Can Deactivating Facebook Reduce Affective Polarization? Experimental Evidence and Heterogenous Effects Based on Partisan Identification Strength

Can Deactivating Facebook Reduce Affective
Polarization? Experimental Evidence and Heterogenous
 Effects Based on Partisan Identification Strength
 By David Shen

Acknowledgements

 First, I would like to thank my thesis advisor, Dr. Robert Y. Shapiro. From the very early

stages of this project, Dr. Shapiro guided me in the right direction with my research. I am also very

grateful to Dr. John D. Huber and to Beatrice Bonini for leading the Honors Seminar. They each

met with me numerous times to answer what was always an extensive list of questions.

Additionally, I would like to thank Wei Yin, whom I met at the Lehman Library office hours.

Without Wei’s computer science assistance, I would have been unable to reconstruct the data set

used in this paper. Finally, thank you to my brother, Philip, and to my parents for their

encouragement and support throughout the year.

Contents
Abstract ........................................................................................................................................... 1
Introduction ..................................................................................................................................... 1
Background ..................................................................................................................................... 4
Literature Review – Part I ............................................................................................................... 6
Argument – Part I.......................................................................................................................... 10
 Affective Polarization as Pure Out-Party Negativity ............................................................................... 10
 Facebook Deactivation and Out-Party Negativity................................................................................... 13
 Initial Summary of Results ...................................................................................................................... 17
Literature Review – Part II ........................................................................................................... 17
Argument – Part II ........................................................................................................................ 21
 Elastic Affect Model ................................................................................................................................ 22
 Sticky Affect Model ................................................................................................................................. 24
Data ............................................................................................................................................... 27
Variables ........................................................................................................................................ 29
 Independent Variable ............................................................................................................................. 29
 Dependent Variables .............................................................................................................................. 30
 Control Variables/Moderators ................................................................................................................ 31
Descriptive Statistics ..................................................................................................................... 33
 Summary Statistics 1: Independent Variables ........................................................................................ 33
 Tabulation 1: Independent Variable ....................................................................................................... 33
 Summary Statistics 2: Dependent Variables ........................................................................................... 33
 Tabulation 2: Dependent Variables ........................................................................................................ 33
Method – Part I ............................................................................................................................. 34
Results – Part I .............................................................................................................................. 36
 Table 1: Effect of Deactivation on the Difference Between Party Feeling Thermometers .................... 36
 Table 2: Effect of Deactivation on the In-Party Feeling Thermometer................................................... 38
 Table 3: Effect of Deactivation on the Out-Party Feeling Thermometer ................................................ 40
 Table 4: Effect of Deactivation on the Likelihood of Negative Partisanship........................................... 43
Method – Part II ............................................................................................................................ 46
Results – Part II............................................................................................................................. 48
 Table 5: Effect of Deactivation on the Out-Party Feeling Thermometer, Conditional on Partisan
 Identification ........................................................................................................................................... 48

Figure 1: Marginal Effects by Partisan Identification .............................................................................. 51
 Table 6: Effect of Deactivation on the Out-Party Feeling Thermometer, Conditional on Complete
 Partisan Identification ............................................................................................................................. 52
 Figure 2: Marginal Effects by Complete Partisan Identification ............................................................. 54
 Table 7: Effect of Deactivation on the Out-Party Feeling Thermometer, Conditional on Baseline Issue
 Alignment Level ...................................................................................................................................... 55
 Figure 3: Marginal Effects by Baseline Issue Alignment ......................................................................... 57
 Table 8: Effect of Deactivation on the Out-Party Feeling Thermometer, Conditional on Partisan
 Identification and Baseline Issue Alignment Level ................................................................................. 58
 Figure 4: Marginal Effects by Baseline Issue Alignment, Controlling for Partisan Identification
 Interaction, Calculated for Independent Leaners ................................................................................... 61
 Figure 5: Marginal Effects by Baseline Issue Alignment, Controlling for Partisan Identification
 Interaction, Calculated for Weak Partisans ............................................................................................ 61
 Figure 6: Marginal Effects by Baseline Issue Alignment, Controlling for Partisan Identification
 Interaction, Calculated for Strong Partisans ........................................................................................... 62
Discussion ..................................................................................................................................... 63
Limitations and Suggestions for Future Research ........................................................................ 67
 Theoretical Limitations ........................................................................................................................... 67
 Data Set Validity ...................................................................................................................................... 69
 Variable Operationalization Issues ......................................................................................................... 71
Conclusion .................................................................................................................................... 72
References ..................................................................................................................................... 75
Appendix ....................................................................................................................................... 81

Abstract

 Affective polarization describes the increasing amount of hostility that U.S.
 partisans feel toward the opposing political party, whether they are Democrats or
 Republicans. Importantly, rising levels of affective polarization have many adverse
 social and political consequences. This study tests whether deactivating one’s
 Facebook account can reduce levels of affective polarization. Unlike previous
 studies, I decompose affective polarization into its component parts: in-party
 positivity and out-party negativity. I find that deactivating Facebook reduces
 negative evaluations of the opposing political party, but has no effect on evaluations
 of one’s own party. This reduction in negativity as a result of account deactivation
 is quite large for independents who lean toward either major party; however, there
 is no change in affective evaluations of the opposing party for individuals who
 identify either weakly or strongly with either party. Although this study relies on
 Facebook deactivation, rather than usage, as a causal variable, these results
 suggest that Facebook usage is contributing to rising levels of affective polarization.
 More concretely, these results indicate that reductions in Facebook usage could
 ameliorate the issue of rising hostility toward the political opposition.

