When Partisanship Is Too Risky: Understanding the Expression of Political Identity

When Partisanship is Too Risky: Understanding the Expression of Political Identity

Presented in Partial Fulfillment of the Requirements for Master of Arts in the Graduate School of The Ohio State University

By Jaqualynn M. Anderson Graduate Program in Communication

The Ohio State University

Committee: Robert Bond, Advisor William “Chip” Eveland, Committee Member

Copyrighted by Jaqualynn M Anderson

Abstract In the media, partisans are often portrayed in constant conflict and detrimental to the democratic process in the United States. If this is the case, partisans may engage in techniques to disguise their partisan affiliation, especially online. This study examines partisan identity expression through social identity theory, impression management, and willingness to self- censor. Utilizing two different samples, participants answer survey questions, read an article, then create an online profile for an imaginary discussion site. Results demonstrate that even if partisans are portrayed negatively, they will continue to identify with their party on their discussion site profile, counter to the hypotheses presented.

Acknowledgements I want to thank the professors who have encouraged me to further my education and offer vital support along the way, both at the graduate and undergraduate levels. Specifically, I would like to express my sincere gratitude to Dr. Zheya Gai and Dr. Melissa Cook from Washington &

Jefferson College for noticing my potential to participate in conferences and further my education outside of the classroom. Another thanks to my cohort at Ohio State. Thank you for providing a space where we all could come together and discuss our accomplishments and failures.

I also want to thank my family and friends for all their love and support during my academic journey. Specifically, I thank Brandon Durbin for reading over my drafts and allowing me to bounce ideas off of you. I know this thesis was very far outside of your field, but I deeply appreciate your interest and concern for my project.

Vita

Education

B.A. in Communication Arts Washington & Jefferson College 2014 – 2018

Capstone Project: “Polarization and News Biases”

B.A. in Political Science Washington & Jefferson College 2014 – 2018

Capstone Project/Independent Study: “A Comparative Analysis of Campaign Finance

Systems in the United States. United Kingdom, and Germany”

Relevant Work Experience

Graduate Teaching Assistant Ohio State University Aug. 2019 – May 2020

Assoc. Marketing Director Office of International Programs (W&J) Dec. 2016 – May 2018

Marketing Intern Stage Right! – Greensburg, PA May 2017 – Aug 2017

Student Faculty Secretary Academic Affairs (W&J) Sep. 2014 – May 2017

Fields of Study

Major Field: Communication

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Table of Contents Abstract ...... i Acknowledgements ...... ii Vita ...... iii List of Tables ...... v Introduction ...... 1 Political Identification ...... 4 Social Identity Theory ...... 4 Partisan Social Identity ...... 8 Managing a Partisan Identity ...... 12 Self-Monitoring Identity ...... 16 Avoiding Identity ...... 18 Spiral of Silence ...... 18 Criticisms of the Spiral of Silence ...... 22 Willingness to Self-Censor (WTSC) ...... 26 Methodology ...... 29 Results ...... 46 Discussion ...... 54 General Discussion ...... 62 References ...... 68 Appendices ...... 75 Appendix A: Pre-Treatment Survey ...... 75 Appendix B: Treatment Articles ...... 78 Appendix C: Profile Creation...... 79 Appendix D: Selecting Discussion Partners ...... 82

List of Tables

Table 1: Demographics of Each Sample ...... 43

Table 2: Hypotheses 1 & 2 – Welch’s T-Test ...... 47

Table 3: Hypothesis 3 – Correlations between Self-Monitoring and Independent Options ...... 48

Table 4: Regression Model – Icon Change (DV), Article Condition & Self-Monitoring (H3a) . 50

Table 5: Regression Model – Label Change (DV), Article Condition & Self-Monitoring (H3b) 50

Table 6: Hypothesis 4 – Correlations between WTSC and Independent Options ...... 52

Table 7: Regression Model – Icon Change (DV), Article Condition & WTSC (H4a) ...... 53

Table 8: Regression Model – Label Change (DV), Article Condition & WTSC (H4b) ...... 53

Table 9: Demographics of Each Sample, Examining Race ...... 58

Table 10: Hypotheses by Sample ...... 63

Introduction

In 2019, 85% of U.S. adults told Pew Research that they believed that political debate has grown more negative and less respectful than in the past, and 78% believe this rhetoric can lead to violence (Drake & Kiley, 2019). With the rise of perceived negativity surrounding political debate, many individuals around the globe are refraining from discussing politics with one another. Parties globally are often portrayed in constant conflict (Geiβ & Schäfer, 2017), leading to possible negative perceptions of their members. Young adults refrain from posting their political opinions online due to a perceived risk of the wrong person seeing it or people perceiving them negatively (Storsul, 2014). Some scholars believe individuals seek to avoid these kinds of social risks associated with their political group membership (Zaller & Feldman,

1992). By identifying with the partisan group or expressing a political opinion, an individual may fear being attributed negative characteristics or possible exclusion from others. Should individuals fail to express their partisanship to others, others might misjudge the opinion climate or refrain from participating in politics generally (Kwon, Moon, & Stefanone, 2015; Storsul,

2014). Due to the increased perception of social risk and expressing a political opinion, will partisans alter their political identity? This study attempts to understand whether partisans may hide behind non-partisan or Independent options when partisanship is evaluated as negative by others.

Membership within a group can lead to the perceptions of the group being attached to the individuals within group. According to social identity theory (SIT), individuals desire to maintain or enhance their perceptions of self (Tajfel & Turner, 1986). Self-perceptions rely on comparisons between one’s group and an out-group which the individual does not belong. If the group is seen as negatively valenced, individuals may attempt to change to a positively valenced

group, compete with positively valenced groups, or redefine their existing group. Identification with a group may vary from private to public as individuals engage in impression management techniques aimed at concealing an inappropriate or negative aspect of oneself (Snyder, 1974). In the age of public social media profiles, individuals may opt to vary their identification with various groups on their profiles, especially a group which carries negative perceptions when expressed publicly. If partisanship is conveyed as a negative social characteristic, they may attempt to distance themselves from a partisan label, selecting options to make them appear more socially acceptable.

Partisans may also censor their partisanship, assessing the opinion climate around them as hostile to this identity. The spiral of silence explains the process through which individuals assess the opinions around them and whether or not they will be isolated from the public by expressing their opinion (Noelle-Neumann, 1974). If their opinion is in the majority, then they will express their opinion; when this is not the case, individuals will stay silent, reluctant to express their opinion due to a fear of isolation from others (Scheufele & Moy, 2000). While this theory asserts that individuals will stay silent, numerous inconsistent studies and a new media environment have questioned the original iteration of the spiral of silence.

New understandings of the spiral of silence acknowledge the pressure individuals may face to express their opinion. When individuals are in situations which they are expected express an opinion, they will engage in self-censoring behaviors (Hayes, 2007). These behaviors can include expressing neutral opinion, ambivalence, or agreement with a group.

Social media allows more people to participate in political discussion than face to face alternatives (Storsul, 2014). The audience on most social media platforms is broad, exposing users to diverse others. The diversity of users on social media can make the social norms and

cues ambiguous (Barnidge, 2017). With ambiguous cues, individuals will attempt to portray their best version of themselves. This can lead others to refrain from posting political opinions online

(Storsul, 2014) or be fearful of expressing their true opinion if they cannot assess if the environment is hostile to their opinion (Chan, 2018). Thus, if self-identified partisans engage in self-monitoring and self-censorship, they may refrain from contributing their true opinions, instead attempting to appear neutral or objective.

In this study, I examine the relationships between partisan identity expression, self- monitoring, and self-censorship. First, I will detail how an identity is formed and managed according to SIT and the impression management literature, followed by a discussion of spiral of silence and the impact these psychological states may have on resulting self-censorship behaviors. Second, I will describe the methodology used in this study. Using an online survey experiment, I will examine differences in partisan identity expression by priming positive or negative evaluations of partisans as a whole. Third, I will present my analysis and results, concluding with a discussion of the implications, limitations, and future directions of this study.

Political Identification

Social Identity Theory

Tajfel and Turner (1986) pioneered social identity theory (SIT) to extend realistic group conflict theory (RCT) by D.T. Campbell. RCT explains the dynamics of group conflict when group interests are not aligned. When perceptions of threat arise from competition with other groups, the group will direct hostility at members of the threatening group (Kinder & Sears,

1981). Following RCT, SIT was developed to explain social conflict and social change. Social identity is derived from the intergroup situations in which group members behave on the basis of their group in relation to other groups, not as individual members (Tajfel & Turner, 1986). This group membership is internalized and an inherent part in their self-concept. Members of the group develop a tendency to favor members of their own group over outside others, called in- group bias. In order to begin feeling in-group bias, individuals must only feel as though they are a member of a group (Nicholls & Rice, 2017). As individuals identify with a group, they will experience emotional connections to the group, triggering in-group bias in evaluating others, and taking on characteristics of their group.

SIT makes three assumptions of an individual’s social behavior. First, an individual will desire to maintain or enhance their perception of self, or self-esteem (Tajfel & Turner, 1986). If the individual no longer possesses a positive perception of self, they will attempt to change it.

The second assumption explains how the perception of self may be influenced by the groups they belong to. Each social group or category possesses a positive or negative valence (Tajfel &

Turner, 1986). Perceptions of an individual’s social identity is reliant on the valence of the groups they belong to. If one’s social identity is attached to a positively valenced group, that individual is likely to possess a positive self-perception. In order to assess the valence of groups,

comparisons must be made. The last assumption states that social comparisons between one’s group and specific other groups will determine the evaluation of one’s group (Tajfel & Turner,

1986). In this way, a hierarchy exists in group evaluations. One group will be seen more positively than another specified group, and thus, belonging to the more highly ranked group will be best for one’s perception of self. Shinnar (2008) examined the social identities of Mexican immigrants working in the United States. They find that these immigrants often see their social identity as negatively valenced by their white, Anglo employers. In order to try and improve their perception of self, they engage in strategies to either appear more desirable (like the dominant group) or make their group more desirable as a whole.

Keeping these assumptions in mind, Tajfel and Turner (1986) outline three theoretical principles parallel to the assumptions. Very similar to the first assumption, an individual will actively attempt to maintain or attain a positively valenced social identity. If their social identity is negative, that individual will strive to correct it. In order to possess a positive social identity, a member’s group must be seen as positively different or distinct from a relevant other group, or out-group. To make these comparisons, one’s social identity must be internalized as a critical aspect of one’s self-concept. If the social identity is not a critical component of one’s self concept, the valence of this group will not extend to the self. For example, if someone supports a sports team, they are often grouped into the fandom of that team. Anyone who has been to a sporting event has witnessed the variability in the strength of one’s attachment to their team.

Phua (2010) finds that those individuals with stronger attachments to the team actively identify with that team and are influenced by the collective perception of their team. Those fans which did not possess a strong attachment to the team were more or less unaffected by the evaluations

of the team. Thus, if someone possesses a weak attachment to the group, the evaluations of said group may not influence the individual’s self-concept.

A group is not compared to every other available out-group, rather just out-groups which are perceived as relevant comparisons. This second theoretical principle asserts that without positive comparison, positive social identity cannot be obtained. Returning to the sport fan example, not every team is pitted against one another at the same time. Instead, teams are compared based on their opponent or division they play in. In this way, the relevant comparison group are only those which can be directly contrasted to the in-group and possess parallel characteristics.

Lastly, the third principle builds on the first two describing possible ways individuals can accomplish a positive social identity. When one’s social identity is not or no longer positive, individuals will either leave their group, join a positively differentiated out-group, and/or attempt to make their group more positively compared. Tajfel and Turner (1986) state three ways in which one can maintain a positive social identity: individual mobility, social creativity, and social competition.

After realizing their social identity is no longer positive, an individual may leave or disassociate from their negatively perceived group. The individual will assess other groups around them and attempt to cross the boundary which originally differentiated the groups (Reid,

Giles, & Abrams, 2005). Individual mobility is only possible if the boundary between groups is permeable and groups are stable. An individual cannot easily cross into a group when boundaries are strict. An example of a strict boundaries are those groups based on phenotypical characteristics, like skin color (Reid, Giles, & Abrams, 2005). An individual cannot cross into that group if they do not or cannot possess those characteristics. This can be contrasted from

other groups derived from values and beliefs. It is easier to transcend boundaries when divisions are on the basis of an abstract belief in which specific phenotypes must belong to a specific group. For these groups, values/beliefs form the basis of membership such as religion or a political party. Groups must also be stable in the hierarchy (Reid, Giles, & Abrams, 2005). If groups are constantly being reevaluated or membership characteristics change, this makes an individual’s ability to exit their group and join a different group difficult. In order to attain a positive social identity, the individual must be aware of what boundaries to cross and which groups to move to. Returning to Shinnar’s (2008) of Mexican immigrants, several immigrants described how they learned English and sought skills to advance their careers. They hoped that by doing so, they could cross the boundary from unskilled to skilled labor, which they believed their Anglo employers would perceive as better. Although several immigrants attempted to cross boundaries, even more found these attempts to be an unrealistic endeavor due to phenotypic and cultural differences.

