# Affective Polarization in Social Networks

Dan Feldman, Ashwin Rao, Zihao He, Kristina Lerman
USC Information Sciences Institute
{dfeldman, ashreyas, zihaohe<EMAIL_ADDRESS>
(2023)

###### Abstract.

Affective polarization has grown dramatically in recent years, with surveys
showing that liberals and conservatives not only disagree on policy issues but
also dislike and distrust each other. While studies have implicated social
media in amplifying polarization, there is a lack of agreement on the
mechanisms driving affective polarization and methods to measure it. Our paper
addresses these gaps. First, we directly measure affective polarization on
social media by quantifying the emotional tone of reply interactions between
users. As predicted by affective polarization, in-group interactions between
same-partisanship users tend to be positive, while out-group interactions
between opposite-partisanship users are characterized by negativity and
toxicity. Second, we show that affective polarization generalizes beyond the
in-group/out-group dichotomy and can be considered a structural property of
social networks. Specifically, we show that emotions vary with network
distance between users, with closer interactions eliciting positive emotions
and more distant interactions leading to anger, disgust, and toxicity. These
findings are consistent across diverse datasets and languages, spanning
discussions on topics such as the Covid-19 pandemic, abortion, and the 2017
French Election. Our research provides new insights into the complex social
dynamics of affective polarization in the digital age and its implications for
political discourse.

Affective Polarization, Social Networks, Social Media Analysis, Twitter,
Polarization, Emotions, Toxicity, Covid-19, Abortion Rights, French Elections,
Pagerank

††copyright: acmcopyright††journalyear: 2023††doi: XXXXXXX.XXXXXXX††ccs:
Information systems Social networking sites††ccs: Information systems Blogs

##  1\. Introduction

Political polarization in the United States has grown in recent decades,
creating a large disagreement between Democrats and Republicans on a range of
social, political, economic, and cultural issues. Beyond disagreeing on
policy, Americans have also grown increasingly less tolerant of opposing
viewpoints, with members of each party disliking and distrusting those
affiliated with the opposing party. This emotional divide, dubbed affective
polarization (iyengar2012affect, 34, 33), has become a strong destabilizing
force in society. Democrats and Republicans both view members of the other
party in a negative light and are increasingly unwilling to cooperate and even
socialize across party lines. Beyond making governance difficult, affective
polarization stokes hostility and even political violence towards out-group
members (kingzette2021affective, 37, 61, 51, 9), and also harms society by
eroding trust, especially trust in experts and institutions. For instance, the
polarized response to the COVID-19 pandemic led individuals affiliated with
one political group to distrust recommendations from government officials if
those recommendations were supported by the opposing political group,
hindering effective response to the pandemic health crisis (green2020elusive,
27, 28, 26).

Research shows that the origins of affective polarization are complex and
multi-faceted, including among many other contributors the news media,
political elites, ideological and demographic factors (iyengar2019origins, 33,
60, 61). Although scholars disagree on the precise degree to which social
media drives partisan animosity compared to offline causes, most see it as an
important amplifier of polarization (tornberg2021modeling, 58). Social media
discourse tends to promote inflammatory language and moral outrage directed at
the out-group (brady2017emotion, 12, 11, 50), which may increase polarization.
Moreover, some researchers believe that echo chambers on social media, which
segregate users within communities of like-minded others, amplify polarization
by exposing users to extreme same-ideology content (cinelli2021echo, 17).
However, others have argued that echo chambers do not insulate people from
opposing viewpoints (bakshy2015exposure, 6), and that exposure to beliefs held
by the opposing party does not reduce, and may indeed worsen, polarization
(bail2018exposure, 4). Additionally, while researchers have attempted to
quantify affective polarization on social media platforms (tyagi2020affective,
59, 62, 41, 50), their methodology measures how people talk about out-group
members, not how they talk to them.

Our paper makes two contributions to the study of affective polarization on
social media. Using data from Twitter pertaining to discussions on diverse
topics and languages, we directly measure affective polarization by
quantifying emotions of interactions between users. Specifically, we classify
users by partisanship (i.e., liberal or conservative) and measure emotions and
toxicity of their replies to others. We show that in-group replies (between
users with the same partisanship) tend to express more positive emotions like
joy. In contrast, out-group replies (between users with opposite partisanship)
tend to express more negative emotions, like anger and disgust, and also use
more toxic language. Such in-group favoritism—out-group animosity is a
hallmark of affective polarization (iyengar2012affect, 34). Further, by
disaggregating users by partisanship, we investigate group differences in
affective polarization between liberals and conservatives.

