types of involvement: value relevant, outcome relevant, and impression relevant (Cho &

Boster, 2005). Although all three types of involvement trigger attitudes related to one’s self- concept, each type of involvement results in different persuasive outcomes (Cho & Boster,

2005). To date, few studies have examined how these specific types of involvement affect the degree to which selective exposure and sharing behaviors predict political outcomes. This study EXPOSURE AND SHARING EFFECTS 13 focuses on examining two types of involvement, value-relevant and impression-relevant involvement, as moderators of the effects outlined in the aforementioned hypotheses.

2.5.1. Value-relevant involvement

This dimension of involvement is defined as “the psychological state that is created by the activation of attitudes that are linked to important values” (Johnson & Eagly, 1989, p. 290).

A person’s political identity and values are integral to their self-concept (Sherif, Sherif, &

Nebergall, 1965), and will inform their judgments and political information consumption behaviors (Perloff, 1989). When value-relevant involvement is high, a person’s range of acceptable beliefs narrows, and they tend to reject a wide set of attitude positions (Cho & Boster,

2005; Fazio, Zanna, & Cooper, 1977). Under such conditions, these types of people will be prone to engaging in behaviors or exhibiting attitudes that align with their values or beliefs, and correspondingly just as likely to avoid adopting value-inconsistent behaviors and attitudes (Fazio et al., 1977).

Given the above, we can predict that individuals with high levels of value involvement will be especially likely to engage in informational consumption or expressive behaviors that affirm their political values, such as selectively consuming or sharing pro-attitudinal social media news. In addition, people with high levels of value involvement are particularly likely to be politically active, and tend to have very extreme opinions (Perloff, 1989). It is plausible that such pro-attitudinal political information consumption and sharing behaviors will translate into political action and opinion polarization at high levels of value involvement. By contrast, consuming and sharing counter-attitudinal political information are behaviors that ostensibly conflict with one’s political values (e.g., Lane et al., 2019). People with high levels of value involvement are more defensively-oriented and unlikely to allow counter-attitudinal information EXPOSURE AND SHARING EFFECTS 14 to shape their political opinions or decisions to partake in political activities. As such, we predict that the negative effects of counter-attitudinal news exposure and sharing on political participation and opinion polarization will be weakest when value involvement is high.

H7: People with higher levels of value involvement will be more likely to (a) spend time

reading and (b) share pro-attitudinal versus counter-attitudinal news stories on social

media.

H8: People with higher levels of value involvement will show stronger positive effects of

pro-attitudinal news exposure on (a) online political participation, (b) offline political

participation, and (c) opinion polarization.

H9: People with higher levels of value involvement will show weaker negative effects of

counter-attitudinal news exposure on (a) online political participation, (b) offline political

participation, and (c) opinion polarization.

H10: People with higher levels of value involvement will show stronger positive effects

of pro-attitudinal news sharing on (a) online political participation, (b) offline political

participation, and (c) opinion polarization.

H11: People with higher levels of value involvement will show weaker negative effects

of counter-attitudinal news sharing on (a) online political participation, (b) offline

political participation, and (c) opinion polarization.

2.5.1. Impression-driven involvement

According to Johnson and Eagly (1989), impression involvement is a form of attitudinal involvement that gauges the degree to which individuals inherently care about the social ramifications of their opinions. Unlike value involvement, which focuses on an individual’s deep-seated values or issue attitudes, impression-driven involvement centers on an individual’s EXPOSURE AND SHARING EFFECTS 15 concern about how others perceive them (Cho & Boster, 2005). Thus, people who exhibit high levels of impression involvement tend to deliberately engage in public displays of behavior that conform to expectations of important referent groups (Leippe & Elkin, 1987).

Sharing pro-attitudinal social media news can be construed as a public demonstration of in-party solidarity in front of an imagined audience (Liu, Rui, & Cui, 2017; Shin & Thorson,

2017). Furthermore, participatory political activities can be regarded as outward indications of the degree to which a person embodies the values of their political affiliation, or seeks greater affiliation (Huddy, Mason, & Aarøe, 2015). With this in mind, it is plausible that those with high levels of impression involvement will be most likely to share pro-attitudinal over counter- attitudinal social media news, and in turn become more politically active after selectively sharing pro-attitudinal news as explicit signs that they conform to in-party norms. By contrast, sharing counter-attitudinal political news is antithetical to partisan group norms (Lane et al., 2019).

Consequently, individuals who exhibit high levels of impression involvement might be reluctant to openly share counter-attitudinal political news and allow such news items to undermine their desire to participate in political activities. When they do share such content and make a semi- public commitment to counter-attitudinal information, it is especially likely to shift their opinions and dampen their participation intentions. As such, the negative effects of counter- attitudinal social media news exposure on online and offline political participation is likely to be strongest among those with high levels of impression involvement.

Additionally, holding polarized issue opinions is arguably a more inward manifestation of one’s political values than political participation (Huddy et al., 2015). Regardless of opinion extremity, people can choose to keep their opinions to themselves without letting others know

(Cho & Boster, 2005). However, shared opinions are likely to lead to commitment and EXPOSURE AND SHARING EFFECTS 16 reinforcement (Lane et al., 2019). Thus, the moderating role of impression involvement on the relationship between attitudinal stance and opinion polarization via selective sharing is different from that of value involvement. Those who desire to make a positive impression on their social network (Smith et al., 2007) should shift their beliefs and behaviors in the direction of the news they have consumed, and especially the content they have shared. The following hypotheses are proposed:

H12: People with higher levels of impression involvement will be more likely to (a)

spend time reading and (b) share pro-attitudinal versus counter-attitudinal news stories on

social media.

H13: People with higher levels of impression involvement will show stronger positive

effects of pro-attitudinal news exposure on (a) online political participation, (b) offline

political participation, and (c) opinion polarization.

H14: People with higher levels of impression involvement will show stronger negative

effects of counter-attitudinal news exposure on (a) online political participation, (b)

offline political participation, and (c) opinion polarization.

H15: People with higher levels of impression involvement will show stronger positive

effects of pro-attitudinal news sharing on (a) online political participation, (b) offline

political participation, and (c) opinion polarization.

H16: People with higher levels of impression involvement will show stronger negative

effects of counter-attitudinal news sharing on (a) online political participation, (b) offline

political participation, and (c) opinion polarization.

The study’s hypotheses and research questions are visualized in Figure 1, which illustrates the within-subjects differences in pro- and counter-attitudinal exposure and sharing with brackets on EXPOSURE AND SHARING EFFECTS 17 the left-hand side of the model, the between-subjects effects of exposure and sharing on the dependent variables with paths on the right-hand side of the model, and the moderating effects of value and impression involvement on main effects indicated with the moderator variables at the bottom of the figure.

Figure 1. Conceptual model with hypotheses and research questions. Brackets represent within-subjects effects

(tested with paired-sample t-tests, by topic) and paths to dependent variables represent between-subjects effects

(tested with a regression model for each DV, by topic; results appear in Tables 1 and 2). Value and impression involvement were tested with subsequent ANCOVAs and regression models. H7a tests moderation of H1, H7b tests moderation of H4, H8a tests moderation of H2a, H8b tests moderation of H2b, and so on.

3. Method

An online experiment was conducted, using a 3 (issue: Ukraine vs. refugees vs. equal EXPOSURE AND SHARING EFFECTS 18 pay) x 2 (stance: pro- vs. counter-attitudinal) within-subjects manipulation of topic and stance. A free-choice paradigm (Hastall & Knobloch-Westerwick, 2013) allowed participants to freely select articles to read and subsequently express intentions to share. The study data, syntax for analysis, and questionnaire are available at: https://osf.io/p9uzw/.