If the individual cannot cross into the boundary into the out-group, they will find ways to create a positive evaluation of their in-group. One way in which this may occur is through a redefinition of terms, also called social creativity. A negative social identity may lead individuals and group members to craft new understandings of the terms and evaluations of their group, reclaiming them as positive terms and evaluations (Reid, Giles, & Abrams, 2005). This can occur by altering the criteria of comparison between groups or changing the comparison group (Tajfel

& Turner, 1986). Thus, if groups are compared based on phenotypical characteristics, comparisons might attempt to be shifted to other characteristics. Mexican immigrants took pride in their culture and tradition, they did not want to abandon these values to conform with Anglo culture (Shinnar, 2008). Instead, immigrants altered the dimensions which comparisons could be

made. When comparing their culture with those of their employers, these Mexican immigrants believed their group possessed more desirable traits, redefining the terms of evaluation. Group member may also choose a different group to compare their group to. Instead of comparing their group to the most positively evaluated group, they may shift evaluations to another group which is less positively evaluated.

The last strategy is used when group boundaries are hard to cross and groups are not stable (Reid, Giles, & Abrams, 2005). Groups may engage in direct social competition with the positively evaluated out-group. The group will seek out positive distinction from the out-group by directly questioning and challenging them (Tajfel & Turner, 1986). Members of the group will find ways to derogate the outgroup (Appiah, Knobloch-Westerwick, & Alter, 2013), attempting to shift the negative valence of their group onto the out-group. Further examining the social identities of Mexican immigrants, Shinnar (2008) also noted how some immigrants directly compared their status with their white co-workers. These white workers possessed the same low-income jobs as these immigrants. Based on their race and status as American citizens, some immigrants believed their co-workers would be in a better position in society. Instead, these workers were perceived as worse off by the immigrants due to their lack of progression in society beyond the low-income labor they are completing. Each of these strategies may be used to accomplish the three outlined principles of SIT.

A Partisan Social Identity

Social identity theory has been studied in numerous contexts and in relation to various social identities. In recent years, scholars have begun move beyond phenotypic groups to other possible social identities including political partisanship. Identifying as a Democrat or

Republican, individuals attempt to describe themselves and identify was or as a member of that

group (Green, Palmquist, Schickler, 2002). Following SIT, party membership appears to act like other social identities. As previously noted, one’s group membership should first be internalized to differentiate them from others (Tajfel &Turner, 1986), accompanied by the adoption of characteristics of other in-group members. Several scholars have provided support to the notion of partisans adopting similar characteristics of their co-party members. Levendusky (2009) notes the tendency of partisans to alter their ideological orientations and policy positions in order to fit in with the rest of their partisan group. Partisans, even if they disagree with a policy, will support the option most endorsed by their party (Klar, 2014). In this way, partisans have internalized their group membership through the adoption of in-group characteristics. Tajfel & Turner (1986) suggest that a social situation be presented in which the selection and evaluation of relevant characteristics can occur. Political parties actively compete to attract the attention of the

American public on political issues. As parties battle during elections, their policies and candidates become relevant, salient characteristics to be judged on. As parties are assessed by the public and given attributes, those that identify with said party will be assigned those attributes as well. Whichever party is ascribed with better characteristics may be seen as desirable, and thus attract individuals to identify with them.

Numerous studies have observed partisans engaging in similar maintenance behaviors which are common when one’s identity is threatened or perceived negatively. Partisans negatively evaluate their opposition and members within it, expressing great hostility towards them, calling them unintelligent or selfish. Partisans undergo social creativity by attempting to lift one’s in-group by redefining the outgroup negatively and assigning negative characteristics to outgroup members (Iyengar, Sood, & Lelkes, 2012; Levendusky, 2009). Instead of contrasting groups on the basis of policy opinions, which are more complex, partisans attach negative

personal characteristics to the opposing party, calling them unintelligent or intolerant, redefining the terms of evaluation. Partisans also engage in social competition, taking on the out-group head on. They often do so in a direct fashion through elections or more symbolically. Republicans and

Democrats engage in direct competition as they actively demonize those in the opposing party

(Abramowitz & Saunders, 2006). If a new political policy emerges which is more positively valenced for the opposing party (Republicans on social issues or Democrats on economic issues), an individual will attempt to strongly contrast their in-group with the out-group, attempting to alter the social cues in their favor (Barnidge, 2017). Partisans are able to do so by reframing the issue to other policy positions which benefit their party, such as reframing a social issue as an economic one for Republicans and vice versa for Democrats.

Although partisans may use social creativity and direct competition to maintain a positive social identity, some scholars believe partisans cannot engage in the last strategy of individual mobility. According to Green, Palmquist, and Schickler (2002), party identification is static for long periods of time, and not susceptible to changes based on political contexts. When scholars find that partisans change their party or political affiliation in surveys, they attribute it to either human or measurement error. A common claim is that if individuals attempted to switch their identification to the opposing party, the gap between Democrats and Republicans appears too big of a gap to cross (Levendusky, 2009), and thus must be a result of error. I do not find this argument convincing as they fail to include another political identity with a permeable boundary:

Independent. Geiβ and Schäfer (2017) note that although positive or negative valence of the right or left camp in politics has been studied, the lack of a camp has not been tested. Thus, in this study, I hope to fill this gap in the literature by examining if partisans can engage in individual mobility.

Party membership has been on the decline globally for the past two decades (Gibson,

Greffet, & Cantijoch, 2017). This would suggest fewer people actively identify with a political party. Although this may be attributable to the political climate globally, the media certainly have a role to play. Parties are often portrayed negatively in the news (Geers & Bos, 2017), which may lead to a negative perceptions of both parties. Instead of emphasizing positive contributions parties make, the media prefers to capture a more dramatic side of politics fueled by polarization and competition. The political advertising from the two major parties has grown increasingly negative (Greer, 2012), and the media cover these parties as such. Meanwhile,

Independents have enjoyed positive portrayals (Klar & Krupnikov, 2016). If being a partisan no longer a positive social identity then partisans will have to find a new way to combat these negative characteristics espoused in media. One possible way is by identifying as an

Independent.

In their book, Independent Politics, Klar and Krupnikov (2016) content analyzed stories in the New York Times to examine the way in which Independents were referred to in news coverage. They found that Independents were often alluded to as objective and rational voters, weighing both parties equally before making decisions. Hawkins & Nosek (2012) also imply the same assertion. Independents are distinctly separated from partisans, as partisans are viewed as bias and an obstruction to the democratic process (Klar & Krupnikov, 2016). It is possible that these perceptions in the news are a reflection of American culture of individualism and self- accountability (Gilens, 1999). If Independents are seen as more positive than partisans (Klar,

2014), then it is possible that partisans may seek to enhance their social identity by crossing the boundary to the Independent out-group. As mentioned earlier, the gap between Democrats and

Republicans is perceived as too large to cross (Levendusky, 2009) and are placed in direct

competition. Identifying as an Independent may be an easier transition as Independents as a group not only possess positive characteristics but are abstract to define. Scholars often find that

Independents vote like partisans (Green, Palmquist, & Schickler, 2002), possessing the same policy positions as partisans but under a different label. If Independents are exemplars of a positive political group and can hold policy positions of either party, it is possible for partisans to engage in individual mobility and cross over to an Independent social identity.

Managing a Partisan Image

Erving Goffman (1967) proposed that self-presentation is a vital component of social interaction. Depending on the social context, it is crucial that individuals emphasize some aspects of themselves, possibly deemphasizing other aspects to adhere to social roles (Storsul,

2014). This monitoring of one’s self-presentation is called impression management. Bolino,

Kacmar, Turnley, and Gilstrap (2008) define impression management as the “efforts by an actor to create, maintain, protect, or otherwise alter an image held by a target audience” (p. 1080). By failing to alter one’s image to fit with a target audience, individuals risk negative evaluations and possible social exclusion. When convincingly altering one’s image, an individual can lead others to perceive the individual as likeable or attractive (Bolino et al., 2008). By concealing potentially negative characteristics and/or highlighting one’s positive characteristics, individuals can appear more positively than if they would display their true image (Snyder, 1974). Research on workplace communication notes that employees may engage in impression management around their co-workers. In interviews, some employees described putting on a “Façade of Conformity,” pretending to embrace the same values as those around them (Bolino et al., 2008). In order to present the best version of themselves to coworkers, they altered their image to fit with what is socially desirable in the situation.

One’s private and public identity may diverge if the social risks of expressing the identity are too great. In the case of the workplace, the façade of conformity lifts outside of the workplace when the social context changes. Hawkins and Nosek (2012) suggest that partisanship may actually be an implicit, secretive identity, and Independent may be an explicit social identity. They suggest, “Independents’ explicit identities are partially formed with a conscious goal to be political citizens who are not swayed by partisan positions. However, implicit identities are the result of associative processes linking the self with one or the other political party” (p. 1437). In their study, these researchers found that although individuals did identify as

Independents when asked directly, they implicitly preferred either Republicans or Democrats more. Not only does their study point to an internalized preference of one party, akin to in-group bias, but impression management behaviors. If individuals want to make the best impression on others, portraying oneself as an Independent may be effective. Klar & Krupnikov (2016) asked participants to select the political party which made the best first impression. The results overwhelmingly favored Independents. In order to avoid “social suicide,” partisans may attempt to hide their partisan identity (Zaller & Feldman, 1992). Doing so would not reject a preference toward one party, instead it would allow people to reap the benefits of a positively valenced public label.

Impression management may be even more pronounced in online contexts. In face to face contexts like a workplace, individuals are aware of the social norms and target audience, being able to see them directly (Barnidge, 2017). Online interactions, however, obscure the norms and audience. Storsul (2014) notes that social contexts are collapsed online as individuals need to be more aware of how they present themselves, unsure of the audiences they may reach and the norms associated therein. Without knowing who may see their online interactions, young adults

have tempered their online image to refrain from controversial interactions. The participants in

Storsul’s (2014) study told researchers they are unlikely to post political statuses on Facebook, as they are aware that not only their friends, but also family and possible future employers may see their online activity. The social risks are too great to have undesirable characteristics to their image. While they did not post political opinions themselves, they did like statuses made by others, indicating their interest, but their caution to behave this way themselves.

In addition to status updates, likes, comments, and shares, users may manage the image they portray on their profile. Krämer and Winter (2008) find that individuals whom care more about their self-image are cautious about the depth of information in their profile as well as what their profile picture contains. Those which are more concerned with their image will provide less information on their profile so others cannot make negative assertions from the information.

Profile pictures can similarly convey information to others, and thus those which are more conscious of social cues and risks will provide only attractive pictures of oneself. While studying

Tinder users, Ward (2017) found that users would choose a profile picture which emphasized their ideal version of themselves. Additionally, users would constantly update their profile after viewing other user’s undesirable and desirable components of their profile. These users engaged in impression management strategies by constantly tweaking their profile after assessing social cues from others. Due to the environment on social media, a broad audience, and vague social contexts, individuals may engage in impression management more online than in face to face interactions.

Individuals desire to maintain a positive self-perception when a group is evaluated negatively by others. What differentiates SIT and impression management is true identity versus the expression of one’s identity. SIT suggests that individuals will actually attempt to identify as

different group when their in-group is threatened; meanwhile, impression management suggests that individual will still identify with their in-group privately but will pretend to be a member of another group when they risk being perceived negatively. Partisanship, according to many scholars, is entrenched in one’s self-concept, at least in America (Green, Palmquist, & Schickler,

2002). This is reflected in studies of implicit partisan attitudes among Independents (Hawkins &

Nosek, 2012). Based on these findings, I expect individuals will identify as a partisan in private, socially safe situations, but as an Independent in public, socially risky settings.

As political participation moves to online environments, I expect individuals to engage in impression management of their political identity. This is likely exacerbated in contexts which present partisans negatively over contexts which praise partisans. As Klar and Krupnikov (2016) find in their content analysis, partisans are framed as constantly in conflict, impeding democratic ideals while Independents espouse desirable democratic characteristics like rationality and objectivity. Based on the nature of social contexts, partisans may choose to manage their self- image when exposed to negative portrayals of their group than in contexts which praise them. If partisans are framed in conflict, insulting one another, and refusing to compromise, partisans may be unlikely to express a partisan identity if this would attribute these characteristics onto them. However, if partisans are said to have compromised and passed a key piece of legislation, partisans may be more likely to identify with their party in order to claim this positive evaluation. Thus, I hypothesize that:

H1. When participants are exposed to partisan conflict, they are more likely to select a

non-partisan profile icon than those participants exposed to bipartisan compromise.

H2. When participants are exposed to partisan conflict, they are more likely to select

“Independent” as their political affiliation than participants exposed to bipartisan

compromise.