Figure 1. Affective polarization in networks. People express warmer feelings
when replying to those closer to them in a social network; when replying to
those farther away, they express more negativity.

More importantly, we show that the concept of affective polarization
generalizes beyond the in-group/out-group dichotomy and can be viewed as a
structural property of social networks. We show that emotions of reply
interactions vary with distance between interacting users, as illustrated
schematically in Fig. 1: when replying to people closer to them in a social
network, specifically a retweet network, users express more positive emotions
like joy, but when replying to those who are farther away, people express more
negative emotions like anger, disgust, and toxicity. Interestingly, we find
that fear is one negative emotion that deviates from this pattern. The trends
we measure are consistent across diverse datasets containing Twitter
discussions of the Covid-19 pandemic (chen2020tracking, 15), abortion
(chang2023roeoverturned, 13), and even political discussions during the 2017
French elections (ferrara2017disinformation, 24).

Our paper makes the following contributions:

  * •

We quantify affective polarization in online discussions. We show that
emotions of in-group interactions (replies between same-partisanship users)
are more positive than out-group interactions (between opposite-partisanship
users).

  * •

We demonstrate group differences in affective polarization between liberals
and conservatives.

  * •

We show that affect of reply interactions varies with network distance between
users.

  * •

We identify additional dimensions of polarization and discuss their
significance.

Our study sheds light on the complex mechanisms of affective polarization in
the digital age, offering insights into the emotional foundations of political
discourse on social media platforms and the interaction of emotions with
network structure. Understanding these mechanisms is crucial for addressing
the challenges posed by polarization in the digital age.

##  2\. Related Work

###  2.1. Issue Polarization

In political science, polarization is understood as the divergence of
viewpoints along particular issues or topics. Both political elites and the
American society at large have been divided by “culture wars”
(hunter1992culture, 31), with liberals and conservatives disagreeing on
controversial issues like abortion, women’s rights and LGBTQ+ rights
(druckman2013elite, 22, 19, 23, 1). Political ideology was also found to
explain differences in compliance with health guidelines and mandates
(gollwitzer2020partisan, 26, 28, 27, 53). The average position of a Democratic
voter increasingly diverges from that of a Republican on most issue areas and
ideological opinions are strongly correlated with party identifications
(mccarty2019polarization, 39). While there is a growing body of research that
investigates the extent of polarization and the debate over partisan sorting
vs polarization (fiorina2008political, 25, 1), it remains out-of-scope for
this study.

###  2.2. Affective Polarization

Opinion surveys have shown that the share of Democrats and Republicans who
view the other party as a threat to the nation’s well-being has doubled in the
last two decades (pew2014polar, 46), a phenomenon called affective
polarization (iyengar2012affect, 34, 33). Affective polarization captures the
increased distrust of the out-group and the opinion that other party members
are hypocritical, selfish and close-minded.

The magnitude and impacts of affective polarization are a subject of intense
academic and popular interest (iyengar2012affect, 34, 35, 60, 2, 32, 21, 63).
In (iyengar2015fear, 35, 60) authors find that the degree of out-group
animosity exceeds in-group favoritism. The division of the public into “‘Us
vs. Them” plays a role in the development of partisan group identity as a
social identity. The increased animosity towards the out-group can drive
pernicious polarization where varied ideological positions and cleavages in
the public coalesce into one political cleavage (mccoy2019toward, 40, 58).
Affective polarization (kingzette2021affective, 37, 61) can reduce belief in
democratic norms and the willingness to work together towards mutually
beneficial public good. Affective polarization can also impact governance by
hindering cross-party cooperation.

While there is agreement on the existence of affective polarization,
researchers disagree on its roots. Past research finds that the degree out-
group animosity is inconsistent with disagreements on policy
(iyengar2012affect, 34) and hence, affective polarization is distinct from
ideological polarization. In contrast, (webster2017ideological, 60) found
ideological polarization to be closely related to affective polarization,
while (tornberg2022digital, 57, 58) showed how affective polarization arises
from partisan sorting.