3.1. Participants

A convenience sample of adult Dutch Facebook users was recruited via invitations distributed through university students’ social media networks and flyers in campus and public settings. As an incentive, participants were entered into a drawing for €5 giftcards. A total of 399 individuals completed the questionnaire. Three cases were removed because of invalid self- reported age, another eight were removed because elapsed time to submit the survey was +/-3 SD

(i.e., > 5 hrs), and a further four were removed because total stimuli browsing time was +/-3 SD

(i.e., > 1 hr). This left a final sample for hypothesis testing of N = 384. This sample was 62.5% women, and ranged in age from 18 to 68 (M = 29.72, SD = 10.59). The majority (96.6%) had

Dutch nationality and were born in the Netherlands (93.5%). The majority (63.4%) had earned a college degree (HBO or WO in the Dutch system).

3.2. Procedure

Data were collected from 21 March to 5 April 2016, prior to a referendum on the EU’s relationship with Ukraine. After completing baseline attitudes and distractors, participants were presented with an overview page of news articles, from which they could select one or more articles to read. Participants reported sharing intentions, post-exposure attitudes, participation intentions, involvement, and other variables. In general, participants reported moderate attitudes toward the referendum, polarized attitudes toward refugees, and positive attitudes toward pay equality (see details in Section 3.4). Neutral individuals on the three topics were omitted from EXPOSURE AND SHARING EFFECTS 19 analysis.

3.3. Stimuli

Eight news articles were adapted from Dutch newspaper stories. Six reflected opposing stances on the three target issues of (a) affirming the EU-Ukraine association in a referendum,

(b) accepting more migrant refugees to the Netherlands, and (c) ensuring pay equality for men and women. The pro- or anti- stances of the articles was confirmed with a pre-test in which 20

Dutch university students rated headlines for their stance on the issue. The full articles were rewritten and edited for length and to match the position and tone of their headlines. Two distractor stories were also included, on soft news topics (single parenthood and monogamy).

Each article preview consisted of a headline and lead (word count: M = 32.75, SD =

3.65), a byline with name and timestamp (e.g., “N. Meyer, yesterday 11:03”). These eight previews were presented (in a randomized order) in two columns and four rows on an overview page, from which participants could select articles for further reading (they were instructed to choose at least one). If a particular article was selected for reading, they were directed to a full page that presented the entire article (word count: M = 651.5, SD = 43.83). When multiple articles were selected, the presentation order of articles was varied. Time spent reading individual articles was unobtrusively recorded by the questionnaire platform (Hastall &

Knobloch-Westerwick, 2013).

To enhance ecological validity, each article (and article preview) was assigned a number of Facebook likes and reactions, and each article was assigned a number of Facebook comments that appeared after the articles. The valence of reactions and comments was intended as a manipulation of opinion climate, but did not yield effects and is not analyzed further.

3.4. Measures EXPOSURE AND SHARING EFFECTS 20

3.4.1. T1 attitudes

Attitudes were measured with 101-point thermometers ranging from 0 = completely against to 100 = completely in favor. Toward the Ukraine referendum, “the association agreement between Ukraine and the EU,” M = 48.39, SD = 25.41; 5.7% did not report an attitude, 30.2% reported a negative attitude, 31.8% reported a neutral attitude at the midpoint of

50, and 32.3% reported a positive attitude. Toward refugees, “allowing more migrants in the

Netherlands,” M = 64.14, SD = 26.45; 4.4% did not report an attitude, 22.4% reported a negative attitude, 10.4% reported a neutral attitude at the midpoint, and 62.8% reported a positive attitude.

Toward equal pay, “pay equality between men and women,” M = 83.07, SD = 31.39; 4.2% did not report an attitude, 14.1% reported a negative attitude, 3.9% reported a neutral attitude at the midpoint, and 77.9% reported a positive attitude. In addition to these scalar attitudes, scores were trichotomized for the purpose of classifying exposure and sharing as pro-attitudinal or counter- attitudinal (excluding neutrals by topic).

3.4.2. Selective exposure

Participants selected an average of 2.85 (SD = 1.53) of the eight available articles: six political articles about the three target issues, plus two lifestyle news distractors. They spent M =

160.16 seconds, SD = 310.47, reading political articles and M = 45.58 seconds, SD = 77.45, reading distractor articles. The time spent on political articles, by issue, and by stance, was recoded to reflect whether the stance of the article was pro-attitudinal or counter-attitudinal for the individual participant (based on their trichotomized attitudes). This procedure yielded measures of time spent reading pro-attitudinal and counter-attitudinal articles for Ukraine, refugees, and pay equality.

3.4.3. News sharing intentions EXPOSURE AND SHARING EFFECTS 21

After the browsing opportunity, participants were presented with the headline for each article, one at a time, and asked three questions: “How likely is it that you would ‘like’ this article on Facebook?” “How is it likely that you would ‘share’ this article on your timeline on

Facebook?” and “How is it likely that you would post a comment under this article on

Facebook?” Participants indicated their likelihood, from 1 = not likely to 7 = very likely. These mean scores for these three items—liking, sharing, and commenting—were taken as an index of sharing intentions (cf. Alhabash, Almutairi, Lou, & Kim, 2019), with α’s ranging from .689 to

.874 for all six political articles. Sharing intentions were variable but relatively low, with a grand mean across articles of 1.61 (SE = .06). The least shared article type was counter-attitudinal

Ukraine (M = 1.31, SD = 0.75) and the most shared article type was pro-attitudinal refugees (M =

2.04, SD = 1.30).

3.4.4. T2 attitudes and polarization

Attitudes were re-administered for each issue with 101-point thermometers. Polarization was measured by subtracting t1 attitudes from t2 attitudes. This difference was multiplied by -1 for those with initially negative attitudes, so that a positive score on polarization indicated greater extremity in the direction of the original attitude, and a negative score indicated movement in the opposite direction (depolarization). On average, attitudes slightly depolarized from t1 to t2 for all three issues: Ukraine, M = -6.31, SD = 15.95, refugees, M = -1.39, SD =

12.87, and equal pay, M = -14.61, SD = 27.94.

3.4.5. Offline and online political participation

Participants indicated, regarding politics in general, “how willing you are to…” engage in different behaviors, 1= not at all willing to 7 = very willing. Three items (adapted from

Kruikemeier, van Noort, Vliegenthart, & de Vreese, 2014) measured online participation (“Sign EXPOSURE AND SHARING EFFECTS 22 a political petition online,” “Send an e-mail to someone to influence him or her politics,” and

“Have a discussion about politics online”), α = .671, M = 2.83, SD = 1.31, and five items

(adapted from Shah et al., 2007) measured offline participation (“Sign a political petition,”

“Participate in a political protest,” “Volunteer for a political party/issue/protest group,” and

“Have a face-to-face discussion about politics,” and “Place a poster or sticker or wear a button with a political message”), α = .807, M = 3.20, SD = 1.33. Online and offline participation were strongly correlated, r = .685, p < .001.

3.4.6. Voting intention

Participants were asked “If the Ukraine referendum were held today, would you go vote?” A majority of 53.9% said yes, 45.8% said no, and only one participant declined to answer.

3.4.7. Involvement

Items were adopted from the Cho and Boster (2005) measure of attitudinal involvement, and worded with regard to political attitudes. Six items measured value involvement (e.g., “My life would change if my political views were different”), α = .778, M = 3.88, SD = 1.18, and five items measured impression involvement (“People could judge me on the basis of my political convictions”), α = .671, M = 3.53, SD = 0.98. These two dimensions of involvement were moderately correlated, r = .323, p < .001.

3.4.8. Other measures

Other variables included in the dataset but not included in these analyses are Facebook use, news use, public opinion perceptions, perceived knowledge, affect, political interest, ideology, political affiliation, group identification, willingness to self-censor, and conflict avoidance.