Self-Monitoring Identity

Despite the social cues indicating partisanship as a negative group identity, some individuals will run counter to the context by continuing to publicly identify as a partisan. Noting that not everyone engages in impression management behaviors, scholars determined that individuals vary in the extent to which they will alter their image for others. Snyder (1974) calls the individual’s willingness to alter their image as self-monitoring. Low self-monitors are more likely to display the same image and behaviors in public as they would in private (Berinksy &

Lavine, 2012). Self-monitoring is defined similarly to impression management, in which individuals will sensitively react to the situations and cues of others (Snyder, 1974). Individuals can possess various levels of self-monitoring. Snyder (1974) notes that as individuals are more concerned with other’s perceptions of them, the more these individuals will self-monitor. An example of individuals which are expected to possess high levels of self-monitoring are actors.

Actors are high in self-monitoring behavior since their job is to hide their true behaviors in favor of the role they are playing (Snyder, 1974). Self-monitoring, as a function of impression management, does not suggest that the individual holds the same attitudes and beliefs as those around them. Instead, individuals will display the characteristics desired by those around them

(Klar & Krupnikov, 2016).

As individuals differ in the extent to which they pay active attention to and regulate their behavior in response to the social context (Berinsky & Lavine, 2012), I expect self-monitoring to moderate the expression of a partisan identity. When presented information cuing negative

evaluation of partisans, individuals high in self-monitoring will likely be more concerned with their image than low self-monitors. Low self-monitors will not react to these cues, opting to continue to present the same image in public as in private. In the context of a social media profile, self-monitoring will be more cautious of the information they provide for others to observe. They will be less likely to complete an in-depth profile which accurate displays all aspects of their identity (Krämer & Winter, 2008). I hypothesize:

H3. Self-Monitoring characteristics will positively influence the selection of non-

partisan/Independent profile options.

H3a. Partisans high in self-monitoring are more likely than partisans low in self-

monitoring to select a non-partisan icon when reminded of partisan conflict.

H3b. Partisans high in self-monitoring are more likely than partisans low in self-

monitoring to select “Independent” as their party affiliation when reminded of partisan

conflict.

Although self-monitoring may explain why some individuals are more likely to alter their political identity in public, other scholars may point to a different explanation. Some individuals engage in impression management, and subsequently self-monitoring, to portray a likeable and attractive version of oneself (Berinsky & Lavine, 2012; Bolino et al., 2008). Others may not be seeking likeability, rather they are fearing exclusion. Thus, an individual may fear that by expressing their political opinion in the form of a political icon or label, they will be excluded from others. Hayes, Glynn, and Shanahan (2005) developed the Willingness to Self-Censor scale to understand the ways in which individuals alter their opinion expression around others.

Drawing from spiral of silence literature, willingness to self-censor may be another individual characteristic moderating whether someone expresses their true image to others.

Avoiding Identity Spiral of Silence

While individuals attempt to express a favorable public identity, they may also stay silent in an attempt to be included by others. The spiral of silence, developed by Noelle-Neuman, is a theory of dynamic public opinion which explains the perpetuation of majority opinions and subsequent silence of minority opinion (Scheufele, 2012). For Noelle-Neuman (1993), public opinion was a form of social control, defining what opinions can be expressed without risking isolation from others. When someone expresses a dominant opinion, they face little social risk; however, should they hold a subordinate opinion, they will stay silent due to a fear of isolation and the social risks involved. Over time, members of society possessing an opposing opinion to the majority will be reluctant to express their opinion, further establishing an unopposed, dominant opinion in society.

As with SIT, the spiral of silence builds from a series of assumptions. First, threat and fear of isolation will lead to social conformity (Scheufele & Moy, 2000). Society threatens individuals with a minority opinion with isolation (Moy & Hussain, 2014). The social pressure which exists in opinion expression is essential for spiral of silence to occur (Scheufele, 2012). In order to avoid isolation, individuals assess the opinion climate to determine the majority opinion.

The risk of isolating oneself from the public is greater than expressing their own opinion, so minority opinion holders will opt to remain silent to avoid isolation (Glynn, Hayes, & Shanahan,

1997). An individual will not encounter a fear of isolation when expressing their opinion privately as the social risks are minimized. The spiral of silence occurs as opinions are expressed publicly where their opinions will be assessed and determined to be consistent with the dominant one or not.

The second assumption states that individuals use a quasi-statistical sense to interpret the opinion climate. The fear of isolation motivates individuals to observe their social environment to determine how best to align with the majority public opinion (Kwon, Moon, & Stefanone,

2009). This assessment is formed on the perceptions of the individual, not the objective public opinion (Scheufele & Moy, 2000); hence, Noelle-Neuman (1993) calls this a quasi-statistical sense, not a strictly statistical one. Once the opinion climate is assessed from one’s perceptions, the spiral of silence assumes individuals will either speak out or remain silent. Individuals will only express their opinion if they possess the majority opinion, while others will stay silent

(Scheufele & Moy, 2000).

The final assumption asserts that a spiral develops over time as minority opinions stay silent and majority opinions continues to speak out. Individuals fear that by expressing their minority opinion, others will exclude them, thus encouraging them to remain silent and effectively drop out of public debate (Mutz & Silver, 2014). As individuals evaluate that the opinion climate is hostile to their minority opinions, they will not voice their opinion. Without the exposure of minority opinions, the majority opinion continues to gain strength leading more individuals to perceive it to be the dominant opinion, silencing more minority opinion holders.

The opinion gains strength, spiraling until the minority opinion is completely silenced. While these assumptions comprise the main mechanism of the spiral of silence, it is not complete with three other factors: moral/value-laden opinions, time, and media.

In order for the spiral of silence to occur, society must assess value-laden opinions over time. Noelle-Neuman (1993) asserts that the spiral of silence will not occur unless issues possess some moral component, making the opinion subject to isolation from the public. An opinion is not considered valued unless the opinion is subject to controversy or moral questioning (Matthes

& Hayes, 2014). Non-moral opinions do not possess social risk as the stakes and strength of them may not be as high. Members of the public continuously assess the opinion climate for these moral opinions to determine if they should fear isolation for expressing their opinion

(Scheufele, 2012). The spiral of silence can only occur over time as the majority is perpetuated and the minority opinions are pressured into silence. Based on these two components, opinions must possess social risk and be assessed over time in order for the spiral of silence to occur.

Media plays a central role on an individual’s assessment of the opinion climate. Noelle-

Neuman (1974) describes the media as presenting consistent and cumulative messages asserting a majority opinion. The media selects an identifiable position on an issue (Scheufele & Moy,

2000), covering this opinion consistently, leading to the perception that this opinion is the majority one. Individuals use their quasi-statistical sense to observe the opinion climate around them which is fueled by confirmatory media messages affirming the majority (Matthes & Hayes,

2014). This being the case, if the media portrays certain opinions as more desired, such as political independence, the public should perceive this opinion to be the majority one.

Individuals that believe political independence is undesirable will remain silent for fear of isolation. As the media continues to promote political independence, more individuals with the opposing opinion will remain silent until no one is willing to describe political independence as undesirable.

While the spiral is occurring, there will be individuals who do not fear isolation, instead maintaining a minority opinion or promoting new contrarian opinions. Hardcores are individuals who stick to a non-dominant opinion even as more and more individuals seemingly oppose their position (Matthes & Hayes, 2014). Avantgardes do not stick to the minority viewpoint, instead they promote new, unpopular viewpoints. These viewpoints are based on ideological belief

systems, concern for the issue, or the opinion of a reference group (Scheufele, 2012). These individuals possess strong opinions and are concerned more with their individual views than the possible isolation they will face from society (Scheufele & Moy, 2000), thus the fear of isolation does not influence opinion expression. Individuals with extremist political opinions will not be swayed by the opinion climate around them, even when seemingly everyone disagrees with them.

One’s quasi-statistical sense is not always accurate. Since the spiral of silence is only concerned with the perceptions of the opinion climate, a dual climate of opinion may exist in which individuals misinterpret which opinion is the dominant one (Shamir, 2014). The media can misreport the dynamics within the public and, through coverage of the “majority” opinion, lead the public to believe this to be the case (Noelle-Neuman, 1974). When this is the case, the media can shape the climate of opinion differently from what would be the objective public opinion.

As a theory of public opinion, the spiral of silence has been applied to political opinions and actions since its conception. Early studies on voting turnout found that when participants were told a particular candidate was more favored, participants were more likely to express support for this candidate (Scheufele & Moy, 2000). Besides voting, all other forms of political participation are public (Hayes, Scheufele, & Huge, 2006), leaving individuals open to public scrutiny and potential isolation. This can be seen in Noelle-Neuman’s (1993) example of her student’s decision to wear a badge supporting a political party. The student removed their badge after feeling uneasy after assessing the opinion climate around her. Despite the impact the spiral of silence has made on political communication research, scholars have found little overall support for the main assertion of the theory that one’s expression of their opinion is influenced

by the perceived climate of opinion (Glynn, Hayes, & Shanahan, 1997). In the next section, I will lay out these criticisms and why partisans may be susceptible to the spiral of silence.

Criticisms of the Spiral of Silence

Opinion Climate Indicators (Media v. Reference Groups)

In the original conception of the spiral of silence, the public assessed the climate of opinion using the media at the national level. Scholars have questioned this understanding, believing the public also relies on reference groups (Scheufele & Moy, 2000) as well. These scholars assert that it is not sensible to disregard reference groups as a source of perceptions.

Assessing the opinions of those in one’s interpersonal network can act as a proxy for the national opinion climate (Chan, 2018). One’s quasi-statistical sense can evaluate the local opinion climate

(Eveland, 2014), applying to an individual’s perception of the overall opinion climate. In an assessment of the spiral of silence locally on social media, Chan (2018) found when individuals perceived their personal social media network as hostile to their opinion, most users did not express their political opinion on the site. In order to maintain status and fearing isolation from their online “friends,” users determined expressing their opinion would be unwise even if the greater public may agree. Thus, reference groups should be considered in addition to media as indicators of the climate of opinion.

Changing Media Environment

The media is expected to offer consistent messages, reinforcing which opinion is dominant in society. This may have been an easy assertion to make in an age of early television, however the media environment now offers more options for individuals to select from , having profound implications for the spiral of silence (Moy & Hussain, 2014). Based on technological advancements, scholars question as to whether the spiral of silence can adequately occur in an

age of increased media choice. Moy and Hussain (2014) offer two competing predictions about the media environment and fear of isolation. First, the fear of isolation will not occur in a fragmented media system. In this system, even niche and minority opinions can find a community which supports these opinions. Second, the fear of isolation may be greater due to the specialized bonds individuals develop in specialized communities.

Based on these predictions, an individual can either seek out a community which agrees with them or feel the threat of isolation from a close group. In line with the second prediction, selective exposure may enhance the fear of isolation individuals experience in a high choice media environment. When an individual selects which media to use, they assess the opinion climate of a reference group, making the environment look more supportive of their opinion than what may be the case objectively (Mutz & Silver, 2014). If this community means a lot to this individual, they may fear going against members of the group, opting to stay silent. Hence, if an individual joins a community, forming close ties with these individuals, they may be reluctant to express an opinion contrary to this group. Although they may have joined the group based on one niche opinion, they are unlikely to hold all opinions consistent with others in the group.

When the group discusses a different opinion which the individual may not agree with, that person may remain silent to avoid risking their relationship with these close ties.

Media fragmentation may also expose individuals to more diverse opinions. Mutz and

Silver (2014) argue that modern U.S. media exposes audiences to cross-cutting perspectives compared to an individual’s interpersonal network. This being the case, individuals which rely on media may feel willing to express their opinion publicly as they can find support from a faction of the public. If the public perceives a dynamic, diverse opinion climate, they may see a lack of consensus around a majority opinion, effectively negating the spiral of silence (Rosenthal

& Detender, 2014). Jeffres, Neuendorf, Bracken, and Atkin (2009) raise a valuable question to consider as well. In a media environment which is open to all opinions, is there really any taboo which would subject an individual to isolation from society? If individuals no longer need to fear isolation from expressing their opinion online, then the spiral of silence may no longer occur generally.

Measures

Utilizing the spiral of silence, scholars have measured key concepts differently, leading to inconclusive results when examining the overall literature. The main measurement concern for scholars is assessing how individuals assess an individual’s willingness to speak out (Scheufele

& Moy, 2000). To do so, most studies depend on asking participants to imagine a hypothetical situation and determine if they would express their opinion (Glynn, Hayes, & Shanahan, 1997;

Scheufele, 2012). In these hypothetical situations, participants are asked to engage in a conversation or express their opinion when the researchers manipulate the opinion climate around them. The concern with this type of measure is multifaceted.

First, scholars often make the mistake of asking the participant to imagine if someone the individual knows expresses a certain opinion (Scheufele & Moy, 2000). This type of expression would not be considered public in the traditional sense of the spiral of silence. In order for the fear of isolation to exist, an opinion must be subject to the scrutiny of an anonymous public

(Noelle-Neumann, 1993). If an individual knows the others in a situation, the publicness of the expression comes into question as the individual may know that the “public” in this hypothetical situation wouldn’t isolate them for holding a contrary opinion.