Dataset | #Retweets | #Retweeters | #Replies | #Responders | Total Tweets | Total Users
---|---|---|---|---|---|---
Roe vs. Wade | 33,237,166 | 5,464,932 | 2,483,829 | 958,927 | 35,720,995 | 6,423,859
COVID-19 | 4,960,113 | 2,092,038 | 236,260 | 156,694 | 5,196,373 | 2,248,732
French Elections | 1,202,487 | 212,909 | 167,399 | 60,051 | 1,369,886 | 272,960
Table 1. Number of retweets and replies from the three datasets in our study.
Number of retweeters and the number of responders gives the total number of
users who participated in a retweet and reply interaction respectively.

###  2.3. Affective Polarization and Social Media

As social and political interactions have moved online, the role of social
media in polarization has come under increased scrutiny (tornberg2022digital,
57, 20, 59, 62, 58, 50). Researchers have identified some ways in which social
media platform’s algorithms, content dynamics and the very nature of online
interactions may drive polarization. Online, users link to like-minded others,
surrounding themselves with people holding similar political opinions while
isolating themselves from opposing viewpoints (rao2022partisan, 48, 17). This
phenomenon, known as “echo chambers,” helps intensify polarization by
reinforcing existing beliefs. However, other research has shown that exposure
to opposing viewpoints can also increase polarization (bail2018exposure, 4,
58). Social media platforms also often algorithmically promote content that
generates high levels of engagement, through likes, shares, and comments.
Polarizing content tends to elicit strong emotional reactions, leading to more
engagement (brady2017emotion, 12). In addition, out-group language is a strong
predictor of social media engagement (rathje2021out, 50), and algorithms may
implicitly incentivize expressions of out-group animosity. As a result,
emotionally charged and divisive content, in some cases aided by outright
social manipulation, will tend to get more exposure, amplifying polarization.

Some works have attempted to quantify affective polarization by measuring the
sentiment of online messages towards entities or political figures they
mention (tyagi2020affective, 59, 41, 50).These studies observed higher
negative sentiment in messages talking about out-group members. Unlike these
works, we measure a range of emotions people express directly to each other,
rather than political figures.

##  3\. Methods

###  3.1. Data

####  3.1.1. 2017 French Elections

We use a corpus of 5.3M about the 2017 French presidential election, which
were collected by querying Twitter with a set of keywords related to the
election (ferrara2017disinformation, 24). The corpus also includes tweets
posted by accounts of presidential candidates, their parties or campaigns,
e.g., @MLP_officiel, @EmmanuelMacron, @enmarchefr, @JLMelenchon, and
@jlm_2017. The vast majority (91%) of tweets are in French, 4% were in
English, with the remaining tweets in other languages.

####  3.1.2. Covid-19

We used a public corpus of tweets about the COVID-19 pandemic
(chen2020tracking, 15) posted between January 21, 2020 and April 22, 2020.
These tweets include one or more terms related to the pandemic, such as
“coronavirus”, “pandemic”, and “outbreak”.

####  3.1.3. Roe v Wade

We also use a public corpus of tweets about abortion rights
(chang2023roeoverturned, 13) collected between January 1, 2022 to January 6,
2023. The tweets were collected by querying Twitter with a list of keywords
and hashtags that reflect both sides of the abortion rights debate in US
during the period that Roe v Wade was overturned by the US Supreme Court. This
corpus includes over $76M$ tweets generated by $9.6M$ users.

####  3.1.4. Data Pre-processing

To study affective polarization, we focus on dynamics of interpersonal
interactions on social media. On Twitter, these interactions are largely in
the form of retweets and replies. Each retweet or reply is linked to a parent
tweet, which, in most cases, is an original tweet. We evaluate all retweets
and replies with a parent tweet that is also present in our data and discard
all retweets and replies that do not have the parent tweet in the data.
Additionally, we also disregard original tweets that do not generate any
interactions. This analytical approach is applied consistently across all
three datasets. This leaves us with: $5.1M$ tweets from $2.2M$ users in the
Covid dataset, $35.7M$ tweets from $6.4M$ users in the Roe v Wade dataset,
and, $1.4M$ tweets from $272K$ users in the French elections dataset.
Additional statistics of number of retweets and replies analyzed across the
three datasets are shown in Table 1.