Introduction

Are social media platforms, like Facebook, exacerbating affective polarization in the American

electorate? Affective polarization, or the extent to which U.S. partisans dislike the opposing

political party, is one of several ways to conceptualize political polarization. Affective polarization

is a prominent research topic because it has numerous harmful political and social consequences

(Druckman and Levy 2022). Furthermore, it has been worsening at an increasing rate, especially

since the turn of the century (Iyengar and Krupenkin 2018). In particular, affective polarization of

the American public has sped up at the same time as the dawn of the internet and social media.

Noticing these parallel trends, scholars have begun to search for a link between increasing internet

usage and rising affective polarization, with mixed results (Boxell, Gentzkow, and Shapiro 2017;

Lelkes et al. 2017).

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Over the past decade, research into the relationship between individual social media

platforms and political polarization has proliferated. Yet, relatively little research on social media

and political polarization has focused on affective polarization, as opposed to various policy-based

definitions of political polarization. Furthermore, far more research has been conducted on Twitter

than Facebook, largely due to difficulties obtaining data on Facebook usage (Kubin and von

Sikorski 2021). This is despite the fact that 69% of all U.S. adults use Facebook, compared to 23%

for Twitter (Social Media Fact Sheet 2021), and 31% of U.S. adults regularly obtain news through

Facebook, compared to 14% for Twitter (Social Media and News Fact Sheet 2022). Altogether, the

relationship between Facebook and affective polarization is worth studying because affective

polarization has many adverse consequences, Facebook has an enormous userbase, and researchers

still do not understand the relationship between Facebook and affective polarization.

 In order to address gaps in scholars’ understanding of the link between Facebook and

affective polarization, I analyze existing data using different methodologies; in doing so, I

demonstrate that leaving Facebook can reduce feelings of negativity toward the opposing political

party. Specifically, I rely on data produced by Allcott et al. (2020) for their experiment on the

effects of deactivating Facebook. In this experiment, a randomly selected treatment group

deactivated their Facebook accounts for four weeks preceding the 2018 midterm elections. Allcott

et al. (2020) measured levels of affective polarization as the gap between ratings of one’s own

party (the in-party) and the opposing party (the out-party), and they found no relationship between

Facebook deactivation and affective polarization. However, in this experiment I test for causal

effects of Facebook on in-party ratings and out-party ratings separately. I find that participants in

the experiment who deactivated Facebook rated the out-party more highly than those who did not

deactivate Facebook, but there was no difference in ratings of the in-party. Thus, reducing

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Facebook usage may be an effective method of reducing hostility toward the opposing political

party. The result that Facebook deactivation reduces negativity toward the out-party also implies,

albeit indirectly, that Facebook usage heightens negativity toward the out-party.

 In a second round of tests, I check for heterogeneity in the Facebook deactivation effect

based on both partisan identification and policy agreement (issue alignment). Does Facebook

deactivation have a greater positive effect on out-party evaluations for more strongly identified

partisans? What about partisans who are in greater agreement with their party’s policy positions?

I find that there is no difference in the effect of deactivation on the basis of issue alignment.

However, I find substantial heterogeneity in the effect of deactivation on out-party ratings based

on partisan identification strength. In particular, there is no effect for individuals who identify

weakly or strongly with either party, but there is a large, positive effect on out-party ratings for

independents who lean toward either party. This result has important implications: On the one hand,

reductions in Facebook usage may only ameliorate affective polarization for independent leaners,

and not individuals who actively identify with either political party. On the other hand, it may

actually be most important that Facebook deactivation is effective at reducing affective

polarization for independent leaners, in particular. In fact, there are more independents than either

Democrats or Republicans, the proportion of independents is increasing, and independents

increasingly vote for candidates of just one party (Abramowitz and Webster 2016). As a result,

independent leaners are an increasingly substantial proportion of the electorate. Altogether, these

results indicate that reducing Facebook usage may be a legitimate method of diminishing partisan

hostility in the U.S.

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Background

The proposition that we live in an increasingly polarized nation should surprise few people;

however, the task of measuring this polarization and defining its causes is an ongoing scholarly

project. Whether political polarization—defined in terms of policy disagreement—is increasing at

all is a question that has long been debated by scholars (Fiorina, Abrams, and Pope 2008;

Abramowitz and Saunders 2008). Yet, policy disagreement, however it is conceptualized, does not

take into account the emotional side of political polarization, which is arguably more important.

More recently, Iyengar, Sood, and Lelkes (2012) have considered political polarization as an

affective phenomenon, which they call affective polarization. They argue that socially identifying

with a political party induces negative evaluations of the opposing party, because it is the nature

of a social identity to alter views of in- and out-group members. This conceptualization of political

polarization focuses on the hostility that members of the two major political parties feel toward

members and elites of the opposing party.