These hypothetical situations also take place in low risk settings including academic group tutorials and interpersonal discussions (Hayes, 2007). Due to the hypothetical nature of the

situation, individuals may not experience the psychological states which elicit the spiral of silence (Glynn, Hayes, & Shanahan, 1997). In other words, hypothetical situations may not trigger the same mechanisms in experiments as in real life, questioning the external validity of these studies. Additionally, scholars often ignore the media component of the spiral of silence. In these hypothetical situations, participants are interacting with others, isolated from media

(Matthes & Hayes, 2014). Scholars ask participants to imagine a conversational setting which not only varies in publicness and social risk, but whether individuals can utilize media to assess the opinion climate around them. Without access to media and social risk, the psychological mechanisms underlying the spiral of silence may not occur within a study.

Mechanisms

The fear of isolation is considered the chief mechanism for why individuals choose not to speak out against the dominant opinion; however, scholars have questioned if other mechanisms are at play (Scheufele & Moy, 2000). Hayes (2007) provides an extensive list of other possible influences on an individual’s willingness to speak out against dominant opinions including interest, knowledge, strength, shyness, and confidence in their opinion. Even considering these individual characteristics, there are some individuals which will continue to express their opinion regardless of social risk. In response to this concern, scholars attempt to understand additional individual traits which more adequately tap into an individual’s willingness to express their opinion. A trait of interest for this study is an individual’s willingness to self-censor. Different from other individual characteristics (Scheufele, 2012), the willingness to self-censor trait answers several of the questions scholars have risen concerning the spiral of silence (Hayes,

Glynn, & Shanahan, 2005).

Willingness to Self-Censor (WTSC)

In response to critiques of the spiral of silence, Hayes, Glynn, and Shanahan (2005) crafted the willingness to self-censor measure, tapping an alternative explanation for opinion silencing. These researchers define self-censorship as the “withholding of one’s true opinion from an audience perceived to disagree with that opinion” (p. 444). In contrast to the original conception of the spiral of silence, individuals assess their immediate opinion climate. After assessing their opinion climate, individuals weigh all costs of speaking out, including one’s fear of isolation as well as other assessments of that opinion on that individual (Hayes, Scheufele, &

Huge, 2006). WTSC instead of assessing the opinion climate at the national level, individuals will assess their immediate social environment. Hayes (2007) asserts that the national opinion climate is not as important for individuals as their local environments with high risks. Thus,

WTSC responds to scholars’ calls to study the spiral of silence at a more local level.

The extent to which individuals censor their opinions in the face of adversity is an individual trait. Individual traits will vary from person to person leaving each individual with a level of self-censoring tendencies (Hayes, Glynn, & Shanahan, 2005). When an individual is high in self-censoring traits, they are more likely than those with lower levels to monitor and be less likely to express their opinion.

WTSC works well in the context of politics online. The majority of social media users opt to remain silent when encountering counterattitudinal information online and roughly 22% intentionally decide to refrain from posting any politics on their social media profile (Kwon,

Moon & Stefanone, 2015). By posting a political opinion online, this status can be seen not only by one’s close friends but acquaintances, future friends, and possibly strangers depending on the social media site (Storsul, 2014). As the audience grows and becomes broader and more

anonymous, individuals will likely experience a fear of isolation from the community. Kwon,

Moon, and Stefanone (2015) found a significant negative relationship between one’s level of

WTSC and political posting behavior. Individuals were less likely to post political statuses when they possessed a high level of WTSC. Thus, individuals which possess high levels of WTSC are likely to opt out of online political debate. Their assessment of the opinion climate of their online networks yielded the perception of great social risk, leading the individual to censor their opinions online. As individuals evaluate their local climate, they are sensitive to cues which express what opinion is perceived as the majority in that situation. If individuals encounter information expressing an opinion as negative on these sites, then those high in WTSC will assess the climate as unfavorable to them. This could be the case with reading statuses, articles, or passages shared online. I believe that WTSC will influence whether this information leads an individual to express a minority/controversial opinion to their social network. In line with my earlier hypothesis concerning impression management, I hypothesize WTSC will also moderate the effect of exposure of partisan conflict:

H4. WTSC characteristics will positively influence the selection of non-

partisan/Independent profile options.

Individuals cannot remain silent in every situation. An important extension made through

WTSC is the expression of one’s opinion when individuals have no option to stay silent. In some situations, members of the public are expected to express some opinion or risk social consequences. While Noelle-Neuman (1993) asserts that individuals will simply silence their opinion, Hayes (2007) examined a condition in which students must engage in conversation with others, requiring they express their opinion. He found that students most commonly reported that they would express that they have no opinion on the topic at hand, followed by leaving the

situation entirely, declining to comment on the topic, change the subject, and pretending to agree with the group. These behaviors manifest in smaller groups where the risk attached with remaining silent and is outside the purview of the original iterations of the spiral of silence.

The media perpetuates certain opinions as the dominant viewpoint allowing individuals to gauge the opinion climate from viewing the messages in the media. As the media portrays politics as increasingly negative (Greer, 2012), the social costs of agreeing with a particular viewpoint may lead some individuals to engage in self-censoring behaviors suggested by Hayes

(2007): expressing no opinion or neutrality on political matters. In political discussions, individuals can assess the opinion climate to determine how supported their political opinion is to those around them. As individuals do not always have the option to remain silent and refrain from expressing their opinions, they will express no opinion or neutrality on a political issue

(Hayes, 2007). When this is the case, these individuals will refrain from engaging in partisan discussions by selecting what media portrays as the neutral option: political independence. When encountering information framing partisans as negative, individuals must weigh the risk of identifying as a partisan. Based on this behavior, I hypothesize individuals who are high in

WTSC will be more likely to select options to reflect this neutral stance:

H4a. Partisans high in WTSC are more likely than partisans low in WTSC to select a

non-partisan icon when reminded of partisan conflict.

H4b. Partisans high in WTSC are more likely than partisans low in WTSC to select

“Independent” as their party affiliation when reminded of partisan conflict.

Methodology

This study makes use of two online survey experiments to manipulate the evaluations of partisans as either positive or negative. The first survey drew participants from a large midwestern university, and the second utilized participants from Amazon’s MTurk. They first answered demographic questions followed by two scales assessing each participant’s level of self-monitoring and WTSC. Following these scales, participants read a brief article generally intended to prime positive or negative evaluations of partisans. Participants then created a social media profile for a hypothetical discussion site. On this site, participants selected discussion partners they would like to discuss current events with.

Participants

Survey 1: College Sample

In the first survey experiment, I acquired participants through the university’s research pool. Participants were recruited from undergraduate communication courses through an online research portal. Participants sign up for the studies by logging into the research portal site and selecting the studies they wish to participate in. Once they have successfully completed the survey, participants are compensated with course credit.

A college student population appeared to be an ideal sample considering the types of questions posed in this study. Since young adults use social media more than any other group of adults (Pew Research, 2019), a sample of college students may understand the prompts and hypothetical social risks within the study. Students are more likely to have experience with social and online media, making the proposed discussion site easier to imagine (Taylor, 2015). As an additional benefit, the treatments utilized in this study have been previously tested on similar college populations. Klar and Krupnikov (2016) demonstrated their treatment articles were

effective at manipulating the valence of partisanship in a college-aged sample. Additionally, both the self-monitoring (Berinsky & Lavine, 2012) and WTSC scales (Hayes, Glynn, & Shanahan,

2005) have been validated and replicated using college samples. In order to remain consistent with these previous studies, I restricted the sample in my first survey to English-speaking college students.

Survey 2: MTurk

In contrast to my initial survey, I attempted to replicate the results of my initial survey on a different sample of the American public. Participants were recruited through Amazon’s

Mechanical Turk (MTurk) using a HIT posting on the site. After agreeing to participate, participants were compensated monetarily. Compensation was comparable to similar online surveys of a similar length. By including an Amazon MTurk survey, I attempted to extend Klar and Krupnikov’s (2016) treatments beyond a college student sample to older adults. It may also be possible that the prompts and task present in the study may not be easily interpreted by an older sample. Thus, by replicating results from the first survey on a different sample, I can express more valid results. I can also account for possible differences between the samples should results be inconsistent between them.

Obtaining a Private Partisan Identity

During this study, I manipulated the valence of partisanship in an attempt to witness a different public partisan identity than the one a participant holds privately. To do so, I acquired each participant’s party identification before treatment. Depending on when I asked participants for their party affiliation, my results may be influenced. For example, high self-monitors are hyper aware of their social settings and will attempt to portray the best versions of themselves to their target audience at all times (Snyder, 1974). If a high self-monitor acknowledges the

researcher as the target audience when participating in a survey, they may pay closer attention to social cues which can lead them to guess the researcher’s goal for the study and alter their image to fit what the researcher wants. By asking partisanship early on, high self-monitors may attempt to maintain their political identification across the study, or if they guess correctly, may change their political identification for the wrong reason, to appease the researcher (Kuklinski, Cobb, &

Gilens, 1997). Additionally, by introducing questions of political identification early in the study,

I may unintentionally prime participants to think about politics during the self-monitoring and

WTSC measures. These measures should be answered free of political influence, as they are private personality-based questions.

Survey 1: Screening Questionnaire

To avoid these problems, I utilized the screening questionnaire provided through the recruitment tool for my college student sample. When participants register to participate in the research program, they are required to complete a battery of demographic questions. Researchers which recruit participants from the research pool have access to these responses and may restrict their studies to only include students which respond in a particular way to these questions. For this study, I am interested in each participant’s political affiliation. I collected this data to avoid asking these questions within the beginning of my survey. The time lag and distance between the screening questionnaire and the survey should not lead to carry over effects as the screening was completed at the start of the semester and is not directly connected to this study. By accessing the screening data, I matched each participant to their earlier response without the need of introducing unnecessary confounds to my survey.

Survey 2: Embedded Question

No screening questionnaire of the same capacity was conducted for the second survey on

MTurk. I believed the costs of conducting a two-wave study would outweigh the benefits. The screening questionnaire would have been too short, and I would have needed to worry about attrition between surveys. While the strength of this decision lies in the ability to include all questions in one survey, I was aware of possible priming or cues associated with asking political questions prior to treatment. I believed this concern is minimal, however, due to the frequency of political questions in surveys. Since MTurkers participate in more studies by nature of this research pool, they are likely to encounter similar political demographic questions in other studies. These questions were unlikely to raise suspicion when they are exposed to similar questions on a regular basis.

Pre-Treatment Survey

All participants first answered basic demographic questions (See Appendix A). The answers to these demographic questions indicate the diversity and representativeness of the sample. Research on impression management notes demographic differences in high/low self- monitoring and self-censoring (Hayes, Glynn, & Shanahan, 2005). Depending on the sample demographics, self-monitoring and WTSC levels may be higher or lower. Younger adults and women are more likely to possess high levels of these characteristics (Krämer & Winter, 2008).

If the composition of these samples differs on these characteristics, results may be different based on demographics. Demographic information is additionally used to compare the distribution of the moderators across demographics with previous studies, to further assess these measures. Following these demographic questions, participants responded to self-monitoring and self-censoring scales which will be explained in greater detail in the measures section.

Treatment

After responding to these measures, participants were asked to read a short passage about a common occurrence in the United States as they would read a news article. In order to cue attention to the passage, participants were told that they will answer questions following the article. By juxtaposing the article and warning of further questions, participants should have paid more attention to the treatment articles. Participants are likely exposed to similar short passages of information in their day to day media environment as indicated by social media character limits of posts and push-button mobile notifications. According to a study by Pew Research

(Shearer & Matsa, 2018), young adults receive most of their news from social media such as

Instagram and Snapchat. Both of these social media platforms provide condensed content and captions. As young adults use these sites, they become accustomed to the shortened format and should be comfortable reading a short paragraph in this study. Older adults also use social media to obtain news (Shearer & Matsa, 2018), but often rely on television as well. Television news also presents information in a condensed format as indicated by the fragmentation of the news cycle (Bennett, 2016). Although the treatment is text-based and not visual, older adults are still used to receiving condensed information in a similar way. Based on the media habits of the populations drawn from in this study, I did not expect any problems to arise with unfamiliarity to shortened news content.

Participants were randomly assigned to read either a non-political, positively valenced, or negatively valenced article. The passages were adapted from Klar and Krupnikov’s (2016) studies. The non-political passage was a paragraph describing the tradition of Groundhog’s Day and Punxsutawney Phil. Both political passages concerned a vague yet crucial piece of legislation passing through Congress. In the positively valenced condition (bipartisan

compromise), both parties seemed at odds, but ultimately compromised to pass the bill. In the negatively valenced condition (partisan conflict), both parties also seemed at odds, flung insults at one another, and failed to pass the bill. All passages were similar in length and language difficulty. The political passages mirrored one another, only varying in the outcome. (See

Appendix B for full passages).

No differences should have arisen between the non-political and bipartisan compromise condition; however, I am cautious to assume that the mention of politics did l not elicit negative personal evaluations. As the current media environment continues to paint the picture of partisanship in an undesirable manner, the mention of politics may allude to partisanship generally. If the participant perceives partisanship as negative before entering the study, they may see the mention of Democrats and Republicans, and react similarly regardless of the outcome of the bill. If this is the case, no difference would appear between conditions.