###  3.2. Emotions & Toxicity Detection

To measure emotions in tweets we use an open-source library Demux
(Chochlakis2023, 16), an extension of SpanEmo. This model was shown to
outperform competing methods on the SemEval 2018 Task 1 e-c benchmark
(SemEval2018Task1, 42). Demux assigns none, one or more emotions to input
text, along with a scalar value representing its confidence. Demux can
recognize a range of emotions in multi-lingual text, including anger, disgust,
fear, sadness, joy, love, trust, pessimism and optimism.

For multi-lingual emotion detection, we used a simpler model trained to
recognize clusters of similar emotions often co-occur: “anger, hate, contempt
and disgust”, “guilt, embarrassment, shame, sadness”, “love, admiration”,
“optimism, hope”, “joy, happiness”, “pride”, “fear, pessimism”, “other
positive emotion,” and “other negative emotions.”

To measure toxicity in tweets, we used an open-source classifier
Detoxify.111https://github.com/unitaryai/detoxify The model is trained on the
multilabel toxic comment classification task to recognize toxicity levels of
tweets. The model outputs a score, a scalar value that captures the likelihood
the tweet expresses toxicity, severe toxicity, obscenity, a threat, or an
insult.

###  3.3. Partisanship Classification

Partisanship classification is an active research area. For identifying the
political bias of news articles, Baly et al. (baly2020we, 7) finetune a
pretrained language model and discourages the algorithm from modeling the news
source instead of focusing on detecting bias in the article. He et al.
(he2021detecting, 30) study the polarized topics between bipartisan news
media. For estimating the partisanship of social media users, previous studies
have relied on follower relationships (barbera2015birds, 8), mention and
retweet interactions (conover2011political, 18, 3), and URL sharing
(le2019measuring, 38, 45, 17, 47). Here we use method described in
(rao2021political, 47) to classify the partisanship of individual users. The
model trains a text classifier to classify Twitter users as liberal or
conservative based on the text of the messages they share. Under the hood, the
classifier leverages political bias scores assigned to well over 6K online
sources by Media Bias-Fact Check (MBFC) (mbfc2023politics, 14). Using these
scores, the model creates training data by assigning each user a score that is
a weighted average of the political bias scores of the URLs they share. After
training a text embedding-based method on this data, the model achieves state-
of-the-art performance on recognizing user ideology. The model categorizes
users as liberal or conservative.

In contrast to the two-party political system prevalent in the United States,
the European political landscape falls under a multi-party framework. Recent
studies (morales2023geometry, 43, 5, 44) suggested that a single variable is
not adequate to capture political orientations within a multi-party political
system and instead a minimum of three variables is required. These variables
roughly correspond to the conventional Left-Right cleavage, in addition to
individual stances on immigration and trade openness. Given these
complexities, we leave identifying in-group and out-group divisions in the
French political system to future work.

(a) Anger (b) Disgust (c) Fear (d) Toxicity (e) Joy (f) No emotion (g) Number
of words

Figure 2. In-group and Out-group Affect in the Roe v Wade data. Boxplot of
emotions expressed in replies between users with the same ideology (in-group)
and users with different ideology (out-group) in abortion discussions. Out-
group interactions show more (a) anger, (b) disgust, and use more (d) toxic
language, but also slightly less (c) fear and substantially less (e) joy. Out-
group interactions are less likely to have (f) no emotions and generally have
fewer (g) words. Differences in means were tested for statistical significance
using the Mann-Whitney U Test with the Bonferroni correction: * indicates
significance at $p<0.05$, ** - $p<0.01$, *** - $p<0.001$, **** - $p<0.0001$
and, $ns$ \- not-significant.

###  3.4. Network Construction

Studies of online social networks differ in how they represent edges between
users within the network. Some researchers (cinelli2021echo, 17, 10) use
follower relations to capture the attention users pay to others. However,
collecting follower relationships on Twitter is highly impractical due to the
limitations of the API and other factors. Instead, researchers rely on retweet
links, which can be more easily extracted from the tweets metadata, to
construct the social graph (conover2011political, 18, 36, 52). Following this
practice, we construct a retweet-based social network for each dataset we
study. We view retweets as evidence that both the author of the original tweet
and the author of the retweet have, at least on one occasion were exposed to
the identical informational content. As the result, we model retweet networks
as undirected, unweighted graphs whose nodes represent users and edges
represent the existence of at least one retweet between two users (in either
direction). Under this interpretation, retweet networks provide the structure
over which information disseminates within social networks.