3.5. Analysis plan EXPOSURE AND SHARING EFFECTS 23

Given the use of multiple topics, and the mix of within-subjects and between-subjects effects predicted, as well as the focus on three dependent variables, the data were analyzed in a series of tests. Each test was conducted separately for each topic, owing to different patterns of individuals holding neutral attitudes (e.g., an individual with a pro- or anti-refugee attitude might have a neutral attitude on pay equality).

First, the within-subjects effects of pro- versus counter-attitudinal exposure (H1) and sharing (H4) were tested with paired-sample t-tests. Then, the effects of exposure and sharing were modeled as predictors in regression models for the outcomes of online participation, offline participation, and opinion polarization. These regression models test the between-subjects main effects outlined in H2, H3, H5, and H6. Next, value involvement and impression involvement were tested as moderators of these main effects. Within-subjects ANCOVA tested whether value

(H7) and impression (H12) involvement moderated pro-attitudinal versus counter-attitudinal (a) exposure and (b) sharing. Interaction effects were probed with the MEMORE macro (Montoya &

Hayes, 2017). Then, to test the two involvement variables as moderators of the effects of exposure and sharing on the dependent variables (H8-11 and H13-16), regression models introduced involvement as moderating variables. In these between-subjects analyses, the

PROCESS macro (Hayes, 2018) was used to probe interactions with the Johnson-Neyman technique. Finally, Research Questions 1, 2, and 3 are addressed by comparing the relative strength of the main effects of exposure versus sharing.

4. Results

4.1. Within-Subjects main effects

To test the basic confirmation bias hypotheses of H1 and H4, paired-sample t-tests compared pro-attitudinal and counter-attitudinal exposure and sharing for each topic. Topics EXPOSURE AND SHARING EFFECTS 24 were analyzed separately owing to the exclusion of attitude-neutrals. For selective exposure, there was a significant effect for the refugee topic alone, t(326) = 3.52, p < .001, d = .195, where more seconds were spent reading pro-attitudinal (M = 33.89, SD = 65.24) than counter- attitudinal (M = 16.75, SD = 63.99) articles. No effect was evident for exposure to the referendum (p = .730, d = .022) or pay equality (p = .584, d = .041) topics. H1 was partially supported. For selective sharing, all three topics showed pro-attitudinal effects: the referendum, t(231) = 4.26, p < .001, d = .280, refugees, t(310) = 8.92, p < .001, d = .506, and pay equality, t(337) = 3.48, p < .001, d = .189. H4 was fully supported, and RQ1 was answered: selective sharing was stronger and more consistently observed than selective exposure.

Table 1. Relationships of Selective Exposure and Sharing with Online and Offline Participation

UA Ref Refugees Equal Pay UA Ref Refugees Equal Pay →Online →Online →Online →Offline →Offline →Offline β β β β β β Model 1: Main effects ΔR2=.198 ΔR2=.268 ΔR2=.242 ΔR2=.315 ΔR2=.384 ΔR2=.349 Pro-Att Exposure .016 -.028 .005 .007 .009 .017 Counter-Att Exposure .054 -.012 .029 .042 -.042 .078 Pro-Att Sharing .232** .286*** .065 .204** .261*** .125* Counter-Att Sharing .051 .085 .259*** -.011 -.049 .084 Value Involvement .298*** .314*** .348*** .461*** .481*** .506*** Impression Involvement .038 .066 .062 .076 .064 .063 Model 2: Value ΔR2=.050 ΔR2=.012 ΔR2=.021 ΔR2=.027 ΔR2=.007 ΔR2=.016 Involvement Interactions Value*Pro Exposure .649* .121 .344 .487 .238 .413* Value*Counter Expo. .417 -.337 .450 .163 -.143 -.048 Value*Pro Sharing -.332 -.203 .040 -.262 .013 -.167 Value*Counter Sharing -.112 .332 .313 -.390 .247 .288 Model 3: Impression ΔR2=.027 ΔR2=.002 ΔR2=.005 ΔR2=.018 ΔR2=.005 ΔR2=.004 Involvement Interactions Impress*Pro Exposure .095 -.130 .059 .135 -.196 .037 Impress*Counter Expo. .429 -.039 .270 .656* .176 -.269 Impress*Pro Sharing -.492 .179 .260 -.269 -.001 .312 Impress*Counter Shar. .964* -.048 -.019 .222 -.118 -.286 N = 231 310 336 231 310 336 Note. Standardized coefficients. UA Ref = Ukraine-EU association referendum. *p < .05, **p < .01, ***p < .001. Model 2 and Model 3 each build on the main effects in Model 1; Model 2 tested all value-involvement interactions simultaneously, and Model 3 tested all impression-involvement interactions simultaneously.

EXPOSURE AND SHARING EFFECTS 25

4.2. Between-Subjects main effects

Next, regression models for each topic (pro-attitudinal and counter-attitudinal exposure and sharing as predictors) tested effects on online and offline participation as well as opinion polarization, controlling for value and impression involvement (main effect models at the top of

Tables 1 and 2). Intention to share the pro-attitudinal article was associated with more online participation intentions and offline participation intentions for the Ukraine referendum and refugee topics (Table 1, columns 1, 2, 4 and 5). Sharing pro-attitudinal articles about equal pay was positively associated with offline participation (Table 1, column 6). For the refugee and pay equality topics, pro-attitudinal sharing was positively linked to polarization, and attitude- inconsistent sharing was linked to depolarization (Table 2, columns 2 and 3).

Table 2. Effects of Selective Exposure and Sharing on Opinion Polarization

UA Ref Refugees Equal Pay β β β Model 1: Main effects ΔR2 = .025 ΔR2 = .121 ΔR2 = .125 Pro-Att Exposure -.008 .067 -.012 Counter-Att Exposure -.119 .010 .025 Pro-Att Sharing .074 .128* .279*** Counter-Att Sharing -.116 -.328*** -.378*** Value Involvement .023 -.008 -.077 Impression Involvement -.022 .109 -.117* Model 2: Value Involvement ΔR2 = .036 ΔR2 = .014 ΔR2 = .021 Interactions Value*Pro Exposure -.507 .002 -.179 Value*Counter Exposure .196 .270 -.185 Value*Pro Sharing .149 .202 .190 Value*Counter Sharing -1.093* -.407 -.556* Model 3: Impression ΔR2 = .015 ΔR2 = .010 ΔR2 = .009 Involvement Interactions Impress*Pro Exposure .375 -.160 .130 Impress*Counter Exposure -.254 .330 -.290 Impress*Pro Sharing -.371 -.269 .504 Impress*Counter Sharing .653 -.113 -.379 N = 229 310 333 Note. Standardized coefficients. UA Ref = Ukraine-EU association referendum. *p < .05, **p < .01, ***p < .001. Model 2 and Model 3 each build on the main effects in Model 1; Model 2 tested all value-involvement interactions simultaneously, and Model 3 tested all impression-involvement interactions simultaneously. EXPOSURE AND SHARING EFFECTS 26

From these main effect findings outlined above, H5a-c were largely supported: the sharing of pro-attitudinal social media news stories was positively associated with offline and online political participation, as well as opinion polarization. By contrast, H2a-c, which predicted positive effects of pro-attitudinal information exposure on political participation and polarization, were not supported at all. These discrepancies in findings answer RQ2, which questioned whether sharing of pro-attitudinal social media news stories will be a more consistently positive predictor of political participation and polarization than exposure to pro- attitudinal social media news stories, which was indeed the case. H3a-c, which specified relationships between the amount of time spent reading counter-attitudinal news stories and both political participation and polarization, proved untenable. However, H6c, which hypothesized that sharing counter-attitudinal social media news stories will be negatively associated with opinion polarization was supported for the equal pay and refugee issues. Based on this finding,

RQ3c was answered, such that the sharing of counter-attitudinal news stories was a stronger predictor of issue depolarization than exposure to counter-attitudinal news stories (except in the case of the Ukraine issue).