Discussion Site Task

After reading one of the three articles, participants were prompted to imagine a website in which either their university (in the case of college participants) or Twitter (in the case of MTurk participants) was designing a site for users to be matched with one another to discuss current events. In order to match users with one another on the site, each user creates a profile and rates other users, similar to the matching software present on dating apps like Tinder. The prompt instructed participants to first create their profile. Once complete with this task, they rated other profiles.

Profile Construction

To construct their profile, participants selected a profile icon, political affiliation, political attitudes, favorite genre of music, favorite genre of movie, and profile tagline (See Appendix C

for full profile construction task). These options mirrored similar profile options on other social media sites, such as Facebook and Twitter. The first selection participants made was their profile icon. Participants selected an icon based on Klar and Krupnikov’s (2016) sticker experiment. In this experiment, participants primed by partisan conflict selected the non-partisan sticker over the partisan sticker options. The options provided in their experiment were the Democratic Party logo, the Republican Party logo, and a bald eagle (signifying Independent). By selecting a bald eagle sticker, participants disassociated publicly from either party. The icons in this study utilized similar graphics to gauge the presentation of identity. To further assess identity expression, participants selected either “Democrat”, “Republican”, or “Independent” as their party affiliation. Participants could opt to select contrasting icons and party affiliation due to the extent to which one is seen as more public than others (Hayes, Scheufele, & Huge, 2006). On many social media sites, users first see the profile icon of another user before seeing information in their profile. Thus, it was possible that participants selected a non-partisan icon, while still selecting a partisan affiliation in their bio. By assessing both icon and label selection, I can account for inexperience or confusion surrounding the meaning of each icon. Individuals may have been unable to ascribe a political party to the icons, as each option was not labeled. If this portion of the profile was unclear, the selection of an affiliation may provide more information than the icon selection.

Participants also rated five statements on political issues as indicators of their partisanship. These statements concerned topical, partisan issues in order to ensure that the participant has likely encountered these issues at least in passing (Morey, Kleinman, & Boukes,

2018; Pew Research Center, 2019). This measure may have also been affected by the treatment as well, causing those who self-monitor and self-censor to moderate the expression of their issue

positions (Storsul, 2018). Each statement corresponded with an issue position used later in the discussion partner phase. Although this data was collected, the relationship between self- monitoring and self-censoring on policy position is outside the purview of this study and will not be assessed here.

Favorite genre of movie and music as well as the tagline were included to disguise the political aspects of the profile. Many social media profiles include components beyond political options, which may have assisted participants to be truthful by disguising intentions of the study.

For both genre questions, participants selected one preferred genre from a list. The tagline was open-ended, allowing the participant to type whatever they felt comfortable. None of these questions are included in analysis.

Discussion Partner Ratings

In reference to the first prompt, participants were reminded that the discussion site wished to match students based on their preferences. During this section, portions of imaginary profiles created by other users were visible. Only the party affiliation and issue position of each profile was provided. Participants viewed five sets of four individual profiles. In each set, the participant saw two Republicans and two Democrats, and two profiles with the same preferred issue position and two with the opposing issue position. The composition in each set was the following: a Democrat with the preferred position, a Democrat with the opposing position, a

Republican with the preferred position, and a Republican with the opposing position. (For full measure, see Appendix D.) Participants ranked these profiles from most likely to talk to (position

1) to least likely to talk to (position 4). Similar to the policy statements above, these ratings fall outside of the focus for this study. Future studies can draw upon these ratings to examine the

relationship between identity, self-monitoring, and self-censoring on the selection of discussion partners.

Debrief

As participants were prompted to engage with a hypothetical discussion site, they may have expected to discuss with others, despite being told the site was imaginary. To reduce any lingering confusion, participants were reminded that the site did not actually exist, and their responses would only be seen by the researchers.

Measures

Demographics

All participants answered basic demographic questions at the start of the study.

Participants provided their age, gender, race, and ethnicity. Following these questions, participants indicated if they are registered to vote. In the MTurk survey, these participants also indicated their political affiliation due to a lack of a screening survey. Demographic data was used to examine the composition of the study in order to contrast my samples. Demographic responses demonstrate the differences between samples which may influence the results of the study.

Self-Monitoring

The evaluations and characteristics of a group shape an individual’s personal identity

(Tajfel & Turner, 1986). If the group becomes negatively evaluated or undesirable, the individual is likely to alter their identification to reflect more positive evaluations. One method is to shift their image to one that is desirable by others around them through self-monitoring. Snyder

(1974) developed and validated the first iterations of a self-monitoring scale. He demonstrated that his scale accurately assessed populations which were expected to have high levels (actors)

and those we expect to possess low levels (psychiatric patients) of self-monitoring. He also differentiated other measures which bare resemblance to self-monitoring, including the

Marlowe-Crowne Social Desirability Scale and several Need for Approval scales, but do not follow the same mechanisms.

While Snyder (1974) presented a valid measure of self-monitoring, the instrument was not reliable or adequate for use in surveys (Snyder & Gangestad, 1986; Berinsky & Lavine,

2012). When a conducting a factor analysis of data from Snyder’s initial scale, three emerge.

Snyder and Gangestad (1986) argued that a proper measure for self-monitoring should only possess one factor. They also believed the initial 25-indicator scale would be too long for use in surveys containing multiple measures. To correct these issues, Snyder and Gangestad (1986) crafted a shortened 18-item measure which only possessed one factor. Berinsky and Lavine

(2012) continued to improve on the self-monitoring scale by condensing it further and changing the measurement scale. The original scale used by Snyder (1974) utilized true/false questions to assess self-monitoring. Berinsky and Lavine (2012) believed the dichotomous, true/false measure an individual’s behavior wouldn’t provide as powerful results as an ordinal, Likert-scale measure. Participants may be able to better indicate how often they engage in self-monitoring behaviors using a scale, instead of selecting, “yes, I do that,” or “no, I don’t do that.” In this way,

Berinsky and Lavine (2012) altered the scale so participants could rate statements using a Likert- scale to respond to how frequently they engage in self-monitoring behaviors. This way of measuring self-monitoring provides more variations in the levels of self-monitoring within a sample which may be more representative of reality. With the dichotomous scale replaced with a

Likert-scale, larger variability appears between high and low self-monitors. Berinsky and Lavine

(2012) verified this to be the case by demonstrating the improved reliability of their scale compared to the former true/false format using ANES data.

In this study, self-monitoring characteristics are assessed using Berinsky and Lavine’s

(2012) adaption of Snyder and Gangestad’s (1986) self-monitoring scale. The instrument asks participants to note how often they engage in self-monitoring behaviors within the statements which described extraversion and personal desirability. Some example statements included are:

“I have considered being an entertainer” and “In a group of people, I am rarely the center of attention.” Participants in the present study saw six statements and rated on a 5-point scale how closely they agreed with each statement (See Appendix A). Although earlier studies mention high reliability of these scales, this study did not seem to have similar levels of reliability

(College sample: α = 0.47; MTurk sample: α = 0.63). I continued with this measure as it was theoretically important for this study, despite less reliability than was originally expected.

WTSC

Developed by Hayes, Glynn, and Shanahan (2005) the Willingness to Self-Censor Scale attempts to measure a participant’s tendency to alter their outward presentation of an opinion when their opinion is in the minority. Participants responded to eight statements on a 5-point

Likert scale, like the previous self-monitoring statements (See Appendix A). These statements described hypothetical situations when the participant’s opinion is in the minority. Statements include, “It is difficult for me to express my opinion if I think others won’t agree with what I say,” and “It is safer to keep quiet than to publicly speak an opinion that you know most others don’t have.” This measure possessed a high level of reliability (College sample: α = 0.83; MTurk sample: α = 0.85).

The WTSC scale gauges the expression of opinion due to social risks and has been tested against dispositional shyness. Although somewhat correlated, the scale does tap into characteristics distinct from shyness (Hayes, Glynn, & Shanahan, 2005). This is an important distinction between self-monitoring and self-censoring behaviors. The self-monitoring scale measures a form of extraversion, a contrast to shyness, while WTSC measures expression due to social risk. Based on this assertion, these two scales may be correlated to an extent; however, according to each theory, each scale measures distinct concepts.

Partisan Identity Expression

A participant’s pre-treatment, private partisan identity acted as the starting point for possible change in political identity. For college participants, I utilized the screening questionnaire to gather each participant’s party affiliation. Without access to a similar screening survey for MTurk participants, I requested their party affiliation among the basic demographic questions. While this may lead to possible influences when encountering the treatment, priming political attitudes, I doubted asking for party identification would raise much suspicion. MTurk participants take surveys which ask for demographics including basic political attitudes on a regular basis by the nature of the research pool. For my college sample, individuals could select

“Republican”, “Democrat”, “Independent”, “Libertarian”, “Other”, or “None.” For sake of this study, I exclude all selections except “Republican” and “Democrat.” MTurk participants possessed four options for party affiliation: “Democrat,” “Republican,” “Independent,” and

“Other.” As with my college sample, I excluded participants which selected “Independent” and

“Other.”

In this study, I expected one’s expression of party identity following the treatment to vary from their initial selection of a partisan identity. As the initial assessment of one’s political

identity was prior to treatment, I believed this expression to be the participant’s honest, private political affiliation unhindered by social risk. Participants stated their political affiliation following treatment in the form of an option on a public profile. Following the treatment, participants were asked to select a political profile icon and a political affiliation for the discussion site. They were reminded that others on the site could see these selections. Under the possible scrutiny of and isolation from others, I hypothesized that the expression of one’s party affiliation would vary from their initial selection.

During the profile construction phase, participants selected a Republican, Democrat, or

Independent icon and/or affiliation from a list. A participant could select a mismatching affiliation and icon. The profile icons correspond to Klar and Krupnikov’s (2016) sticker study in which participants heard a prompt priming perceptions of partisans as either positively or negatively evaluated, answered a question about the upcoming election, and then were compensated for participating by taking a sticker. The researchers observed which sticker was taken by the participants even though the participants believed they were simply being rewarded a sticker for their participation. Those that were high in self-monitoring, and thus concerned about other’s perceptions of them, selected the Independent option, represented by a bald eagle.

In this study, I utilized similar icons, changing the realistic bald eagle photo sticker to a red, white, and blue eagle logo similar to the Republican elephant logo and Democratic donkey logo.

I desired to make the eagle sticker look similar to these logos to reduce any desire to select a photo over a logo. Partisan labels were listed as a multiple-choice options. For this study, I am only concerned with partisans who select options which either match their private political affiliation or alter their image by selecting the Independent affiliation. If a partisan selected

options counter to their private party affiliation (a Democrats selected a Republican option and vice versa), they were excluded from analysis.

Policy Preferences

As part of the profile creation phase, participants indicated their level of agreement to six policy-based statements. They selected how strongly they agreed or disagreed with the statement on a 5-point Likert Scale. Each statement corresponded with an issue undertaken by either the

Republican or Democratic party in recent years. These preferences were not utilized in this study’s analyses. In the future. this data can be used to assess identity, self-monitoring, and

WTSC in relation to policy preferences.

Attention & Manipulation Checks

Participants completed two attention check and one manipulation check question.

Between the self-monitoring and WTSC measures, participants were asked to answer “Never” for the statement, “Please answer ‘Never’ for this question.” Following treatment, participants were asked to recall what occurred in the passage. For the partisan conflict and bipartisan compromise condition, participants indicated if the bill was passed or not. For the non-political treatment, participants selected which holiday the passage described. Following the treatment attention check, participants responded to a three option, multiple choice question as to how partisans were portrayed in the passage. If the treatments were properly manipulating perceptions of partisans, I expected those in the partisan conflict condition to select partisans as negatively portrayed for insulting one another and failing to pass the bill, while those in the bipartisan compromise condition would select that partisans were positively portrayed for compromising and passing the bill. Those in the non-political condition also answered this manipulation check.

Table 1: Demographics of Each Sample College Students MTurk N = 632 N = 601 Age (M , SD) 20.44 (3.15) 36.79 (10.88) Male (N) 199 369 Female (N) 431 227 White (N) 524 456 Black (N) 58 101 Other Races (N) 32 34 Democrat (N) 221 261 Republican (N) 115 163 Independent (N) 94 170 Self-Monitoring (M, SD) 2.91 (0.54) 2.57 (0.66) WTSC (M, SD) 2.80 (0.71) 2.77 (0.78)

I expected these participants to answer the question with “Republicans and Democrats were not mentioned.”

Participant Demographics

For the first survey, I utilized a college sample consisting of 632 participants (see Table

1). Participants ranged from 18-53 years old (M = 20.44, SD = 3.15). This sample consisted of more females (N = 431) than males (N = 199). The majority of this sample also identified as white (N = 524). 58 participants described their race as black. 32 participants identified as a race other than white or black, while 18 decline to provide their race. For this sample, the average level of self-monitoring was M = 2.91 (SD = 0.54), just below the mid-point of the 5-point scale.

Similarly, the average WTSC level was below the mid-point of the scale (M = 2.71, SD = 0.71).