 No matter how it is measured, affective polarization is a worsening problem. Druckman

and Levendusky (2019) review the major methods of measuring affective polarization, all of which

are based on survey questions: The most common method is to ask respondents to rate the two

parties on a 101-point “feeling thermometer,” with 0 representing the coldest value and 100

representing the warmest value. In other studies, affective polarization has been measured by

asking respondents to assign positive and negative traits to the parties, or by asking them how

much they trust each party. Finally, surveys have asked “social-distance” questions, such as how

respondents would feel about their children marrying a member of the opposing party. Iyengar,

Sood, and Lelkes (2012) have found that feeling thermometer measurements, negative trait

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measurements, and social-distance measurements all reveal measurable increases in affective

polarization over the past several decades.

 Affective polarization is a highly relevant research topic, not only because it is trending

upward, but also because it has harmful social consequences. Iyengar and Westwood (2015) have

found that implicit bias—a concept they treat as related to affective polarization—on the basis of

partisanship exceeds implicit bias on the basis of race. They even demonstrate that this bias can

have real world consequences; for example, partisanship significantly influences the selection of

scholarship recipients, regardless of their qualifications. Partisan bias can be explicit as well.

Martherus et al. (2021) find a significant association between individuals’ levels of affective

polarization and their willingness to dehumanize members of the opposing party. This relationship

exists for members of both parties, and for both subtle forms of dehumanization and for a

willingness to compare members of the other party to primates.

 In addition to the negative social effects noted above, affective polarization also

undermines the proper functioning of American democracy. Kingzette et al. (2021) demonstrate

that when the favored party of affectively polarized partisans (of either party) is in control of the

presidency, these partisans oppose limits on executive power; however, they support the same

constitutional protections when the opposing party is in control of the White House. Additionally,

Abramowitz and Webster (2016) have found that out-party animus has become a stronger predictor

of party loyalty in voting than in-party favoritism, and partisans increasingly dislike the opposing

party more than they like their own. They argue that these forces discourage politicians from

appealing to voters from the opposing party and disincentivize efforts to collaborate with the

opposition in Washington. Given the harmful social and political consequences of rising affective

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polarization, studies investigating methods of reducing affective polarization, such as this one, are

highly necessary.

Literature Review – Part I

The question of whether Facebook causes its users to affectively polarize remains unanswered in

the affective polarization literature. Only three studies have investigated a causal link between

Facebook usage and affective polarization. First, Beam, Hutchens, and Hmielowski (2018) rely on

survey panel data to study the relationship between Facebook usage and affective polarization.

They find that over time Facebook usage is actually associated with slightly lower levels of

affective polarization. However, this study relies on observational, rather than experimental data,

so any causal claims must be approached with caution. Second, Nordbrant (2021) relies on similar

methods, but with Dutch respondents and Dutch political parties. She also finds that using social

media does not elevate levels of affective polarization. Yet, in addition to the issue of using

observational data, the results of this study may not apply to U.S. respondents and U.S. political

parties.

 Most relevant to the question at hand, Allcott et al. (2020) introduce the first, and only,

large-scale study in which Facebook usage is randomized, with the caveat that the randomized

treatment is four weeks of Facebook account deactivation. On the one hand, they find that

Facebook deactivation is not significantly associated with affective polarization. On the other hand,

they do find that deactivation significantly reduces pro-party news exposure, policy polarization,

and their index of overall political polarization. Furthermore, the (negative) affective polarization

coefficient comes close to statistical significance. In this paper, I will rely on data collected by

Allcott et al. (2020), but I will exploit different, more direct, measurements of affective

polarization as well as unexamined heterogeneity in the results.

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Despite the lack of direct evidence for a causal relationship between Facebook usage and

affective polarization, there are numerous reasons to believe that such a relationship might exist.

One important set of theories posit that social media platforms create to information-limiting

environments, which in turn solidify political biases. The “filter bubble” hypothesis states that

web-based companies, like Google and Facebook, have built their products on their ability to

provide personalized information, such as through algorithmic filtering. This tendency is a threat

to healthy democratic discourse because such personalized information tends to confirm, rather

than challenge beliefs (Pariser 2011). The “echo chamber” hypothesis states that the rise of the

internet, and social media in particular, provides the opportunity for individuals to self-segregate

into niche communities based on shared beliefs. This separation is also a threat to democracy

because it hinders dialogue between individuals with opposing beliefs (Sunstein 2017). One theory

relies on algorithms, and the other relies on community membership, but both claim that social

media leads to political fragmentation by presenting individuals primarily with information and

viewpoints that confirm their beliefs and prejudices. Accordingly, numerous studies have tested

how well these theories actually describe social media platforms.