In addition, sharing counter-attitudinal articles about pay equality was positively linked to online participation (Table 1, column 3). Contrary to H6a, this finding provides some evidence that counter-attitudinal information sharing was positively rather than negatively associated with online political participation. This result also addresses RQ3a: Counter-attitudinal sharing was not a strong negative predictor (in the case of pay equality was even a positive predictor) relative to counter-attitudinal exposure. Furthermore, there was no association between counter- attitudinal sharing and offline political participation across all three issues, lending no support to

H6b. This also addresses RQ3b regarding the relative contribution of exposure and sharing. EXPOSURE AND SHARING EFFECTS 27

An additional regression model (not in table) tested whether exposure to and sharing of

Ukraine articles were associated with the intention to vote in the referendum (an outcome specific to this topic). Only pro-attitudinal sharing was a (positive) predictor, β = .174, p = .035.

4.3. Within-Subjects interaction effects

Within-subjects ANCOVA with value and impression involvement as covariates identified moderating effects of value involvement on selective (pro- vs. counter-attitudinal)

2 sharing for stories on the referendum, F(1, 229) = 7.74, p = .006, hp = .033, refugees, F(1, 308)

2 2 = 12.33, p < .001, hp = .038, and equal pay, F(1, 334) = 3.91, p = .049, hp = .012. Probing these interactions with Johnson-Neyman technique via the MEMORE macro (Montoya & Hayes,

2017) found that moderate and high levels of value involvement (≥ 3.16 for Ukraine, ≥ 2.25 for refugees, and ≥ 3.16 for pay equality, where M = 3.88) were linked to more pro-attitudinal over counter-attitudinal message sharing, whereas those low on value involvement show no difference in sharing pro-attitudinal versus counter-attitudinal articles. H7b was supported. Value involvement fell short of moderating selective exposure to referendum (p = .074), refugee (p =

.080), and equal pay (p = .169) articles. H7a was not supported.

Impression involvement did not moderate selective sharing, (ps > .25), lending no support to H12b. With regard to H12a, neither did impression involvement moderate the bias in pro-attitudinal versus counter-attitudinal exposure (ps > .35).

4.4. Between-Subjects interaction effects

Then, regression models were extended to test value involvement and impression involvement as moderators. One set of models tested value involvement as a simultaneous moderator of all focal variables (pro-attitudinal exposure, counter-attitudinal exposure, pro- attitudinal sharing, and counter-attitudinal sharing) on the DVs of interest (Model 2 in Tables 1 EXPOSURE AND SHARING EFFECTS 28 and 2). Alternatively, impression involvement was tested as a simultaneous moderator of focal variables in Model 3 (bottom of Tables 1 and 2). A number of significant interactions emerged, as indicated in the Tables. The PROCESS macro (Hayes, 2018) was used to probe and interpret these interactions with the Johnson-Neyman method.

There was partial support for H8a-b which hypothesized that pro-attitudinal information exposure will have positive effects on offline and online participation at high levels of value involvement. People high on value involvement (the 13.79% at or above a score of 5.306) had a positive effect of Ukraine pro-attitudinal exposure on online participation. Also, people high on value involvement (the 14.54% at or above a score of 5.012) had a positive effect of equal pay pro-attitudinal exposure on offline participation. Although not hypothesized, at low levels of value involvement (the 1.72% at or below a score of 1.824) there was a negative effect of

Ukraine pro-attitudinal exposure on online participation. Similarly, people low on value involvement (the 7.42% at or below a score of 2.003) had a negative effect of equal pay pro- attitudinal exposure on offline participation.

People high on value involvement (the 39.57% at or above a score of 4.058) had a negative effect of Ukraine counter-attitudinal sharing on their polarization. Similarly, people with moderate to high on value involvement (the 89.52% at or above a score of 2.486) had a negative effect of counter-attitudinal sharing on their polarization for the issue of equal pay, where higher involvement led to stronger depolarization effects. In all, these findings run counter to H11c, which hypothesized that the negative effect of counter-attitudinal information sharing on opinion polarization is weakest among individuals with high levels of value involvement.

With regard to H14b, the relationship between counter-attitudinal information exposure on offline participation depended on impression involvement. However, the effect was in an EXPOSURE AND SHARING EFFECTS 29 unexpected direction. Specifically, people high on impression involvement (the 21.12% at or above a score of 4.374) had a positive effect of Ukraine counter-attitudinal exposure on offline participation. Next, people low on impression involvement (the 6.90% at or below a score of

2.062) had a negative effect of Ukraine counter-attitudinal sharing on online participation, while people high on impression involvement (the 21.12% at or above a score of 4.328) had a positive effect of Ukraine counter-attitudinal sharing on online participation. This finding ran counter to

H16a, which hypothesized that the negative effects of counter-attitudinal information sharing on online political participation will be stronger among people high on impression involvement.

In addition, value and impression involvement did not moderate any effects of exposure or sharing on voting intentions in the Ukraine-EU referendum.

None of the other moderation effect hypotheses or research questions were supported with statistically significant results. With regard to H8c, H9c, and H10c, which hypothesized that the respective effects of information exposure and pro-attitudinal information sharing on opinion polarization would depend on value involvement, there was no support. Furthermore, regarding

H13c, H14c, H15c, and H16c, impression involvement did not moderate the effects of either information sharing or exposure on opinion polarization.

Also, the effects of pro-attitudinal information sharing on the two types of political participation did not depend on value involvement. Additionally, moderation analyses showed that the effects of counter-attitudinal exposure and sharing on both types of political participation did not depend on value involvement. H9a-b, H10a-b, and H11a-b received no support. With respect to H13a-b, the relationship between pro-attitudinal information exposure and both types of political participation also did not depend on impression involvement. The relationship between counter-attitudinal information exposure and online political participation also did not EXPOSURE AND SHARING EFFECTS 30 depend on impression involvement (H14a). The effects of pro-attitudinal information sharing on both types of political participation did not depend on impression involvement, lending no support to H15a-b. Finally, the effects of counter-attitudinal information sharing on offline political participation did not depend on impression involvement, lending no support to H16b.

In short, H1 was supported (partial, by topic), H4 was supported, H5 was supported, H6c was supported (partial, by topic), H7b was supported, and H8a-b was supported (partial, by topic). Participants had confirmation bias in their exposure and sharing. Sharing stories affected polarization; pro-attitudinal sharing also related to participation. Value involvement heightened pro-attitudinal sharing as well as the influence of pro-attitudinal exposure on participation.

5. Discussion

In sum, this study’s findings are multi-faceted and complex. In terms of main effects, results provide partial support for the hypothesis that people will spend more time reading pro- attitudinal than counter-attitudinal social media news articles. The presence of a confirmation bias was contingent on the issue: the refugee issue was the only topic to produce more pro- attitudinal than counter-attitudinal exposure, and it also produced greater selective sharing than the other two topics. The topic of refugees was the most polarized (at baseline) of the issues examined in this study, which likely contributes to this difference with the other two issues.

Furthermore, pro- and counter-attitudinal information exposure did not have any main effects on opinion polarization and political participation.

In contrast, findings showed support for the hypothesis that people will be more likely to share pro-attitudinal over counter-attitudinal news articles on social media. As hypothesized, pro-attitudinal information sharing was positively associated with online and offline political participation for issues pertaining to the Ukrainian referendum and refugees. However, there was EXPOSURE AND SHARING EFFECTS 31 a positive association between counter-attitudinal information sharing on online participation for the issue of equal pay. Consistent with predictions, the sharing of pro-attitudinal news articles about refugees and equal pay had positive effects on opinion polarization. Also, the sharing of counter-attitudinal news articles about refugees and equal pay were negatively associated with opinion polarization.