The MTurk survey consisted of 601 participants (see Table 1). The age of this sample is higher than that of the college sample (M = 36.79, SD = 10.88). Further, this sample consisted of a more even split between male (N = 369) and female (M = 227) participants. Racial composition

was similar to the college sample, as the majority of participants are white (N = 456); however, more participants are black (N = 101) than the college sample. 34 participants indicated a race other than white or black, and 8 declined to provide their race. The MTurk sample possessed a lower average self-monitoring score (M = 2.59, SD = 0.66) than the college sample. This difference was expected as older adults tend to possess an established self-image, while young adults are still forming theirs and worry about the perceptions of others more (Taylor, 2015).

Their WTSC level, on the other hand, is similar (M = 2.77, SD = 0.78). The differences between each sample could contribute to the generalizability of this study by demonstrating that if there is an effect in both samples, we might see the same phenomenon across a larger population. Should an effect appear in only one sample, I can speculate based on the differences across the samples to determine if that sample possessed unique characteristics which the other did not.

In both studies, only self-identified partisans were analyzed since this study attempts to determine if partisanship is expressed differently across conditions. For the college sample, many participants (N = 67) decided to decline to provide their political affiliation on the screening questionnaire. In total, 135 participants identified with a political party other than Democrat,

Republican, or Independent. Independents (N = 94) were further excluded from analysis as they are not the focus of this study. This leaves 221 Democrats and 115 Republican participants for analysis in this sample (N = 336).

MTurk participants selected their partisan identity from four options, Democrat,

Republican, Independent, and Other. More participants identified as being an Independent (N =

170) than a Republican (N = 163) which is an interesting phenomenon and indicative of

Independents representing a sizable portion of the public. Although interesting, Independents are

excluded from analysis in this. In total, 424 participants identified as either a Democrat (N = 261) or a Republican (N = 163).

Results

For my analyses, I examined each hypothesis using both samples. I did not combine samples. I did this to examine the differences between each sample so if one sample yielded significant results, I could speculate as to why. For each hypothesis, I offer a review of the hypothesis, the statistical test utilized to test said hypothesis, and the data corresponding to these tests.

H1: Partisan Conflict elicits Icon Change

Hypothesis 1 states that participants in the partisan conflict condition were more likely to select a “non-partisan” icon, effectively altering their expression of their partisan identity, than those in the bipartisan compromise condition. In order to test this hypothesis, I isolated the partisan participants which read the conflict passage and the compromise passage. Once these individuals were isolated, I coded whether these partisans chose an icon consistent with their initial partisan affiliation (coded as 0) or if they selected the non-partisan/Independent icon

(coded as 1). I then compared the means for both groups using Welch’s two-sample t-test. In order to support this hypothesis, I should find that the mean for the partisan conflict condition was significantly larger than the mean for the bipartisan compromise condition.

As indicated in Table 2, no significant difference exists between the means of each condition in my college sample, t = -0.88, df = 189.57, p = 0.38. In this sample, I found that more participants in the bipartisan compromise condition (M = 0.26) expressed a different partisan identity than those in the partisan conflict condition (M = 0.20) which is in the opposite direction than predicted. Unlike my college results, my MTurk sample demonstrated a significant difference between the mean for the partisan conflict condition (M = 0.63) and the mean for the

Table 2: Hypotheses 1 & 2 – Welch’s T-Test M M (Partisan Conflict) (Bipartisan Compromise) t (df) p College Students Icon Change 0.20 0.26 -0.88 (189.57) 0.38 Label Change 0.09 0.08 0.37 (206.8) 0.70 MTurk Icon Change 0.63 0.47 2.62 (260.39) <0.01 Label Change 0.17 0.09 1.90 (271.83) 0.06 Note: Any Results in bold-faced font indicate p < .05

bipartisan compromise condition (M = 0.47), t = 2.62, df = 260.39, p < .01. Thus, I receive partial support for H1 from my MTurk sample.

H2: Partisan Conflict elicits Label Change

Similar to H1, hypothesis 2 asserts that participants in the partisan conflict condition would select the Independent political label on their profile more than those in the bipartisan compromise condition. After creating variables indicating if participants selected the label consistent with their initial partisan identity (coded as 0) or selected the Independent option

(coded as 1), I compared the means of these conditions using Welch’s two-sample t-test.

I found no support for H2 with either sample (see Table 2). Among my college sample, the mean for the partisan conflict condition (M = 0.09) did not significantly differ from the mean for the bipartisan compromise condition (M = 0.08) on selecting the Independent label. Both conditions were as likely to select their partisan label, t = 0.37, df = 206.8, p = 0.70. For the

MTurk sample, results were on the verge of significance, t = 1.90, df = 271.83, p = 0.06. On average, participants in the partisan conflict condition (M = 0.17) did select Independent more than those in the bipartisan compromise condition (M = 0.09), but this difference is not significant. Based on the results from both samples, H2 is not supported.

Table 3: Hypothesis 3 – Correlations between Self-Monitoring and Independent Options r df p College Students Icon Change 0.01 303 0.91 Label Change 0.05 328 0.32 MTurk Icon Change 0.16 388 .001 Label Change 0.04 410 0.38 Note: Any Results in bold-faced font indicate p < .05

H3: Self-Monitoring on the Expression of Partisanship

In hypothesis 3, I expected a significant positive relationship between self-monitoring levels and the selection of non-partisan/Independent options over partisan ones. Each participant’s self-monitoring score was calculated as the average score for the six self-monitoring questions, ranging along a 5-point scale. Higher scores indicate a higher likelihood that individual will engage in self-monitoring scores. Similar to H1 and H2, when a participant selects a non-partisan/Independent option, they receive a code of 1, while those who select the corresponding partisan option, they are coded as a 0 on that dimension. Thus, the higher score indicates change to an Independent option. In the college sample, self-monitoring score ranged from 1.50 to 4.83 (M = 2.91, SD = 0.54). MTurk participants ranged from 1 to 4.5 (M = 2.57, SD

= 0.66). If H3 is supported, Pearson’s correlation test should indicate a significant positive correlation between self-monitoring and the selection of a non-partisan/Independent options.

When conducting these correlations, the relationship is in the predicted direction, but only one correlation was significant (see Table 3). When examining the college sample, no correlation existed between self-monitoring and icon change, r (303) = 0.01, p = 0.91. Likewise,

self-monitoring did not significantly correlate with label change, r (328) = 0.05, p = 0.32.

Neither result is close to significance. The MTurk sample revealed one significant correlation between self-monitoring and icon change. For these participants, as self-monitoring increased, so too did the selection of a non-partisan/Independent icon, r(388) = 0.16, p = .001. Unfortunately, no significant correlation was found for self-monitoring and label change, r(410) = 0.04, p =

0.38, consistent with the college sample. Thus, H3 receives partial support from the MTurk sample.

Two hypotheses follow from H3. Participants who are high in self-monitoring are more likely than participants low in self-monitoring to select the non-partisan/Independent icon

(Hypothesis 3a) and select Independent as their party label (Hypothesis 3b) when reminded of partisan conflict. In order to test these hypotheses, I test whether self-monitoring moderates this relationship through a multiple linear regression. This multiple linear regression was calculated to predict icon change and label change based on the article condition (partisan conflict or bipartisan compromise) and self-monitoring.

Neither hypothesis was supported (see Table 4 & Table 5). When examining self- monitoring for the dependent variable of icon change in the college sample, the overall model was not significant, F (3, 193) = 0.30, p = 0.82, r2 < .01. This was also the case when the dependent variable was label change, F (3, 204) = 0.51, p = 0.68, r2 = .01. The MTurk sample also yields unsupportive results. When examining self-monitoring for the dependent variable of icon change, the overall model was significant, F (3, 258) = 3.96, p < .01, r2 = .04. This model accounts for 4% of the variability, which is small but still significant. Specifically, this analysis showed that self-monitoring is a significant predictor of icon change (B = 0.13, SE = 0.06, t =

2.052, p < .05), but article condition was not (B = 0.34, SE = 0.24, t = 1.437, p = 0.15). Despite

Table 4: Regression Model – Icon Change (DV), Article Condition & Self-Monitoring (H3a) B (SE) t p F (df) p r2 adj. r2 College Students 0.30 (3, 193) .82 .00 -.01 Constant 0.21 (0.27) 0.79 .43 Article Condition -0.10 (0.35) 0.35 .78 Self-Monitoring 0.16 .88 0.01 (0.09)

Interaction 0.02 (0.12) 0.15 .88 MTurk 3.96 (3, 258) .01 .04 .03 Constant 0.13 (0.17) 0.78 .44 Article Condition 0.34 (0.24) 1.44 .15 Self-Monitoring 0.13 (0.06) 2.05 .04

Interaction -0.07 (0.09) -0.82 .41 Note: Any Results in bold-faced font indicate p < .05

Table 5: Regression Model – Label Change (DV), Article Condition & Self-Monitoring (H3b) B (SE) t p F (df) p r2 adj. r2 College Students 0.51 (3, 204) .68 .01 -.01 Constant -0.05 (0.17) -0.30 .76 Article 0.02 (0.23) 0.08 .94 Condition Self-Monitoring 0.04 (0.06) 0.78 .44

Interaction <0.01 (0.08) <0.01 .99 MTurk 1.69 (3, 275) .17 .02 .01 Constant 0.21 (0.11) 1.85 .07 Article -0.14 (0.16) -0.88 .38 Condition Self-Monitoring -0.04 (0.04) -1.03 .30 Interaction 0.08 (0.06) 1.38 .17 Note: Any Results in bold-faced font indicate p < .05

the significance of self-monitoring, the interaction between these variables is insignificant (B = -

0.07, SE = 0.09, t = -0.82, p = .41). Due to the insignificance of the interaction between self- monitoring and article condition, no moderation effect can be supported, so H3a is not supported.

H3b also receives no support with the MTurk sample. The overall model was not significant for label change as the dependent variable, (F (3, 275) = 1.69, p = 0.17, r2 = 0.02). Thus, based on both samples neither H3a nor H3b is supported.

H4: WTSC on the Expression of Partisanship

Similar to self-monitoring, I also hypothesized that a significant positive relationship would exist between one’s WTSC level and selection of non-partisan/Independent options

(Hypothesis 4). WTSC levels are calculated as the average score of each of the eight WTSC questions ranging on a 5-point scale. Higher scores indicate higher levels of WTSC. As I have previously noted, when a participant alters the expression of their partisan identity, they are coded as a 1 along the icon or label dimension. If they do not change and select partisan options consistent with their partisan identity, they are coded as 0. Both samples possessed similar average WTSC scores. The college sample ranged from 1.5 - 4.83 (M = 2.80, SD = 0.71), and the

MTurk sample ranged from 1 to 5 (M = 2.77, SD = 0.78). H4 would be supported if a correlation test indicates a significant positive correlation between WTSC levels and the selection of a non- partisan/Independent icon and selection of an Independent label.

No support is found for H4 with either sample (see Table 6). The correlation between

WTSC and icon change in the college sample is in the predicted direction, but insignificant, r(303) = 0.09, p = 0.14. This is also the case for WTSC and label change, r(328) = 0.06, p =

0.27. Neither relationship is significant for the MTurk sample: WTSC and icon change, r(383) =

Table 6: Hypothesis 4 – Correlations between WTSC and Independent Options r df p College Students Icon Change 0.09 303 0.14 Label Change 0.06 328 0.27 MTurk Icon Change 0.06 383 0.23 Label Change 0.03 406 0.59

0.06, p = 0.23, as well as WTSC and label change, r(406) = 0.03, p = 0.59, are insignificant, failing to support H4.

Hypotheses 4a and 4b predicted a moderating relationship of WTSC on the selection of icons given the article condition (see Table 7 & Table 8). As with previous results, neither model was significant. When the dependent variable was icon change, the model was not significant, F

(3, 193) = 0.83, p = .48, r2 = .01. Likewise, when the dependent variable was label change, the overall model was insignificant as well, F (3, 204) = 0.60, p = .62, r2 = .01. When examining the

MTurk sample, the overall model for when icon change was the dependent variable was significant, F (3, 255) = 4.00, p = .01, r2 = .04. Investigating the individual predictors, both

WTSC (B = 0.11, SE = 0.05, t = 2.05, p = .04) and article condition (B = 0.51, SE = 0.23, t =

2.20, p = .03) were significant predictors of icon change. While both individual predictors were significant, no moderation relationship is found as indicated by the insignificant interaction term between these variables, B = -0.12, SE = 0.08, t = -1.53, p = .13. Due to the lack of support through either model for the dependent variable of icon change, H4a is rejected. H4b is also rejected as the overall model when the dependent variable is label change was also insignificant

Table 7: Regression Model - Icon Change (DV), Article Condition & WTSC (H4a) B (SE) t p F (df) p r2 adj. r2 College Students 0.83 (3, 193) .48 .01 -.00 Constant 0.08 (0.17) 0.48 .63 Article -0.00 (0.23) -0.01 .99 Condition WTSC 0.06 (0.61) 1.06 .29 Interaction -0.02 (0.08) -0.23 .82 MTurk 4.00 (3, 255) .01 .04 .03 Constant 0.15 (0.16) 0.94 .35 Article 0.51 (0.23) 2.20 .03 Condition WTSC 0.11 (0.05) 2.05 .04 Interaction -0.12 (0.08) -1.53 .13 Note: Any Results in bold-faced font indicate p < .05

Table 8: Regression Model – Label Change (DV), Article Condition & WTSC (H4b) B (SE) t p F (df) p r2 adj. r2 College Students 0.60 (3, 204) .62 .01 -.01 Constant 0.08 (0.11) 0.77 .44 Article -0.12 (0.15) -0.80 .42 Condition WTSC -0.00 (0.04) -0.06 .95 Interaction 0.05 (0.05) 0.94 .35 MTurk 1.17 (3, 273) .32 .01 .00 Constant 0.01 (.011) 0.13 .90 Article 0.17 (0.16) 1.07 .29 Condition WTSC 0.03 (0.04) 0.80 .42 Interaction -0.03 (0.05) -0.66 .51 Note: Any Results in bold-faced font indicate p < .05

when utilizing the MTurk sample, F (3, 273) = 1.17, p = .32, r2 = 0.01, just as the model was insignificant for the college sample.