 Studies investigating the nature of news and other political content shared on Facebook

generally indicate that neither the filter bubble, nor echo chamber hypothesis, perfectly describe

Facebook; however, the evidence still suggests that Facebook tends to be an information-limiting

environment. Two studies rely on observational panel data to test for information-limiting effects:

Beam, Hutchens, and Hmielowski (2018) find that using Facebook actually leads to increased

exposure to news from out-party sources. However, they measure news exposure using survey

questions. By contrast, Kitchens, Johnson, and Gray (2020) find that Facebook usage shifts news

exposure toward in-party sources, and they use direct measurements of web activity, which are

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more reliable than survey measures. Other studies rely on directly observed cross-sections of

Facebook content: With regard to the content users are exposed to, Cinelli et al. (2021) observe

that individuals on Facebook are far more likely to see content posted by ideologically similar

users, providing evidence for echo chambers. Yet, when testing for the echo chambers themselves,

Yarchi, Baden, and Kligler-Vilenchik (2021) find that Facebook friend networks are not politically

homogenous, especially when compared to other social media platforms. Similarly, Bakshy,

Messing, and Adamic (2015) find little evidence of echo chambers using comparable methods, and

they also find little evidence that the Facebook news algorithm displays any political bias in its

recommendations. They do observe that individuals are primarily exposed to in-party content, but

this phenomenon occurs largely because individuals choose to engage with in-party posts. Across

the board, there is clear evidence that Facebook users are primarily exposed to content that supports

their party, but it is more likely that this content bias is caused by self-selection than either echo

chambers or filter bubbles.

 Because Facebook usage is associated with exposure to partisan media, it is relevant that a

broad literature demonstrates a link between partisan media and affective polarization. Garrett et

al. (2014) find that greater exposure to in-party news sites or blogs is associated with higher

affective polarization. Levendusky (2013) conducts an experiment in which participants view

video clips from partisan news outlets, and he finds that viewing in-party news clips increases

negative affect toward the opposing party. Additionally, studies have demonstrated that increased

exposure to campaign advertisements, as a result of living in a battleground state, is associated

with higher levels of affective polarization (Iyengar, Sood, and Lelkes 2012; Sood and Iyengar

2016).

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In addition to being biased toward one party, the news and political posts that users see on

Facebook tend to be negative and inflammatory; furthermore, studies have documented that social

media content of this nature induces negative partisan affect. Rathje, Bavel, and van der Linden

(2021) find that both negative affect language and language about the out-party are very strong

predictors of resharing and engagement on Facebook, although the out-party language effect is

greater. Additionally, posts about the out-group tend to be highly negative, and the reactions they

generate usually include anger and mockery. Other studies have noted the impact of negative social

media news and political posts on partisan affect. For example, the negativity of a news story read

on Twitter is associated with increased feelings of anger and disgust (Park 2015). (It would be

reasonable to assume that the same would occur on Facebook.) Another experiment demonstrates

that exposure to tweets which negatively framed 2016 presidential candidates increased

participants’ perceptions of ideological distance from the out-party candidate (Banks et al. 2021).

(Again, a similar effect could reasonably be expected with a Facebook post.) Perceived ideological

distance is highly relevant to affective polarization, because Rogowski and Sutherland (2016) and

Webster and Abramowitz (2017) have linked affective polarization to perceived ideological

distance from out-party candidates.

 The prevalence of false information on Facebook may also drive increases in affective

polarization. Allcott and Gentzkow (2017) observe that extensive “fake news” was shared across

social media platforms during the 2016 election cycle, including Facebook. Building on this

finding, Vosoughi, Roy, and Aral (2018)—in an experiment studying fake news on Twitter—

demonstrate that false news articles inspire feelings of fear and disgust.

 Beyond posts and news articles, interactions between Facebook users are often uncivil,

toxic, and rude, and exposure to such discourse is associated with negative partisan affect. Su et

 9

al. (2018) analyze Facebook comment sections for U.S. news outlets. They find widespread

evidence of incivility and rudeness, especially for conservative and local news channels. Similarly,

Ventura et al. (2021) examined Facebook comments under livestreams of 2020 presidential

election debates, finding that most comments were short, toxic, and disrespectful to the opposing

party. Kim et al. (2021) actually test for such incivility experimentally by asking Facebook users

to comment on news articles. They find that frequent commenters on Facebook use more rude and

toxic language than less frequent commenters, and a sample of actual Facebook users produces

more toxic comments than those produced by a nationally representative sample. As for the effects

of this toxicity, exposure to uncivil comments increases perceived policy polarization of the public,

anger, and attitude certainty, while inciting incivility in subsequent comments (Borah 2014;

Gervais 2015; Hwang, Kim, and Huh 2014).

 Taking into account the current research on Facebook and partisan media more generally,

it is surprising that no direct evidence of a causal link between Facebook and affective polarization

has been discovered. To provide this evidence, I will conduct novel analyses of data collected by

Allcott et al. (2020). Their study tests for the effect of deactivating Facebook on a variety of

outcomes, including affective polarization; however, I will test for the effect of deactivation on a

more direct measure of affective polarization than the one adopted by these researchers. In doing

so, this paper will be the first to demonstrate a strong causal link between any operationalization

of Facebook usage and affective polarization.