Taken together, the mostly significant positive associations between pro-attitudinal information sharing and political participation across issue types provide fairly strong support for the argument that pro-attitudinal information sharing is a much stronger sign of one’s commitment to their political views than pro-attitudinal partisan-based information exposure (cf.

Shin & Thorson, 2017). When people overtly express their identification with specific political values or opinions in the form of pro-attitudinal information sharing, such information sharing is likely to translate into political action. However, it is important to note that the link between selectivity and participation is essentially cross-sectional, as participation was only measured post-test. In contrast, the effects on attitudinal polarization may be characterized as causal, as they control for baseline attitudes. The effects of sharing on polarization are absent for the

Ukraine referendum, which was the least salient and least contentious issue of the three. Given the confusion or apathy among the Dutch electorate regarding the vote (cf. Van der Brug, van der

Meer, & van der Pas, 2018), sharing information about it on social media would involve less of an identity-defining statement or catalyst for change.

With regard to interaction effects, there was some modest evidence that the positive association between pro-attitudinal information exposure and political participation depends on value involvement. Specifically, a positive effect of pro-attitudinal information exposure on both online and offline political participation tended to be strongest among those with high levels of EXPOSURE AND SHARING EFFECTS 32 value involvement for the issues of Ukraine referendum and equal pay. This set of findings provides support for the assumption that people tend to be more politically defensive at high levels of value involvement. When people are politically closed-minded, they are likely to engage in pro-attitudinal information consumption behaviors that translate into participatory action. However, these findings only provide one part of the overall picture: Although not hypothesized, there were also negative associations between pro-attitudinal news exposure and political participation when value involvement levels were low for the issues of the Ukraine referendum and equal pay. Perhaps, at low levels of value involvement, people are more amenable to allowing opposing political viewpoints to influence their political decisions. And when people are willing to consider alternative political views, they could experience attitude ambivalence and develop weakened political attitudes that result in lower levels of political participation. Alternatively, low-involvement individuals who read pro-attitudinal content may believe that an active majority agrees with them and that their own political participation is not needed because others will participate in their place.

In addition, the effects of counter-attitudinal news sharing on opinion polarization depended on value involvement, for the issues of Ukrainian referendum and equal pay. However, these findings ran counter to the study’s hypotheses. Specifically, the negative influence of counter-attitudinal news sharing on opinion polarization was strongest when value involvement in the issue of the Ukrainian referendum was high. Likewise, the negative effect of counter- attitudinal news sharing on opinion polarization regarding equal pay was strongest when value involvement was high. This finding is surprising because people tend to be closed to alternate viewpoints at high levels of value involvement. It would appear, however, that those high on value involvement were especially prone to persuasion (i.e., depolarization) if they engaged EXPOSURE AND SHARING EFFECTS 33 with—and shared—news they disagreed with.

Generally speaking, value-relevant involvement was a more consistent moderator of the relationships that information exposure and sharing have with political participation and polarization than is impression-driven involvement. Nevertheless, there was a negative association between counter-attitudinal news sharing on online participation among those with low levels of impression involvement on the issue of the Ukrainian referendum. This finding provides qualified evidence for the idea that people with low levels of impression involvement are unlikely to be strongly committed toward publicly performing actions that align with their partisan values. When impression involvement levels are low, people seem alright with explicitly letting their social media followers know that they are considering alternative political stances.

Also, there was a positive association between counter-attitudinal news sharing and online participation among those with high levels of impression involvement on the referendum issue.

Perhaps, people with high levels of impression involvement openly share counter-attitudinal news with the goal of openly deriding such information. Such denigration of counter-attitudinal news in front of an imagined social media audience allows them to explicitly prove that they are committed to their partisan values, strengthening their resolve to take political action.

5.1. Implications

In terms of theoretical contributions, this study builds upon existing research on political information consumption and self-expression patterns on social media in the following ways.

First, this study parses out the respective effects of selective exposure and selective sharing on political outcomes. We demonstrate that people are much more likely to engage in selective sharing than selective exposure on social media and that selective sharing on social media is a much more robust predictor of political outcomes than selective exposure. This underscores the EXPOSURE AND SHARING EFFECTS 34 importance of delineating between exposure and sharing when examining how selectivity in information consumption and political self-expression influence political outcomes. Theories of selective exposure such as the SESAM (Knobloch-Westerwick, 2015) would therefore benefit from distinguishing between exposure, sharing, and other modes of content consumption and engagement. Second, scholars have stressed that patterns of selectivity in information consumption and self-expression on social media can have serious political consequences (Beam et al., 2018; Lane et al., 2019). This study is thus one of few that examines the effects of both selective exposure and sharing on different types of political outcomes, specifically participation and opinion polarization. Our consistent result that sharing is more consequential than exposure is consistent with theories of public commitment such as identity shift (Lane et al., 2019). People might harbor polarized issue attitudes internally, but such attitudes are not synonymous with explicit political action (e.g., Miller & Conover, 2015). For instance, our study shows that pro- attitudinal information sharing was positively associated with political participation whereas counter-attitudinal sharing was negatively associated with polarized issue attitudes. This pattern of findings clearly illustrates that political participation and opinion polarization are conceptually distinct outcomes. More theoretical development is needed to articulate different processes for different outcomes in online political communication.

Third, it seems intuitively obvious that highly involved individuals will have very selective political information consumption and expressive behaviors (Cho & Boster, 2005). Yet few studies on political social media use have actually tested involvement as a moderator of selectivity and its effects. Our study addressed this research gap by examining whether effects of selective sharing and exposure on political outcomes depended on involvement levels. Further theoretical refinement could articulate conditions under which involvement is consequential, and EXPOSURE AND SHARING EFFECTS 35 future research should include accuracy involvement (Hart et al., 2009; Johnson & Eagly, 1989).

Fourth, many experiments examining online-based selective exposure (for reviews see

Clay et al., 2013 and Knobloch-Westerwick, 2015) have been conducted in the context of news websites. However, little experimental work has employed social media contexts (but see Kaiser,

Keller, & Kleinen-von Königslöw, in press; Mothes & Ohme, 2019), even though large majorities of individuals have become reliant on Facebook and other social media for news rather than online news sites (Bakshy et al., 2015; Settle, 2018). As such, this study improves upon previous study designs by using a free choice paradigm to examine partisan selective exposure and sharing in the context of a Facebook news feed.

This study’s findings have practical implications for harnessing social media platforms for political engagement. Given that this study’s findings largely show that pro-attitudinal news sharing is positively associated with political participation, campaign practitioners and politicians can encourage their voter base to share political messages that favor their in-party through their online social networks. Such selective sharing not only helps to keep other people politically informed, but also makes people themselves more likely to take tangible political action. Furthermore, messages tailored to match peoples’ partisan leanings and levels of value involvement can be disseminated through online social networks to foster political engagement.

Based on this study’s findings, campaign planners can identify voters from their own camp with high levels of value involvement on social media and increase political participation levels among these individuals by encouraging them to share pro-attitudinal news stories. Furthermore, research has shown that Facebook news use can lead to depolarization (Beam et al., 2018).

Similarly, our study’s findings show that sharing counter-attitudinal political information in a

Facebook context leads to less polarized issue attitudes, regardless of value involvement levels. EXPOSURE AND SHARING EFFECTS 36

With this in mind, social media platforms can devise strategies to promote counter-attitudinal information sharing, e.g., by designing advertisements to encourage people from all types of value involvement levels to like, share, or comment on counter-attitudinal (or perhaps balanced, cf. Brenes Peralta et al., 2017) news. Ultimately, such counter-attitudinal news sharing on

Facebook is likely to be beneficial to democracy by causing even people with higher levels of value involvement to hold less extreme attitudes and embrace alternative issue viewpoints.