The Roe v Wade network includes 19M retweets made by 4.56M users; the Covid-19
network includes 4.3M retweets by 2.1M users and the French elections data
include 670K retweets from 220K users. Given the large size of our networks,
we used an embedding technique for visualizing large graphs called LargeVis
(tang2016visualizing, 55).222https://github.com/lferry007/LargeVis The
embeddings of the three networks are shown in Figure 5.

####  3.4.1. Measuring Distance in Networks

We use two distinct ways to measure distance in networks. The first one is
shortest-path distance that measures the length of the shortest path between
two nodes. The second measure is based on Personalized PageRank score (PPR)
(haveliwala2002topic, 29). Intuitively, PPR measures the probability that a
random walk from node A reaches node B. We use inverse of PPR as a measure of
distance between two nodes. As an additional robustness check, we measure
Euclidean distance between nodes in 2D embedding space (Fig. 5) generated by
LargeVis model. Results using embedding distance are consistent with the main
two ways we measure distance and are included in the Appendix.

##  4\. Results

###  4.1. In-group vs Out-group Affect

We measure affective polarization by quantifying emotions expressed during
reply interactions between users with a known ideology. We define as in-group
interactions all replies between pairs of users with the same partisanship:
liberal replying to a liberal, or conservative replying to a conservative.
Similarly, we define as out-group interactions all replies between users with
different partisanship: a liberal replying to a conservative or vice versa.
Figure 2 visualizes the distribution of emotions of reply interactions in the
abortion discussions (Roe v Wade dataset). The boxplot shows the mean, median,
75% and the 95% of emotion confidence scores. Anger (Fig. 2a), disgust (Fig.
2b) and toxicity (Fig. 2d) of out-group interactions are substantially higher
than for the in-group interactions, consistent with the idea that out-group
animosity fuels affective polarization. Similarly, in-group interactions show
more joy (Fig. 2e) than out-group interactions, consistent with the idea of
in-group favoritism. All differences are statistically significant.

Our empirical results confirm that affective polarization exists in online
abortion discussions and yield additional insights. We did not find
significant differences in other emotions we measured, like love, sadness or
optimism. We also found that out-group interactions are more emotional (less
likely to have ‘no emotions detected’ Fig. 2f) and shorter. Additionally, we
found that fear does not behave like other negative emotions: in-group
interactions express more fear than out-group interactions (Fig. 2c). This may
be related to the role of fear as an agent of social cohesion. By identifying
a common threat (e.g., in folktales and legends (tangherlini2017toward, 56,
54)), fear creates solidarity, which serves to unite the group.

We observe similar trends in the Covid-19 data (Appendix, Fig. 8). Anger,
disgust and toxicity of out-group interactions are all higher than for the in-
group interactions, and out-group replies are also shorter and more emotional.
Joy and fear are higher for the in-group interactions, even though fear is
about twice as common in the Covid-19 data.

Figure 3. Affective polarization. Difference between the mean emotion
confidence of out-group interactions and the mean emotion confidence of in-
group interactions in the Roe v Wade and Covid-19 datasets. Error bars show
standard errors.

Figure 3 summarizes these results for both datasets by showing the difference
between the mean emotion confidence of out-group interactions and in-group
interactions. There exists a consistent emotion gap: partisan interactions
across party lines exhibit substantially more negativity and toxicity, and
less joy. This provides empirical evidence for affective polarization on
social media.

(a) Anger (b) Disgust (c) Fear (d) Toxicity (e) Joy (f) No emotion (g) Number
of words

Figure 4. Partisan differences in in-group and out-group affect in the Roe v
Wade data. Boxplots of emotions expressed in replies between same-partisanship
users (in-group interactions) and opposite-partisanship users (out-group
interactions) in the abortion debate, disaggregated by partisanship. Out-group
interactions show more (a) anger, (b) disgust, and use more (d) toxic
language, but also slightly less (c) fear and substantially less (e) joy. Out-
group interactions are less likely to have (f) no emotions and generally have
fewer (g) words, with systematic partisan differences. Differences in means
were tested for statistical significance using the Mann-Whitney U Test with
the Bonferroni correction: * indicates significance at $p<0.05$, ** -
$p<0.01$, *** - $p<0.001$, **** - $p<0.0001$ and, $ns$ \- not-significant.