Discussion

H1 & H2: Changing Partisanship

I received partial support for my initial hypotheses building on impression management theory. I predicted that when confronted with partisan conflict, one’s partisan identity would be assigned negative characteristics. In order to avoid the group’s evaluations from affecting one’s self-perception, an individual would alter the expression of their social identity, presenting themselves as a non-partisan/Independent to avoid negative perceptions by others. Although I believed this to be the case, I only found partial support for H1. All results were in the predicted direction, but only one t-test using the MTurk sample was significant.

One reason I did not find support was the manipulation which was used. When looking at the manipulation check, 76% of college participants (N = 386) and 77% of MTurk participants

(N = 335) passed the manipulation check. The manipulation check asked participants how partisans were portrayed in each passage. For some in the partisan conflict condition, they believed partisans were portrayed positively (college: N = 21; MTurk: N = 31, ). Likewise, several in the bipartisan compromise condition believed partisans were portrayed negatively

(college: N = 57; MTurk: N = 24). These interpretations of each passage could be a sign of a weak manipulation, possibly due to affective polarization.

Affective polarization builds on social identity theory to explain the separation between

Democrats and Republicans. As many scholars have noted, members of the public are becoming increasingly sorted based on their political affiliation (Green, Palmquist, & Schickler, 2002).

According to Mason (2015), this sorting is not only emotional, but it is also social. Partisans demonstrate strong in-group bias, favoring their party over the opposing party. This preference has been shown to be implicit (Hawkins & Nosek, 2012), signaling the bias is in-grained as part

of their self-concept. This bias leads to negative evaluations of opposing party members regardless of how they are presented (Mason, 2015). If affective polarization is high, participants may believe that their party should not compromise with the opposing party, as this is similar to negotiating with the enemy. If partisans compromise, they may be sacrificing their values effectively cooperating with the other side. Meanwhile, if partisans are arguing, refusing to cooperate with the other side, they are standing up for their beliefs and refusing to sacrifice their perspective for the other side.

An additional concern which I did not account for during analysis was party strength. If individuals indicate a strong party identification, they are unlikely to alter their identity. Instead, they may be more likely to engage in direct social competition or social creativity, bolstering their group over the opposing group. Strong identifiers will not move to another group as their party is an integral part of their self-concept. When a value is integral to one’s self-concept, social identity theory suggests these individuals are unlikely to cross boundaries to another group with a different value (Tajfel & Turner, 1986). Similarly, individuals with strong opinions will not be silenced when they care deeply about it (Matthes & Hayes, 2014). These individuals will not alter their image or go silent since they have the rest of their party to turn to in case they feel socially threatened. For strong identifiers, impression management and willingness to self-censor would not occur. Weak identifiers may not actively express their identity since it is not as integral to their self-concept. The data for party strength was collected but did not directly pertain the hypotheses of interest. Unfortunately, by failing to account for this variable, I believe all the hypotheses in the study were rejected. I speculate that if this variable was included, some of the results may have supported the hypotheses discussed in this study. Moving forward, party strength will be an important variable to include in future studies.

The support of only one of the first two hypotheses was very unexpected. H1 received support, while H2 was rejected. I believe this may be explained in party by the publicness of an icon versus a label. On most social media sites, the first thing users can view of other users is their names and profile pictures. If participants perceived the icon to possess greater weight due to its publicness and temporal precedence over other profile information, they may have opted to select a different profile icon compared to their party affiliation. For partisan labels, users must delve into someone’s profile further and can be perceived as less public than a profile icon.

Unfortunately, I did not ask participants about the perceived publicness of each component of the profile. As Hayes (2007) suggests, if an action is perceived as more public, this may lead participants to react differently than if it occurred in private.

A final reason limited support was obtained could be a factor of the present political environment. This study occurred during the 2020 Presidential primary elections. It is possible that one’s partisan identity is very salient to participants during this time more than if this study would be conducted during a mid-term election or an off-year election. With these partisan identities primed, they may express stronger attachments to their partisanship. Additionally, if an individuals is constantly concerned with their partisan affiliation and how others may evaluate them, they may identify as Independents from the start.

H3, H3a, & H3b: Self-monitoring and Independence

The only significant result for the effect of self-monitoring on the selection of non- partisan/Independent options was for icon change using the MTurk sample. The correlation suggests that when a participant possesses a high level of self-monitoring, they are more likely to select the non-partisan/Independent icon. This result is in line with Klar and Krupnikov’s (2016) sticker experiment result which found that high self-monitors chose the bald eagle sticker over

partisan logo stickers. What is interesting is the lack of support for the college sample. I expected the college sample to provide stronger results due to the tendency of college students to self- monitor more than older adults (Taylor, 2015). While college students did in fact possess a higher average self-monitoring score than the MTurk sample, the correlation was highly insignificant for icon selection and self-monitoring. The most likely explanation for this result, also indicated by the lack of support for H1 and H2 by the college sample, is that party identification is not an image which the individuals in the sample self-monitor. This identification may be static for them, but this does not explain why the MTurk sample yielded a significant correlation.

I believe the differences may explain this significant correlation for MTurkers compared to college students. The MTurk sample possessed more partisans overall to analyze (N = 424) compared to college students (N = 336), this may have assisted in parsing out individual differences across conditions. The sample was also more diverse, representing non-white participants to a greater extent than the college sample (see Table 9). For the college sample, only one black Republican was represented in the sample. The MTurk sample possesses more black Republicans than black Democrats, overrepresenting this population, but may be significant on the results. Overall, black participants (M = 2.98, SD = 0.57) possessed significantly higher self-monitoring scores than white participants (M = 2.51, SD = 0.67), t =

7.23, df = 156.09, p <.001. Black Republicans were highest in self-monitoring (M = 3.12, SD =

0.42). Black Democrats were not as high with a mean self-monitoring score just below the mid- point of the scale (M = 2.92, SD = 0.56). Black participants also changed their icon more often

(M = 0.74, SD = 0.44) on average than white participants (M = 0.51, SD = 0.50), t = 3.94, df =

133.14, p < .001. Icon change between black participants of either party was not significant, t = -

Table 9: Demographics of Each Sample, Examining Race College Sample MTurk White Black White Black N = 524 N = 58 N = 456 N = 101 Age (M, SD) 20.53 (2.37) 20.13 (1.72) 38.12 (11.46) 32.73 (7.76) Male (N) 159 23 273 71 Female (N) 363 35 181 30 Democrat (N) 159 36 198 41 Republican (N) 113 1 108 46 Icon Change (M, 0.26 (0.44) 0.17 (0.38) 0.51 (0.50) 0.74 (0.44) SD) Label Change (M, 0.09 (0.28) 0.06 (0.23) 0.11 (0.32) 0.15 (0.36) SD) Self-Monitoring 2.90 (0.54) 2.97 (0.54) 2.51 (0.66) 2.98 (0.57) (M, SD) WTSC (M, SD) 2.81 (0.70) 2.47 (0.71) 2.77 (0.79) 2.84 (0.74)

0.51, df = 38.31, p = 0.61. Since black participants possessed higher self-monitoring scores and were more likely to change their icon than white participants (see Table 9), I believe these differences between the sample led to the one significant finding for H3.

No significant correlation was derived from either sample. Even though black participants in the MTurk sample were higher in self-monitoring than white participants, they (M

= 0.15, SD = 0.36) did not significantly change their label more than white participants did (M =

0.11, SD = 0.32), t = 0.91, df = 122.71, p = 0.36. This result demonstrates that the nature of the sample likely led to the significant finding, thus H3 may not otherwise have been supported at all.

Neither H3a nor H3b was supported using either sample. These results demonstrate that self-monitoring does not moderate the relationship between exposure to negative evaluations of partisans and the selection of non-partisan/Independent options on a profile. The lack of significant findings for these hypotheses demonstrates that individuals do not self-monitor their

partisan identity. Instead, they maintain their party affiliation from their initial, private selection.

This may also have been influenced by party strength as discussed for H1 and H2. Stronger partisan identifiers are unlikely to monitor their partisan image around others, thus future studies which examine self-monitoring and partisanship should consider this variable.

H4, H4a, & H4b: WTSC and Independence

No significant results were fund for the hypotheses considering the relationship between partisan valence, willingness to self-censor, and the expression of a non-partisan/Independent options on a discussion site profile. WTSC scores did not differ between the samples, unlike self- monitoring. The similarity between these samples likely assisted in achieving similar results for both. Overall, these results indicate that WTSC does not play a role in the expression of a partisan identity. This may be indicative of Mutz & Silver’s (2014) understanding of the spiral of silence in the Web 2.0 world. They argued that it is possible that the spiral of silence may not occur in the present media environment as individuals can find others that support their view within the media or online. Additionally, the spiral of silence may not occur in this study for two reasons, the threat of isolation was not great enough, and no majority opinion was perceived.

One of the criticisms of spiral of silence and WTSC research is the use of hypothetical situations to test the mechanisms underlying these concepts. In this study, I commit this taboo by asking participants to imagine a hypothetical discussion site provided by their university or

Twitter. I believed these organizations could believably create such a site, but participants still read a prompt emphasizing that the discussion site was imaginary. Glynn, Hayes, and Shanahan

(1997) caution scholars who test the spiral of silence and similar mechanisms from studying them without real social risk. By utilizing a hypothetical situation, participants are unlikely to have the same psychological reactions to the “public” situation as they would in a real public

situation. I believe this may be one of the additional reasons why I found no support for my hypotheses which incorporate WTSC. The lack of a relationship may be due to the lack of real social risk, and thus I should have heeded the warning concerning hypothetical situations.

The spiral of silence can only occur when a majority opinion is perceived and the minority opinion becomes silenced as a result of a fear of isolation (Noelle-Neuman, 1993). It is possible that a majority and minority dynamic was not established, and thus no social risks were perceived by the participants. According to Pew Research Center (2019), the American public is split into roughly thirds based on political affiliation. Thirty-eight percent of the public identify as Independents, while 31% and 26% identify as Democrats and Republicans, respectively.

Whether a partisan identifies as a Democrat or a Republican, they have roughly a third of the public to turn to. If participants are knowledgeable of this breakdown, they may be aware that no true majority exists. Although this is the case, the spiral of silence builds on a quasi-statistical sense which reflects the individual’s perception of majority, not objective statistics like those provided by Pew Research (2019). This study may not have been effective at creating the perception of a majority opinion, likely due the participant’s real-life experiences. Participants have likely formed their political identity by interacting with others. They are aware that both

Democrats and Republicans are established groups in society. Since elections flip between the two major parties on a semi-regular basis, participants are likely aware that neither political opinion is a true majority.

The media does not act as originally thought of in this study. News media does not overly favor one party over the other either (Budak, Goel, & Rao, 2016). Thus, Noelle-Neuman’s

(1974) assertion that the media provides consistent and cumulative messages asserting the majority does not occur in this case. In this study, I attempted to provide one message which

portrayed a negative opinion of partisanship as the majority opinion, but this is not consistent with existing media coverage, nor do I provide more media to enforce this perspective.

Lastly, as with the other hypotheses, party identification strength may have greatly influenced the results for H4, H4a, and H4b. Depending on party strength, these individuals likely vary in how concerned they are about isolation from others. Noelle-Neuman would call these strong partisans as hardcores, sticking to a minority opinion in the face of the majority opinion and social risks associated. Without considering party strength, I cannot discuss the presence of hardcores within my sample and WTSC’s effects considering party strength.

General Discussion

The main question underlying this study was, “will partisans alter their political identity?” The results from this study support the notion that partisans are willing to identify as their private partisan identity on an imaginary political discussion site even in the presence of partisan conflict. While these results run counter to those hypothesized, they build on the literature examining partisan identity through social identity theory, self-monitoring, and willingness to self-censor. (See Table 10 for a list of hypotheses.)

Only one result supported the initial hypothesis stating that when exposed to partisan conflict, participants will select a non-partisan icon compared to those exposed to bipartisan compromise. A significant result only occurred in the MTurk sample, while the college sample yielded insignificant results. This would suggest that something about a profile icon elicits a different response than a partisan label and warrants further consideration.