5.2. Limitations and future research

In terms of study limitations, this study did not measure political participation levels prior to stimuli exposure. This prevents us from making causal claims about how selective exposure and selective sharing affect political participation. Nevertheless, this study still provides consistent evidence for the association between selective sharing and political participation.

Furthermore, this study operationalized selective sharing by using items gauging the likelihood of commenting on the news article or “liking” the news article. On the one hand, these items can be regarded as generalized indicators of expressive social media political use (e.g., Macafee &

De Simone, 2012). Future research can explore differences in the affordances of social media sharing (e.g., liking vs. sharing). Moreover, measuring actual sharing (i.e., participants’ sharing behavior in their own networks rather than sharing intentions in a questionnaire), perhaps in an unobtrusive manner, can address limitations associated with self-reported intentions. Also, this study did not examine mechanisms related to confirmation bias such as cognitive dissonance or motivated reasoning. Future research examining partisan selectivity on social media should incorporate such mechanisms into their study design, to better explain the links between selectivity and its effects on political outcomes. A further limitation is the focus on three specific topics (although varied in salience and baseline polarization) and variation that might exist in EXPOSURE AND SHARING EFFECTS 37 stimuli (e.g., in argument strength). Future work should continue to include more variation in topics and message design. Nevertheless, this study provides strong evidence of selective information consumption and self-expression behaviors in Netherlands, a country with a multi- party system, and makes a particularly unique contribution by juxtaposing exposure and sharing.

With regard to directions for research, future studies can examine how social media cues such as the number of “likes” or “reactions” moderate the effects of selective exposure and selective sharing on political outcomes. Furthermore, traditional public opinion theories such as the spiral of silence posit that people will assess opinion congruence by paying attention to cues in their social environments. If people perceive their opinions to be in the minority, they will self-censor by lapsing into silence on socio-political issues (Noelle-Neumann, 1974). It is important to examine whether the tenets of such theories receive support in the context of online social environments (Liu et al., 2017). Future studies can thus examine the respective effects of selective exposure and selective sharing on other outcome variables such as political efficacy or specific opinion expressions in social media. In addition, cross-national comparisons can help to validate study findings and examine differences across political systems and cultures. Future research should examine how the present predictions and findings will fare in other countries.

5.3. Conclusion

In sum, this study builds upon existing research on selective exposure and sharing in the following ways: First, this study uses a free-choice experimental design to provide much stronger evidence for the effects of information stance on selective sharing than on selective exposure.

Second, selective sharing is a much more consistent predictor of political outcomes than selective exposure. Third, the effects of selective sharing and exposure on political outcomes depend more on value involvement than impression involvement. Fourth, issues matter. By using EXPOSURE AND SHARING EFFECTS 38 a variety of topics that varied in their preexisting salience and polarization, we introduced variation that may explain the differences seen in confirmation bias and its effects. Future research should even more systematically vary issue characteristics to test how they shape selectivity effects. As social media platforms continue to play an important role in the political process, more research needs to be conducted to understand how individual level predispositions and socio-environmental factors can exacerbate or attenuate the effects of partisan selective exposure and sharing behaviors on key political outcomes.

EXPOSURE AND SHARING EFFECTS 39

References

Adamic, L. A., & Glance, N. (2005). The political blogosphere and the 2004 US election:

Divided they blog. In J. Adibi, M. Grobelnik, D. Mladenic, & P. Pantel (Eds.), LinkKKD

’05: Proceedings of the 3rd International Workshop on Link Discovery (pp. 36-43). New

York, NY, USA: ACM Press. doi:10.1145/1134271.1134277

Alhabash, S., Almutairi, N., Lou, C., & Kim, W. (2019). Pathways to virality:

Psychophysiological responses preceding likes, shares, comments, and status updates on

Facebook. Media Psychology, 22, 196-216. doi:10.1080/15213269.2017.1416296

Barbera, P., Jost, J. T., Nagler, J., Tucker, J. A., & Bonneau, R. (2015). Tweeting from left to

right: Is online political communication more than an echo chamber? Psychological

Science, 26, 1531-1542. doi:10.1177/0956797615594620

Bakshy, E., Messing, S., & Adamic, L. A. (2015). Exposure to ideologically diverse news and

opinion on Facebook. Science, 348, 1130-1132. doi:10.1126/science.aaa1160

Beam, M. A., Hutchens, M. J., & Hmielowski, J. D. (2016). Clicking vs. sharing: The

relationships between online news behaviors and political knowledge. Computers in

Human Behavior, 59, 215-220. doi:10.1016/j.chb.2016.02.013

Beam, M. A., Hutchens, M. J., & Hmielowski, J. D. (2018). Facebook news and (de)polarization:

Reinforcing spirals in the 2016 US election. Information, Communication, & Society, 21,

940-958. doi:10.1080/1369118X.2018.1444783

Bode, L., Vraga, E. K., Borah, P., & Shah, D. V. (2014). A new space for political behavior:

Political social networking and its democratic consequences. Journal of Computer-

Mediated Communication, 19, 414-429. doi:10.1111/jcc4.12048

Bos, L., Kruikemeier, S., & de Vreese, C. (2016). Nation binding: How public service EXPOSURE AND SHARING EFFECTS 40

broadcasting mitigates political selective exposure. PLoS One, 11(5), e0155112.

doi:10.1371/journal.pone.0155112

Boulianne, S. (2015). Social media use and participation: A meta-analysis of current research.

Information, Communication, & Society, 18, 524-538.

doi:10.1080/1369118X.2015.1008542

Brenes Peralta, C., Wojcieszak, M., Lelkes, Y., & de Vreese, C. (2017). Selective exposure to

balanced content and evidence type: The case of issue and non-issue publics about

climate change and health care. Journalism & Mass Communication Quarterly, 94, 833-

861. doi:10.1177/1077699016654681

Brown, W. J., & Basil, M. D. (1995). Media celebrities and public health: Responses to “Magic”

Johnson’s HIV disclosure and its impact on AIDS risk and high risk behaviors. Health

Communication, 7, 345-370. doi:10.1207/s15327027hc0704_4

Cho, H., & Boster, F. J. (2005). Development and validation of value-, outcome-, and

impression-relevant involvement scales. Communication Research, 32, 235-264.

doi:10.1177/0093650204273764

Choi, J., & Lee, J. K. (2015). Investigating the effects of news sharing and political interest on

social media network heterogeneity. Computers in Human Behavior, 44, 258-266.

doi:10.1016/j.chb.2014.11.029

Clay, R., Barber, J. M., & Shook, N. J. (2013). Techniques for measuring selective exposure: A

critical review. Communication Methods and Measures, 7, 147-171.

doi:10.1080/19312458.2013.813925

Colleoni, E., Rozza, A., & Arvidsson, A. (2014). Echo chamber or public sphere? Predicting

political orientation and measuring political homophily in Twitter using Big Data. EXPOSURE AND SHARING EFFECTS 41

Journal of Communication, 64, 317-332. doi:10.1111/jcom.12084

Coppini, D., Duncan, M. A., McLeod, D. M., Wise, D. A., Bialik, K. E., & Wu, Y. (2017). When

the whole world is watching: A motivations-based account of selective expression and

exposure. Computers in Human Behavior, 75, 766-774. doi:10.1016/j.chb.2017.04.020

Fazio, R. H., Zanna, M. P., & Cooper, J. (1977). Dissonance and self-perception: An integrative

view of each theory's proper domain of application. Journal of Experimental Social

Psychology, 13, 464-479. doi:10.1016/0022-1031(77)90031-2

Feezell, J. T. (2016). Predicting online political participation: The importance of selection bias

and selective exposure in the online setting. Political Research Quarterly, 69, 495-509.

doi:10.1177/1065912916652503

Festinger, L. (1957). A theory of cognitive dissonance. Stanford, CA, USA: Stanford University

Press.