Argument – Part I

Affective Polarization as Pure Out-Party Negativity

Unlike Allcott et al. (2020), I will conceptualize affective polarization purely as negative affect

toward the opposing political party, rather than the gap between one’s ratings of each party. Allcott
 10

et al. (2020) adopt the most common measurement of affective polarization: the difference between

101-point feeling thermometers for the two major political parties. However, in this study, affective

polarization will be measured using the out-party feeling thermometer alone. The in-party feeling

thermometer will be considered an element of partisan affect, but not a component of affective

polarization. I measure affective polarization using just the out-party feeling thermometer, not only

because Allcott et al. (2020) have not yet analyzed it as a dependent variable, but also because it

better captures the meaning of affective polarization.

 The out-party feeling thermometer is a more straightforward measure of affective

polarization because it directly quantifies negative sentiment toward the opposing party. Iyengar,

Sood, and Lelkes (2012) define affective polarization as “the extent to which partisans view each

other as a disliked out-group” (406). Since this paper’s publication, the concept of affective

polarization has continued to be discussed primarily in terms of a dislike of the opposing party,

with in-party evaluations considered only as a reference point (Iyengar, Sood, and Lelkes 2019).

From an intuitive standpoint, then, measuring negativity toward the out-party appears to be the

most direct measurement of affective polarization. So, why do Allcott et al. (2020) measure

affective polarization using the difference between party feeling thermometers, rather than just the

out-party feeling thermometer? They are likely following the lead of the seminal paper in the field

of affective polarization: Iyengar, Sood, and Lelkes (2012).

 The difference measure for affective polarization became conventional due to

methodological concerns that do not apply in the case of this study. Despite defining affective

polarization in terms of out-party dislike, Iyengar, Sood, and Lelkes (2012) use the difference

between party feeling thermometers for all regression analyses, even if they separate the feeling

thermometers for other comparisons. They opt for the difference measure at the recommendation

 11

of Winter and Berinsky (1999), who find that party feeling thermometers display validity issues in

regressions due to interpersonal comparability. Specifically, they determine that various factors—

including political beliefs—are correlated with how individuals answer political feeling

thermometer questions, both in terms of their response neutral point and variance from that neutral

point. They note that interpersonal differences in variance, or scaling factor, are most likely

substantive, so the more important issue is incomparable neutral points. The neutral point issue

applies to multivariate regression analyses using observational, cross-sectional data. In such cases,

one might confuse a relationship between how respondents feel toward a political party with how

respondents approach the feeling thermometer question. For example, a Republican and a

Democrat who feel equally positive about their own party might rate their respective party

differently, because the neutral points of their 101-point scales differ. To correct for this

discrepancy, these researchers recommend that regressions be conducted using the difference

between feeling thermometers. Fortunately, this correction is not necessary in this study due to the

randomized nature of the data set.

 Because the Allcott et al. (2020) data includes a randomized treatment as well as controls

for pre-treatment feeling thermometer levels, correcting for the neutral point is not necessary, and

feeling thermometer measures can be analyzed directly. The issue that Winter and Berinsky (1999)

raise is that the effect of certain independent variables on feeling thermometer scores can be

conflated with the effect of that independent variable on how sentiment is reported. However, in

this study, the independent variable is a randomized treatment. There is no difference between the

treatment and control groups other than the treatment itself, so there is no reason for the two groups

to systematically diverge in how they report feeling thermometer levels. Furthermore, all

regressions in this study control for pre-treatment affect levels (including both party feeling

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thermometer ratings in the main specification), so different respondent neutral points are

effectively controlled for anyway. Of course, equal changes in sentiment among different

respondents over the treatment period could correspond to feeling thermometer changes of

different magnitudes; however, as noted previously, Winter and Berinsky (1999) advocate for not

correcting for different scaling factors, because these scaling factors usually reflect substantive

differences in affect.

 It is also noteworthy that studies involving randomized experiments have analyzed the

feeling thermometers separately. Both Lelkes (2021) and Levendusky (2013) apply randomized

treatments and report effects for separated feeling thermometer scores. Notably, neither paper even

controls for pre-treatment affect levels in the relevant regressions. Thus, there appear to be no

methodological concerns with analyzing effects on the in-party and out-party feeling thermometers

separately using two-wave survey data with a randomized independent variable. Having resolved

the issue of measurement, I will delve into the substantively meaningful portion of the argument.

Facebook Deactivation and Out-Party Negativity

At this point, it is necessary to reiterate that this study deals with Facebook deactivation as a

potential method of ameliorating affective polarization. It does not directly address whether using

Facebook causes affective polarization to rise; rather, I determine whether restricting Facebook

usage causes affective polarization to fall. Of course, these two questions are intertwined: if

Facebook deactivation has an effect on affective polarization, then Facebook usage likely has an

effect as well, and vice a versa. The effect of Facebook deactivation should operate in the opposite

direction of Facebook usage, but these effects may not be equal in magnitude. The differences

between Facebook deactivation and Facebook usage as explanatory variables will be addressed

 13

further in the second portion of this study. For now, the important question is whether deactivation

has an effect at all.

 In the literature review, I explain a number of reasons why one might expect to find a

relationship between Facebook usage and affective polarization. These reasons are: Facebook

usage is associated with a partisan bias in political content exposure; partisan media is generally

linked to affective polarization; news and political posts on Facebook tend to be negative, and

sometimes even false; and political interactions between Facebook users are often uncivil. If, for

any of these reasons, using Facebook increases affective polarization, then individuals who

deactivate Facebook would likely experience a decrease in affective polarization. However, my

argument is not simply that Facebook deactivation will decrease affective polarization. I also

predict that that Facebook deactivation will asymmetrically impact in-party and out-party affect.