Extending the Literature

According to social identity theory, individuals will attempt to possess a positive social identity by belonging to a positively evaluated group. When their group is no longer positively evaluated, individuals will identify with a different group or engage in strategies to bolster their group back to a positive position. This could include changing the evaluation characteristics or altering the comparative reference group. While I believed partisans would alter their partisan identity when partisans were portrayed negatively, I did not find support for this notion. Instead, partisans may engage in another strategy such as social creativity or altering the reference group, leading them to continue to identify with their partisan group. This result is in line with research suggesting partisanship is a static identity, unchanging from private to public contexts.

Table 10: Hypotheses by Sample College Students MTurk H1: Icon Change Rejected Supported H2: Label Change Rejected Rejected H3: Self-Monitoring & Independent Options Rejected Partially Supported H3a: Moderation for Icon Change Rejected Rejected H3b: Moderation for Label Change Rejected Rejected H4: WTSC & Independent Options Rejected Rejected H4a: Moderation for Icon Change Rejected Rejected H4b: Moderation for Label Change Rejected Rejected

Impression management literature would also suggest that individuals desire to portray the best version of themselves to others. While self-monitoring did correlate with icon selection in the MTurk sample, a significant relationship was not found between self-monitoring and label selection in either sample. This suggests that one’s partisan label is either not a characteristic which individuals self-monitor, or they believe that their partisan label is necessary to their impression on others, and thus they will not alter it. Partisans may see their label as the best way to make an impression on others, so they do not engage in impression management strategies related to this characteristics.

Similarly, willingness to self-censor derives from a fear of being isolated from others. If partisanship is perceived as negative by the surrounding audience, I believed partisans would engage in self-censoring strategies including expressing neutrality or ambivalence. By expressing a neutral perspective, I hypothesized partisans who are confronted with negative portrayals of partisanship would select non-partisan options, which would be the most neutral position. Although this relationship was hypothesized, I found no support for this conclusion.

WTSC and the expression of an Independent identity were not correlated, nor were individuals higher in WTSC more likely to censor their partisan identity in favor of a non-partisan one.

These results suggest that the mechanism underlying WTSC does not play a role in this relationship, identifying a case in which WTSC may not extend and the spiral of silence may not occur.

Limitations

One limitation of this study is the strength of the manipulation. Klar and Krupnikov

(2016) utilized the same passages in their study of political identity, finding that these passages adequately primed negative or positive partisan evaluations. In this study, a quarter of participants in both samples did not evaluate the passages as they were intended. This may be indicative that these passages may not be as strong as Klar and Krupnikov suggest or are not as effective as they once were in 2012. This manipulation may also seem to counter expectations of partisanship, leading participants to question the manipulation. News media often portray the two parties in conflict, emphasizing conflict over cooperation (Bennett, 2016). This would mean that the bipartisan compromise condition would come as a shock, while the partisan conflict condition may not be strong enough to elicit a response when participants are already desensitized to this kind of content.

Although I attempted to keep both surveys as similar as possible, differences were bound to occur between surveys. First, a college sample is not representative of the general population.

I supplemented this sample with an MTurk sample, but even then MTurk also possesses its issues. The MTurk sample included more black participants than the college sample. These black participants changed their icon more often than whites and possessed a significantly higher self- monitoring scores. Both samples are also acquainted with taking online surveys due to the nature of the recruitment process. This may have led participants to react differently than others who do not commonly encounter surveys. One methodological difference occurred between the two

surveys. The MTurk survey required participants to provide their partisan affiliation prior to treatment, possibly priming participants or leading to consistency bias. I do not believe this was a problem as I found significant results in the MTurk sample over the college sample where I had access to screening answers. If MTurk participants wished to maintain consistent with their answers prior to treatment, I should have found no change in party identification for either icon or label. Since I found similar results to the college sample for the most part, I do not believe the nature of the survey altered responses for the MTurk Sample.

This study also possesses several measurement limitations. For both surveys, the self- monitoring and WTSC measures were not randomized. Although previous studies have not noted an effect of placing one measure prior to the other, participants may be less likely to complete the WTSC properly when placed after self-monitoring measures. Future studies should examine the interaction between these measures in other contexts. This study also failed to grapple with participants’ perceptions of the passages and profile creation. It is assumed in this study that participants would see partisans as either positive or negative, likewise, they would also interpret the profile to be public and have high social risks. These assumptions come into question as few of my hypotheses were supported. Future studies would benefit from initial qualitative tests of the treatment and profile creation task to examine if participants perceive these components as the researcher interprets them.

Perhaps the largest limitation of this study is its failure to consider party strength in relation to each of the variables of interest. Strong party identifiers are unlikely to engage in any of the mechanisms hypothesized to occur when partisans are framed negatively. These individuals, regardless of condition, are unlikely to alter their identity due to the strength of their identification. Social identity theory suggests that these individuals will not attempt to change

their identity, instead they will directly compete with or alter the comparison between the opposing party. Additionally, the inclusion of party strength would acknowledge Noelle-

Neuman’s (1993) concept of hardcores in society and a group of individuals which do not fear isolation from society. By failing to include this variable, I have ignored a possibly influential variable in the understanding of party identity expression.

Future Directions

In the future, the mechanisms in this study should be investigated alongside party identification strength. The strength of identification was measured for each participant’s initial party selection; however, this variable was not considered in this study. Moving forward, party identification strength should be included when considering the expression of a partisan identity.

The failure to consider this variable when attempting to understand social identity, impression management, and willingness to self-censor is a misstep in this study which can be rectified by returning to this data in the future.

Two other components in the profile stage collected data which warrant future research.

The policy statements in the profile did more than to assess which position is favored by each participant. Each participant’s rating for these statements can be summed to create an ideology or party strength measure. Future studies can examine issue position and issue position strength in relation to the variables in this study. Those that are higher in self-monitoring and WTSC may be more likely to express more moderate or neutral positions than those low in either scale.

Participants also ranked potential discussion partners. According to social identity theory, individuals will favor individuals who are in the same group as them (Tajfel & Turner, 1986). If this is the case, partisans should preference other users who identify as the same party as their initial political identification response, regardless of that user’s policy position. Morey,

Kleinman, and Boukes (2018) conducted a very similar study, finding this to be the case. Future studies can examine the data from this study to see if Morey, Kleinman, and Boukes’ results replicate as well as the effects of self-monitoring, WTSC, and valence of partisans on the selection of discussion partners.

Conclusion

75% of Americans believe there to be possible violent results from political rhetoric

(Drake & Kiley, 2019). Despite the social risks, this study indicates that partisans are willing to maintain their partisan identities, even if they may be assigned negative characteristics. One’s partisan identity may not be subject to change because they read one article portraying both sides of a political conflict negatively. Although the results of this study do not provide much support for my hypotheses, I was able to identify potential boundary conditions and instances which are not subject to the theoretical mechanisms I proposed. The limitations of this study certainly play a large role in the results, some of which can be easily corrected in future studies.

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Appendix A: Pre-Treatment Survey How old are you? (18 – 108; Drop-down list) What gender do you identify with? • Male • Female • Gender-fluid • Agender • Other • Prefer Not to Say What is your race? • American Indian/ Alaska Native • Asian • Black or African American • Pacific Islander • White • Prefer Not to Say Are you Hispanic or Latino? • Yes • No • Prefer Not to Say Are you Registered to Vote? (Y/N) ______For MTurk Participants Only: What political party do you belong to? • Democrat • Republican • Independent • Other [open dialogue box] If participant answered “Independent” or “Other”: Which party do you consider yourself closer to? • Republican Party • Democratic Party • Neither

How strong is your political affiliation? • Very Strong • Strong • Moderate • Weak • Very Weak ______Self-Monitoring Scale:

Based on Snyder & Gangestad (1986). The original scale was T/F, but Berinsky & Lavine (2012) indicate a 5-point scale is a stronger measure.

All statements are on a 5-point scale. 1 = Never; 2 = Once in a while; 3 = Half of the Time; 4=

Most of the Time; 5 = Always

1. When I am with other people, I put on a show to entertain or impress them. 2. I am a good actor. 3. When I am with other people, I am the center of attention. 4. I have never been good at game like charades or improvisational acting. (reversed) 5. In different situations and with different people, I act like very different persons 6. I am not particularly good at making other people like me. (reversed)

______

WTSC Scale:

Developed by Hayes, Glynn, & Shanahan (2005).

1. It is difficult for me to express my opinion if I think others won’t agree with what I say. 2. There have been many times when I have thought others around me were wrong but I didn’t let them know. 3. When I disagree with others, I’d rather go along with them than argue about it. 4. It is easy for me to express my opinion around others who I think will disagree with me.

5. I’d feel uncomfortable if someone asked my opinion and I knew that they wouldn’t agree with me. 6. I tend to speak my opinion only around friends or other people I trust. 7. It is safer to keep quiet than to publicly speak an opinion that you know most others don’t share. 8. If I disagree with others, I have no problem letting them know.

Appendix B: Treatment Passages NON-POLITICAL: (adapted from Klar & Krupnikov; added material)

Every February, Americans wait for Groundhog Phil in the little town of Punxsutawney, PA. The tradition originates from Pennsylvania Dutch superstitions. According to folklore, Phil’s sighting of his own shadow means there will be 6 more weeks of winter. If Phil does not see his shadow, it means “there will be an early spring.” The official website of Punxsutawney Phil, perhaps not impartial, claims the Groundhog has issued a correct forecast 100% of the time. Celebrations started as a small gatherings in the 1880’s. Today, over 40,000 people gather to see Groundhog Phil. In other German-speaking areas, a badger may also predict the weather.

PARTISAN UNITY: (Klar & Krupnikov; taken from compromise study)

Recently, Democrats and Republicans in Washington voted on an important economic bill that was crucial for the government to continue to function. Prior to the vote, the party leaders stated clearly that Democrats and Republicans had vastly different goals and priorities for this bill. Democrats and Republicans debated and voiced their disagreements on the Congressional floor. Legislators listened to each other with open minds and calmly discussed every sentence in the bill. The level of courtesy suggested there could be room for compromise between the parties. Ultimately, the two parties did manage to reach a compromise and pass the bill.

PARTISAN DISAGREEMENT: (Klar & Krupnikov; taken from compromise study)

Recently, Democrats and Republicans in Washington voted on an important economic bill that was crucial for the government to continue to function. Prior to the vote, the party leaders stated clearly that Democrats and Republicans had vastly different goals and priorities for this bill. Democrats and Republicans debated and voiced their disagreements on the Congressional floor. Legislators lobbed insults at each other and clashed head-on over nearly every sentence of the bill. The level of conflict suggested that there could be no compromise between the parties. Ultimately, the two parties did not manage to reach a compromise and did not pass the bill.

Appendix C: Profile Creation PROMPT: “Now, we would like you to imagine that your university (Twitter) is designing a

site for students (users) to discuss current events. On this site, you will be

matched with other students to talk to. In order for students have the best

discussions, you will be asked to create a profile and rate other students' profiles.

Once your profile is created and you have rated other profiles, you will be

matched with other students to discuss current events.”

Select a profile icon a.

Select a political party a. Democrat b. Independent c. Republican

Please rate on a scale of 1 (strongly disagree) to 5 (strongly agree) how much you agree with each of the following statements. 1. It is the government’s responsibility to ensure all have health care. 2. America needs stronger border security to prevent illegal immigration along the Mexican border. 3. Abortion should be legal in all/most states. 4. Gays and Lesbians should be allowed to marry legally. 5. Our economic system unfairly favors powered interests. 6. I approve of the job the President has done.

Select a favorite genre of movie (according to IMDb) a. Comedy b. Sci-Fi c. Horror d. Romance e. Action f. Thriller g. Drama h. Mystery i. Crime j. Animation k. Adventure l. Fantasy m. Superhero Select a favorite genre of music (according to Billboard) a. Pop b. R&B/Hip-Hop c. Country d. Christian/Gospel e. Dance/Electronic f. International g. Latin h. Rock i. Blues j. Classical k. Indie l. Jazz m. Other

Write a tagline for your profile. (open-ended)

Appendix D: Selecting Discussion Partners

Determining Issue Positions Based on the political statements earlier, two issue positions are derived 1. It is the government’s responsibility to ensure all have health care. Individualized, Private Healthcare Healthcare for All 2. America needs stronger border security to prevent illegal immigration along the Mexican border. Increased Border Security Open Borders 3. Abortion should be legal in all/most states. Pro-Choice Pro-Life 4. Gays and Lesbians should be allowed to marry legally. Pro-LGBTQ+ Marriage Anti-LGBTQ+ Marriage 5. Our economic system unfairly favors powered interests. Raising taxes on the Wealthy Lowering Taxes on Everyone 6. I approve of the job the President has done. Approves of President Trump Disapproves of President Trump

Example Set: Please rank the following profiles you would most like to talk to (1) to least like to talk to (4): 1. Democrat who believes in Raising Taxes on the Wealthy 2. Democrat who believes in Lowering Taxes on Everyone 3. Republican who believes in Raising Taxes on the Wealthy 4. Republican who believes in Lowering Taxes on Everyone