Frey, D. (1986). Recent research on selective exposure to information. Advances in Experimental

Social Psychology, 19, 41-80. doi:10.1016/S0065-2601(08)60212-9

Garrett, R. K. (2009a). Echo chambers online?: Politically motivated selective exposure among

Internet news users. Journal of Computer-Mediated Communication, 14, 265-285.

doi:10.1111/j.1083-6101.2009.01440.x

Garrett, R. K. (2009b). Politically motivated reinforcement seeking: Reframing the selective

exposure debate. Journal of Communication, 59, 676-699. doi:10.1111/j.1460-

2466.2009.01452.x

Garrett, R. K. (2013). Selective exposure: New methods and new directions. Communication

Methods and Measures, 7, 247-256. doi:10.1080/19312458.2013.835796

Garrett, R. K., Carnahan, D., & Lynch, E. K. (2013). A turn toward avoidance? Selective EXPOSURE AND SHARING EFFECTS 42

exposure to online political information, 2004-2008. Political Behavior, 35, 113-134.

doi:10.1007/s11109-011-9185-6

Garrett, R. K., Dvir Gvirsman, S., Johnson, B. K., Tsfati, Y., Neo, R., & Dal, A. (2014).

Implications of pro- and counter-attitudinal information exposure for affective

polarization. Human Communication Research, 40, 309-332. doi:10.1111/hcre.12028

Gil de Zúñiga, H., Molyneux, L., & Zheng, P. (2014). Social media, political expression, and

political participation: Panel analyses of lagged and concurrent relationships. Journal of

Communication, 64, 612-634. doi:10.1111/jcom.12103

Hameleers, M., Bos, L., & de Vreese, C. H. (2018). Selective exposure to populist

communication: How attitudinal congruence drives the effects of populist attributions of

blame. Journal of Communication, 68, 51-74. doi:10.1093/joc/jqx001

Hart, W., Albarracín, D., Eagly, A. H., Brechan, I., Lindberg, M. J., & Merrill, L. (2009). Feeling

validated versus being correct: A meta-analysis of selective exposure to information.

Psychological Bulletin, 135, 555-588. doi:10.1037/a0015701

Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A

regression-based approach (2nd ed.). New York, NY, USA: Guilford Press.

Hogg, M. A. (2014). From uncertainty to extremism: Social categorization and identity

processes. Current Directions in Psychological Science, 23, 338-342.

doi:10.1177/0963721414540168

Hwang, H., Kim, Y., & Huh, C. U. (2014). Seeing is believing: Effects of uncivil online debate

on political participation and expectations of deliberation. Journal of Broadcasting &

Electronic Media, 58, 621-633. doi:10.1080/08838151.2014.966365

Jeong, M., Zo, H., Lee, C. H., & Ceran, Y. (2019). Feeling displeasure from online social media EXPOSURE AND SHARING EFFECTS 43

postings: A study using cognitive dissonance theory. Computers in Human Behavior, 97,

231-240. doi:10.1016/j.chb.2019.02.021

Kaiser, J., Keller, T. R., & Kleinen-von Königslöw, K. (in press). Incidental news exposure on

Facebook as a social experience: The influence of recommender and media cues on news

selection. Communication Research. doi:10.1177/0093650218803529

Kim, Y. (2015). Does disagreement mitigate polarization? How selective exposure and

disagreement affect political polarization. Journalism & Mass Communication Quarterly,

92, 915-937. doi:10.1177/1077699015596328

Kim, Y., & Chen, H.-T. (2016). Social media and online political participation: The mediating

role of exposure to cross-cutting and like-minded perspectives. Telematics and

Informatics, 33, 320-330. doi:10.1016/j.tele.2015.08.008

Kim, Y. M. (2009). Issue publics in the new information environment: Selectivity, domain

specificity, and extremity. Communication Research, 36, 254-284.

doi:10.1177/0093650208330253

Knobloch-Westerwick, S. (2015). Choice and preference in media use: Advances in selective

exposure theory and research. New York, NY, USA: Routledge.

Knobloch-Westerwick, S., & Kleinman, S. B. (2012). Preelection selective exposure:

Confirmation bias versus informational utility. Communication Research, 39, 170-193.

doi:10.1177/0093650211400597

Kruikemeier, S., van Noort, G., Vliegenthart, R., & de Vreese, C. H. (2014). Unraveling the

effects of active and passive forms of political Internet use: Does it affect citizens’

political involvement?. New Media & Society, 16, 903-920.

doi:10.1177/1461444813495163 EXPOSURE AND SHARING EFFECTS 44

Kümpel, A. S., Karnowski, V., & Keyling, T. (2015). News sharing in social media: A review of

current research on news sharing users, content, and networks. Social Media + Society,

1(2), article 23. doi:10.1177/2056305115610141

Kwak, N. (1999). Revisiting the knowledge gap hypothesis: Education, motivation, and media

use. Communication Research, 26, 385-413. doi:10.1177/009365099026004002

Kwak, N., Williams, A. E., Wang, X., & Lee, H. (2005). Talking politics and engaging politics:

An examination of the interactive relationships between structural features of political

talk and discussion engagement. Communication Research, 32, 87-111.

doi:10.1177/0093650204271400

Hastall, M. R., & Knobloch-Westerwick, S. (2013). Caught in the act: Measuring selective

exposure to experimental online stimuli. Communication Methods and Measures, 7, 94-

105. doi:10.1080/19312458.2012.761190

Huddy, L., Mason, L., & Aarøe, L. (2015). Expressive partisanship: Campaign involvement,

political emotion, and partisan identity. American Political Science Review, 109, 1-17.

doi:10.1017/S0003055414000604

Johnson, B. T., & Eagly, A. H. (1989). Effects of involvement on persuasion: A meta-analysis.

Psychological Bulletin, 106, 290-314. doi:10.1037/0033-2909.106.2.290

Knobloch-Westerwick, S., & Johnson, B. K. (2014). Selective exposure for better or worse: Its

mediating role for online news’ impact on political participation. Journal of Computer-

Mediated Communication, 19, 184-196. doi:10.1111/jcc4.12036

Lane, D. S., Lee, S. S., Liang, F., Kim, D. H., Shen, L., Weeks, B. E., & Kwak, N. (2019). Social

media expression and the political self. Journal of Communication, 69, 49-72.

doi:10.1093/joc/jqy064 EXPOSURE AND SHARING EFFECTS 45

Lee, C., Shin, & Hong, A. (2018). Does social media use really make people political polarized?

Direct and indirect effects of social media use on political polarization in South Korea.

Telematics and Informatics, 35, 245-254. doi:10.1016/j.tele.2017.11.005

Lee, F. L. F., Chen, H.-T., & Chan, M. (2017). Social media use and university students’

participation in a large-scale protest campaign: The case of Hong Kong’s Umbrella

Movement. Telematics and Informatics, 34, 457-469. doi:10.1016/j.tele.2016.08.005

Lee, J. K., Choi, J., Kim, C., & Kim, Y. (2014). Social media, network heterogeneity, and

opinion polarization. Journal of Communication, 4, 702-722. doi:10.1111/jcom.12077

Lee, S. K., Lindsey, N. J., & Kim, K. S. (2017). The effects of news consumption via social

media and news information overload on perceptions of journalistic norms and practices.