####  4.1.1. Partisan Differences in Affective Polarization

Next, we disaggregate in-group and out-group interactions along partisan lines
to explore group differences in affective polarization. Across online
discussions about abortion (Roe v Wade data in Fig. 4) and the pandemic
(Covid-19 data in Fig. 9), liberals show consistently less negative affect in
their in-group interactions. Conservatives show more toxicity in their out-
group interactions than liberals do in abortion discussions but more anger and
disgust in Covid-19 discussions. However, differences in interactions within
groups pale in comparison to the differences between in-group and out-group
interactions. We conclude that both partisan groups contribute to affective
polarization.

####  4.1.2. Out-group Replies are Shorter

We find that out-group replies are systematically shorter than in-group
replies (Fig. 2g), regardless of partisanship (Fig. 4g). This difference is
statistically significant: in the Roe v Wade data, out-group replies are on
average four words shorter, while in the Covid-19 data, they are half a word
shorter. Together with the finding that out-group interactions are more
emotional, this suggests that people communicate across party lines not to
convey information but rather to express animosity and promote conflict.

(a) Roe v Wade (b) Covid-19 (c) French Elections

Figure 5. Social Networks. Retweet networks of online discussions about a)
abortion, b) the Covid-19 pandemic, and c) 2017 French elections. The networks
were visualized using large graph embedding method.

###  4.2. Affect and Network Structure

(a) Anger (b) Disgust (c) Fear (d) Toxicity (e) Joy (f) No emotion (g) Number
of words

Figure 6. Affective polarization in the retweet network of Roe v Wade data.
Density plot shows the number of replies with a specific emotion between two
users a given distance apart. Emotions like (a) anger, (b) disgust and (d)
toxicity increase with Pagerank distance between users, while (c) fear and (e)
joy decrease with distance, as do (f) no emotions and (g) reply length.

In this section, we explore the relationship between affect and network
structure, using retweets to create social networks for the three datasets we
study (Fig. 5). The retweet network of abortion conversations in the Roe v
Wade dataset (Fig. 5a) shows two large dense clusters with additional
substructure near the larger cluster, as well as small clusters in the
periphery. The coarse-grained structure of the network captures partisan
divisions: most of the liberals in our data are in the larger dense cluster
and the conservatives are in the smaller dense cluster. The fragmentation of
the retweet network into two dense clusters reflects the polarized nature of
the abortion debate, which remains one of the most contentious and emotionally
divisive issues in society. The abortion conversations in our data were
especially charged, since they included reactions to the US Supreme Court
overturning the Roe v Wade decision, which guaranteed US women access to
abortion. The strong emotions the decision elicited on both sides of the issue
continue to reverberate.

The retweet network of the Covid-19 discussion (Fig. 5b) contains many small
dense clusters. Although discussions about the pandemic did grow to be
polarized, during the first months of the pandemic covered by our dataset,
these divisions do not appear entrenched. The retweet network of the French
Elections data (Fig. 5c) is sparser due to less data, but with complex
structure.

####  4.2.1. Affect vs Network Distance

We measure distance between two users in the retweet network as the inverse of
the pair’s personalized Pagerank score. Figure 6 shows a density plot of reply
interactions as a function of the distance between pairs of interacting users
in the Roe v Wade data. The grey cells in the figures represent the number of
replies expressing a particular emotion. (Figures 10 and 11 in the Appendix
show similar plots for the Covid-19 and French Elections data.) Users interact
with others across diverse distances in the retweet network. To uncover trends
in the data, we perform linear regression on emotion confidence scores using
distance as the independent variable. Blue lines in Fig. 6 (similarly in Figs.
10, 11) show regression lines along with the 95% confidence intervals. When
users reply to those who farther away from them in the retweet network, they
tend to be more negative, expressing more anger (Fig. 6a) and disgust (Fig.
6b) and using more toxic language (Fig. 6d). In contrast, users tend to
express more joy (Fig. 6e) and fear (Fig. 6c) in replies to others who are
closer to them in the retweet network and also use more words (Fig. 6g).

(a) Pagerank Distance (b) Shortest Path

Figure 7. Regression coefficients of affect as a function of network distance.
The bars represent the value of the regression coefficient of the emotion or
toxicity of replies as a function of network distance measures in terms of (a)
Pagerank distance or (b) shortest path between interacting users. Error bars
show 95% confidence interval. The disgust emotion applies only to the Roe v
Wade and the Covid-19 datasets, while the guilt emotion applies to the 2017
French Elections dataset. Regression coefficient of the number of words in
replies scaled down by 100.