 Specifically, I argue that Facebook deactivation will decrease negativity toward the out-

party but have no effect on in-party positivity. In a comparison between those who do, and do not,

use Facebook, there are a number of reasons to expect an asymmetry between effects on in- and

out-party evaluations: First, people pay more attention to negative news stories than positive ones

(Soroka, Fournier, and Nir 2019). As a result, news consumption on Facebook should display a

stronger relationship with negative affect than positive affect. Second, political Facebook content

is most popular when it derides the opposing party (Rathje, Van Bavel, and van der Linden 2021).

Thus, users are more likely to see content that is critical of the other party than supportive of their

own. Third, uncivil discourse is common on Facebook, and such interactions are more likely to

stimulate negative reactions than positive ones (Kim et al. 2021; Su et al. 2018; Ventura et al. 2021).

Finally, broader forces all appear to be pushing out-party ratings down, while in-party ratings

remain the same or even drop, as was the case in 2016 (Iyengar and Krupenkin 2018). For all of

 14

these reasons, I expect to observe a relationship between Facebook deactivation and out-party

affect, which I have equated to affective polarization in this study; yet, there is comparatively little

evidence suggesting that Facebook deactivation should be associated with in-party affect.

 An important reason to doubt that Facebook deactivation effects will be confined to out-

party ratings is that Facebook users tend to view political content that supports the viewpoints and

leaders of their own party; therefore, those who use Facebook might be expected to rate the in-

party more highly than those who deactivate Facebook. As mentioned previously, even though

there is mixed evidence for echo chambers and filter bubbles, there is still evidence that Facebook

usage is associated with an in-party bias in political content consumption. However, studies have

shown that in-party media has a greater impact on out-party affect than in-party affect. Sood and

Iyengar (2016) find that exposure to political advertising decreases out-party affect more than it

increases in-party affect. Levendusky (2013) even finds that media from in-party sources decreases

out-party affect, but has no effect on in-party affect. Thus, individuals who are spared the negative

influence of Facebook, should rate the out-party more highly, but may exhibit no difference in in-

party ratings from those who use Facebook.

 At this point, it is important to return to the results of Allcott et al. (2020), because these

findings inform my hypothesis that there is will be no effect of Facebook deactivation on in-party

affect. Allcott et al. (2020) have already demonstrated that there is no effect of deactivation on the

gap between in-party and out-party ratings using the same data set analyzed in this study. Given

that there is no effect of deactivation on the difference between the in-party and out-party feeling

thermometers, it is highly unlikely that out-party ratings should rise significantly while in-party

ratings fall significantly. Instead, the following outcomes are possible: a significant effect on in-

party ratings but not out-party ratings, a significant effect on out-party ratings but not in-party

 15

ratings, a significant effect on neither, or a significant parallel movement of both. For the reasons

outlined above, I have identified a reduction in out-party negativity, but no change in in-party

positivity, as the most likely outcome of Facebook deactivation.

 To assess my hypothesis that Facebook deactivation impacts in-party and out-party affect

asymmetrically, I conduct one additional test involving a concept known as negative partisanship.

Abramowitz and Webster (2018) introduce this concept to quantify the extent to which an

individual dislikes the opposing political party more than they like their own party. The extent of

negative partisanship is calculated as follows:

 NegativePartisanship = [100 − ( − )] − ( − )

 If this measurement takes a value greater than zero, Abramowitz and Webster (2018) call

this individual a negative partisan. They use an indicator variable for negative partisanship to test

the effect of various explanatory variables on the likelihood that an individual is a negative partisan.

Similarly, in this study I will test the effect of Facebook deactivation on the likelihood that

individuals are negative partisans. If it is the case that deactivation reduces out-party negativity

but has no effect on in-party positivity, it should also be the case that deactivation reduces the

likelihood that individuals in the sample are negative partisans.

H1: Facebook deactivation will reduce out-party negativity, but have no effect on in-party

positivity. With regard to the four empirical tests of this hypothesis, I expect that Facebook

deactivation will: A) increase out-party ratings; B) have no effect on in-party ratings; C) have no

effect on the gap between in-party and out-party ratings; and D) reduce the likelihood that

individuals are negative partisans.

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Initial Summary of Results

The results section confirms all predictions contained within H1. Deactivating Facebook reduces

negativity toward the out-party, but does not alter levels of in-party positivity. Specifically,

deactivation induces significantly higher out-party ratings in the treatment group than the control

group, but has no significant effect on either in-party ratings or the difference between party ratings.

Facebook deactivation also leads to a significant and substantial (30.1%) decrease in the likelihood

that individuals are negative partisans. One important caveat of these results (to be examined in

further detail in the discussion section), is that the effect of deactivation on the out-party feeling

thermometer is quite small: only 2.705 points out of 100. (The true magnitude of this effect is

explored further in the results section.)