Computers in Human Behavior, 75, 254-263. doi:10.1016/j.chb.2017.05.007

Leippe, M. R., & Elkin, R. A. (1987). When motives clash: Issue involvement and response

involvement as determinants of persuasion. Journal of Personality and Social

Psychology, 52, 269-278. doi:10.1037/0022-3514.52.2.269

Liao, Q. V., & Fu, W.-T. (2013). Beyond the filter bubble: Interactive effects of perceived threat

and topic involvement on selective exposure to information. In W. E. Mackay, S.

Brewster, & S. Bødker (Eds.), CHI ’13: Proceedings of the SIGCHI conference on

human factors in computing systems (pp. 2359-2368). New York, NY, USA: ACM.

doi:10.1145/2470654.2481326

Liu, Y., Rui, J. R., & Cui, X. (2017). Are people willing to share their political opinions on

Facebook? Exploring roles of self-presentational concern in spiral of silence. Computers

in Human Behavior, 76, 294-302. doi:10.1016/j.chb.2017.07.029

Lord, C. G., Ross, L., & Lepper, M. R. (1979). Biased assimilation and attitude polarization: The EXPOSURE AND SHARING EFFECTS 46

effects of prior theories on subsequently considered evidence. Journal of Personality and

Social Psychology, 37, 2098-2109. doi:10.1037/0022-3514.37.11.2098

Lu, Y., Heatherly, K. A., & Lee, J. K. (2016). Cross-cutting exposure on social networking sites:

The effects of SNS discussion disagreement on political participation. Computers in

Human Behavior, 59, 74-81. doi:10.1016/j.chb.2016.01.030

Macafee, T., & De Simone, J. J. (2012). Killing the bill online? Pathways to young people's

protest engagement via social media. Cyberpsychology, Behavior, and Social

Networking, 15, 579-584. doi:10.1089/cyber.2012.0153

Matthes, J. (2012). Exposure to counterattitudinal news coverage and the timing of voting

decisions. Communication Research, 39, 147-169. doi:10.1177/0093650211402322

Miller, P. R., & Conover, P. J. (2015). Red and blue states of mind: Partisan hostility and voting

the United States. Political Research Quarterly, 68, 225-239.

doi:10.1177/1065912915577208

Montoya, A. K., & Hayes, A. F. (2017). Two condition within-participant statistical mediation

analysis: A path-analytic framework. Psychological Methods, 22, 6-27.

doi:10.1037/met0000086

Mothes, C., & Ohme, J. (2019). Partisan selective exposure in times of political and

technological upheaval: A social media field experiment. Media and Communication, 7,

42-53. doi:10.17645/mac.v7i3.2183

Mutz, D. C. (2002). The consequences of cross-cutting networks for political participation.

American Journal of Political Science, 46, 838-855. doi:10.2307/3088437

Noelle-Neumann, E. (1974). The spiral of silence: A theory of public opinion. Journal of

Communication, 24, 43-51. doi:10.1111/j.1460-2466.1974.tb00367.x EXPOSURE AND SHARING EFFECTS 47

Oeldorf-Hirsch, A., & Sundar, S. S. (2015). Posting, commenting, and tagging: Effects of

sharing news stories on Facebook. Computers in Human Behavior, 44, 240-249.

doi:10.1016/j.chb.2014.11.024

Pellikaan, H., de Lange, S. L., & van der Meer, T. W. G. (2018). The centre does not hold:

Coalition politics and party system change in the Netherlands, 2002-2012. Government

and Opposition, 53, 231-255. doi:10.1017/gov.2016.20

Perloff, R. M. (1989). Ego-involvement and the third person effect of televised news

coverage. Communication Research, 16, 236-262. doi:10.1177/009365089016002004

Settle, J. E. (2018). Frenemies: How social media polarizes America. Cambridge, UK:

Cambridge University Press.

Shah, D. V., Cho, J., Nah, S., Gotlieb, M. R., Hwang, H., Lee, N. J., Scholl, R. M., & McLeod,

D. M. (2007). Campaign ads, online messaging, and participation: Extending the

communication mediation model. Journal of Communication, 57, 676-703.

doi:10.1111/j.1460-2466.2007.00363.x

Sherif, C. W., Sherif, M., & Nebergall, R. E. (1965). Attitude and attitude change: The social

judgment-involvement approach. Philadelphia, PA, USA: W. B. Saunders.

Shin, J., & Thorson, K. (2017). Partisan selective sharing: The biased diffusion of fact-checking

messages on social media. Journal of Communication, 67, 233-255.

doi:10.1111/jcom.12284

Slater, M. D., & Rouner, D. L. (1992). Confidence in beliefs about social groups as an outcome

of message exposure and its role in belief change persistence. Communication Research,

19, 597-617. doi:10.1177/009365092019005003

Slater, M. D., & Rouner, D. L. (1996). Value-affirmative and value-protective processing of EXPOSURE AND SHARING EFFECTS 48

alcohol education messages that include statistical or anecdotal evidence. Communication

Research, 23, 210-235 doi:10.1177/009365096023002003

Smith, S. M., Fabrigar, L. R., Powell, D. M., & Estrada, M.-J. (2007). The role of information-

processing capacity on goals in attitude-congruent selective exposure effects. Personality

and Social Psychology Bulletin, 33, 948-960. doi:10.1177/0146167207301012

Stroud, N. J. (2008). Media use and political predispositions: Revisiting the concept of selective

exposure. Political Behavior, 30, 341-366. doi:10.1007/s11109-007-9050-9

Stroud, N. J. (2010). Polarization and partisan selective exposure. Journal of Communication,

60, 556-576. doi:10.1111/j.1460-2466.2010.01497.x

Trilling, D., van Klingeren, M., & Tsfati, Y. (2016). Selective exposure, political polarization,

and possible mediators: Evidence from the Netherlands. International Journal of Public

Opinion Research, 29, 189-213. doi:10.1093/ijpor/edw003

Vaccari, C., Valeriani, A., Barberá, P., Bonneau, R., Jost, J. T., Nagler, J., . . . Tucker, J. A.

(2015). Political expression and action on social media: Exploring the relationship

between lower- and higher-threshold political activities among twitter users in Italy.

Journal of Computer-Mediated Communication, 20, 221-239. doi:10.1111/jcc4.12108

Valenzuela, S. (2013). Unpacking the use of social media for protest behavior: The roles of

information, opinion expression, and activism. American Behavioral Scientist, 57, 920-

942. doi:10.1177/0002764213479375

Van der Brug, W., van der Meer, T., & van der Pas, D. (2018). Voting in the Dutch “Ukraine-

referendum”: A panel study on the dynamics of party preference, EU-attitudes, and

referendum-specific considerations. Acta Politica, 53, 496-516. doi:10.1057/s41269-018-

0107-z EXPOSURE AND SHARING EFFECTS 49

Weeks, B. E., Lane, D. S., Kim, D. H., Lee, S. S., & Kwak, N. (2017). Incidental exposure,

selective exposure, and political information sharing: Integrating online exposure patterns

and expression on social media. Journal of Computer-Mediated Communication, 22, 363-

379. doi:10.1111/jcc4.12199

Westerwick, A., Johnson, B. K., & Knobloch-Westerwick, S. (2017). Confirmation biases in

selective exposure to political online information: Source bias versus content bias.

Communication Monographs, 84, 343-364. doi:10.1080/03637751.2016.1272761

Wheeless, L. R. (1974). The effects of attitude, credibility, and homophily on selective exposure

to information. Speech Monographs, 41, 329-338. doi:10.1080/03637757409375857

Wojcieszak, M., Bimber, B., Feldman, L., & Stroud, N. J. (2016). Partisan news and political

participation: Exploring mediated relationships. Political Communication, 33, 241-260.

doi:10.1080/10584609.2015.1051608