 Even though Facebook deactivation exerts only a small effect on out-party affect in this

experiment, two questions must be addressed with regard to the magnitude of this difference: 1) Is

the effect small among all participants, or is there heterogeneity in the effect? 2) Is the effect small

because Facebook deactivation only occurred for four weeks; moreover, would the effect have

been greater if the deactivation had lasted longer? The second question hints at the key point that

memory plays a role in how Facebook deactivation impacts partisan affect. The following section

will address the question of heterogeneity, and in doing so, shed light on the issue of memory.

Literature Review – Part II

Seeing that Facebook deactivation does, in fact, decrease negativity toward the out-party, it is

worthwhile to investigate potential heterogeneity in this effect. To identify key moderators, I will

go through the literature on why affective polarization is increasing. Any individual-level causes

of affective polarization should be logical choices as potential moderators of the effect of Facebook

deactivation on this same outcome. The broader affective polarization literature can be broken

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down into 4 main categories of causes. In addition to partisan media (including social media), the

three remaining categories are: partisan social identification (and sorting), elite ideological

polarization, and partisan policy agreement (issue alignment).

 It is well known that socially identifying with a political party increases affective

polarization. Greene (1999) investigates the nature of partisanship as a social identity and how this

identity is related to affective polarization. As one might expect, he finds that greater social

identification with a political party is significantly associated with affective polarization;

furthermore, the standard 7-point scale (from strong Democrat to strong Republican) does a decent

job representing this effect. He also finds that partisan identification is significantly associated

with both in-party and out-party affect. In a similar vein, Groenendyk and Banks (2014)

demonstrate that greater partisan identification is associated with greater anger following exposure

to negative political news. Huddy, Mason, and Aarøe (2015) produce the same result using threats

to the strength of respondents’ parties.

 Building on partisanship as a social identity, one major theory about rising affective

polarization posits that the increasing alignment of partisan identification with other social

identities has increased the power of partisan social identification to induce affective polarization.

Mason (2015) introduces the theory of social sorting: the alignment of one’s partisan identity with

other social identities associated with one’s party increases one’s strength of partisan social

identification; by extension, this heightened identification increases affective polarization. This

first paper deals with the alignment between partisan identity and ideological identity (liberal or

conservative). Later, Mason (2016) expands her theory of social sorting to include other social

identities, such as race, religion, and political movements. As an example, a Republican who

identifies as conservative and Christian is more likely to dislike the Democratic Party than a

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Republican who identifies as moderate and agnostic. In a related study, Mason and Wronski (2018)

demonstrate a significant association between the degree to which an individual is attached to

party-aligned groups—racial, religious, and ideological identities—and the strength of their

partisan identification. This relationship between partisan sorting and affective polarization is

important, because levels of partisan social and ideological sorting have increased significantly

over the past several decades (Mason 2015, 2016). Correspondingly, partisan identification

increasingly predicts partisan affect levels (Iyengar, Sood, and Lelkes 2012; Iyengar and

Krupenkin 2018).

 For several reasons, partisan identification emerges as the first moderator in this study. First,

partisan identification (on a 7-point scale) represents actual social identification with a political

party. Second, partisan identification is strongly associated with affective polarization, and even

affects how individuals emotionally respond to political media. Third, the relationship between

partisan identification and affective polarization is strengthening over time. Due to insufficient

data, partisan sorting will not be investigated as a potential moderator. (See the uneven partisan

breakdown in Table A7 in the appendix.)

 An additional theory about rising affective polarization traces the phenomenon to

increasing ideological polarization between elites in the two major parties. Studies have shown

that party elites have become increasingly ideologically extreme since the 1950’s, and

representatives within both parties have voted more consistently with their party (Hetherington

2009). With the backdrop of this increasing policy polarization, Rogowski and Sutherland (2016)

demonstrate that increased ideological distance between political candidates increases the

difference between affective evaluations of those two candidates. Furthermore, Webster and

Abramowitz (2017) demonstrate that an increase in an individual’s perceived ideological distance

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from the out-party candidate is associated with decreased ratings of that candidate’s party. Because

this cause of affective polarization operates at a national, rather than individual, level, it will not

be considered as a moderator in this study. It is worth mentioning, however, because it provides

context for the following theory.

 Another theory states that the increasing alignment of mass partisans’ policy preferences

with their party’s platform is to blame for increases in affective polarization. Unlike party elites,

typical voters have not diverged in terms of ideological extremity over the past 50 years

(Hetherington 2009). However, increasing proportions of partisans’ policy preferences are at least

in agreement with their typical party platforms (Bougher 2017; Hetherington 2009; Webster and

Abramowitz 2017). Bougher (2017) argues that this increasing issue alignment is to blame for

rising levels of affective polarization. Specifically, she claims that individuals with higher levels

of issue alignment express greater out-party hostility because they disagree to a greater extent with

the opposing party. The results of her study demonstrate a significant relationship between issue

alignment and negative out-party evaluations. Webster and Abramowitz (2017) also demonstrate

that partisans who hold more of their policy views in line with their party demonstrate higher levels

of affective polarization.

 As a result of these findings, I have selected issue alignment as the second moderator of

interest in this study. The reasons are as follows: First, issue alignment is significantly associated

with affective polarization. Second, levels of issue alignment are increasing in the U.S. Third, issue

alignment provides a conceptual contrast with partisan identification: one theory conceives of

partisanship as merely tribal, while the other views partisanship in more of a substantive light. In

fact, comparing the effects of policy and party has been a longstanding debate within the affective

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polarization literature (Dias and Lelkes 2022; Lelkes 2021; Orr and Huber 2020). Thus, measuring

the effects of both moderators will contribute to an existing discourse.

 Having identified partisan identification and issue alignment as potential moderators in

this study, it is worth pointing out that neither is tested as a moderator in Allcott et al. (2020). The

researchers who produced this data set tested a long list of moderators for the effect of Facebook

deactivation on the difference between party ratings, but neither of the moderators identified here

were included in that list.

 Testing partisan identification and issue alignment as potential moderators constitutes the

second major contribution of this study. Partisanship as a social identity and agreement with party-

aligned policies constitute the two most important individual-level causes of affective polarization,

so they are likely to moderate the effect of Facebook deactivation on affective polarization as well.

Furthermore, the trends in these two phenomena within the American electorate increase their

relevance. Levels of issue alignment are rising at the national level, and partisan sorting has made

partisan identification an increasingly strong predictor of partisan affect. Therefore, it is worth

asking how the reduction in out-party negativity brought about by Facebook deactivation varies

based on an individual’s level of partisan identification and issue alignment.

Argument – Part II

In this section, I will offer two alternative hypotheses for how these two potential moderators will

impact the effect of Facebook deactivation on out-party ratings. These two hypotheses diverge on

the basis of how memory is assumed to operate in this study. As alluded to previously, Facebook

deactivation is not the same as Facebook exposure, because any effect of Facebook deactivation

should be mediated by memory. To elaborate, if Facebook exposure does increase out-party

negativity (which, for the sake of this argument, I assume it does, because I have already identified

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a significant effect of Facebook deactivation on out-party ratings), then deactivating Facebook can

only decrease out-party negativity if participants forget about polarizing content that they had

previously been exposed to on Facebook. At the very least, such partisan media must lose salience

in the mind of the participant for partisan affect to adjust.

 The key distinction between the two alternative hypotheses is as follows: The first

hypothesis assumes that four weeks of deactivation was enough time to completely “reset” all

participants’ levels of partisan affect vis-à-vis Facebook; that is, all participants completely forget

what had polarized them on Facebook. The second hypothesis assumes that four weeks of

deactivation was not enough time for all respondents to forget what it was on Facebook that had

affectively polarized them; accordingly, affect levels have not adjusted for those who have not

forgotten any partisan Facebook content. I will refer to the first scenario as the “elastic affect

model,” and the second scenario as the “sticky affect model.” I include both hypotheses because

existing research cannot inform a prediction about how memory will function in a social media

deactivation experiment. Furthermore, the distinction between each hypothesis is important

because it fundamentally alters expectations about moderating effects.

Elastic Affect Model

If I assume that four weeks is enough time to reset participant affect, then a large body of research

on the effects of partisan media on affective polarization can inform a hypothesis about

deactivation effect heterogeneity. In this scenario, participant affect levels return to a hypothetical

baseline that they would exist at in the absence of any exposure to Facebook. If affect is truly this

elastic, then the impact of Facebook deactivation on party ratings is the exact inverse of the effect

of Facebook exposure in terms of magnitude, direction, and most importantly, heterogeneity. For

example, if researchers have found that exposure to social media increases affective polarization

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more for hypothetical group A than hypothetical group B, then I would hypothesize that Facebook

deactivation should decrease affective polarization more for group A than B. This elastic affect

assumption is not entirely implausible given the rapid rate at which the tide of public opinion can

shift during a political campaign, for example.

 According to the elastic affect model, I would expect Facebook deactivation to have a

greater effect on out-party ratings for more strongly identified partisans and for more issue-aligned

individuals, with partisan identification acting as a more substantive moderator than issue

alignment. Huddy, Mason, and Aarøe (2015) conduct an experiment in which respondents read a

political blog post. They find that more strongly identified partisans react more angrily than more

weakly identified partisans when this post includes a threat to their party. They also find that more

issue-polarized (not exactly the same as issue-aligned, but similar) individuals react more angrily

than less issue-polarized individuals, but this difference is smaller than in the partisan identification

comparison. Mason (2016) conducts a similar experiment and produces similar results with respect

to partisan identification and issue polarization. Suhay, Bello-Pardo, and Maurer (2018) conduct

an experiment where the treatment is exposure to critical comments under a political news article,

and they measure the effect of this treatment on party feeling thermometers. Although they do not

test for the moderating impact of any conceptualization of issue alignment, they do find that more

strongly identified partisans experience a greater increase in affective polarization in response to

the treatment. Synthesizing the results of these studies, I could reasonably expect that exposure to

political Facebook content would increase out-party negativity more for more strongly identified

and for more issue-aligned partisans, albeit with a greater discrepancy in the effect based on levels

of partisan identification. The elastic affect model predicts that the impact of Facebook

deactivation would exactly mirror this outcome: deactivating Facebook should decrease out-party

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