The observed trends do not depend on how we measure network distance. Figure
12 shows emotions of interactions as a function of the shortest path between
interacting user. Although there is more variation in distances, the trends
observed in Fig. 6 still hold.

Figure 7 brings together all regression coefficients across all three datasets
in the same plot, showing agreement with the traditional measures of affective
polarization (Fig. 3). Results hold even in French data, where we do no have
user partisanship labels. Note that instead of disgust, the other emotion with
a significant trend is the emotion cluster “guilt, shame,” which acts similar
to fear. There are minor differences across datasets. In the abortion
discussions, replies to more distant users were less emotional and the French
discussions did not show shorter replies to more distant users. These results
show that we can measure affective polarization even in the absence of
partisan labels that define the in-group and the out-group. Social networks
organize themselves so that users feel warmer towards those who are closer to
them in the social network and colder towards those who are farther away from
them.

###  4.3. Discussion

Our study analyzed emotions expressed in online interactions and found
evidence of affective polarization. In the discussions about abortion and the
Covid-19 pandemic, users who were identified as liberal or conservative,
expressed more emotions in their replies to out-group members, i.e., opposite-
partisanship users, than in their replies to in-group members, i.e., same-
partisanship users. The valence of replies had the hallmarks of affective
polarization, namely “in-group favoritism, out-group animosity”
(iyengar2012affect, 34). Specifically, in the discussions about abortion and
the pandemic, out-group replies contained more negative emotions like anger
and disgust, and used more toxic language, than replies to in-group members.
In addition, in-group replies in the abortion debate expressed more joy,
although this effect was not statistically significant in the Covid-19
discussions.

Importantly, we showed that affective polarization generalizes beyond the in-
group/out-group dichotomy. When accounting for the social distance between
interacting users in the retweet network, a proxy of the follower graph,
anger, disgust and toxicity all increased with distance, while joy largely
decreased. These findings generalized across datasets, languages (French
Elections data was mostly in French), methods used to measure emotions and
network distance, confirming robustness of findings. This finding is important
in two ways. First, it allows for estimating the degree of affective
polarization even when group membership is not known. Second, it suggests that
emotions play an important role in organizing social networks. The
implications of this idea have to be investigate in future work.

Our findings also shed more light on affective polarization. Although we were
able to measure multiple categories of emotions and toxicity, only a subset of
these displayed group polarization: anger, disgust, fear, joy, toxic language
and obscenities. Of the positive emotions we measured, including love and
optimism, only joy showed group variation and even then, the differences were
less than for negative emotions. Our study also revealed that like joy, fear
is usually higher in in-group interactions. This stands in contrast to
previous studies (iyengar2015fear, 35), but suggests the complex role of fear
in social interactions. Studies of folklore and mythology suggest that fear
serves a social cohesion function: by making social threats salient, fear
increases solidarity so as to counter these threats (tang2016visualizing, 55,
56). Our other finding that in-group replies tend to be longer, also supports
the notion that in-group interactions serve to share information on how to
negotiate threats, while the shorter out-group interactions serve to promote
conflict rather than exchange information. More work needs to be done to
further explore these tantalizing ideas.

##### Limitations

Like any study of social media, ours has a number of limitations that tamper
its conclusions. By necessity, our datasets represented a small sample of
online discussions, even when controlling for the topic. Beyond biases
introduced by keyword-centered tweet collection, not observing all
interactions may have limited the range of emotions we observed. Similarly,
retweets are a biased sample of the follower relationships (rao2023retweets,
49), which may have impacted our conclusions. Beyond this, errors in
partisanship detection, emotion and toxicity classification, could have
further polluted our findings. While we cannot discount all of these biases,
the consistency of our results across datasets, scenarios and languages, gives
us confidence about our conclusions.

Another thing to consider is that our results could be explained by some
confounder, rather than group polarization or network structure. For example,
emotionally charged content is retweeted more frequently (brady2017emotion,
12), adding emotional texture to retweet networks. Moreover, Twitter’s
personalization algorithm may highlight emotionally charged content, thereby
driving engagement (brady2021social, 11). Although we cannot discount all
confounders, the consistency of our findings across different datasets is
encouraging.

##  5\. Conclusion

We found evidence of affective polarization in online interactions wherein
users expressed more negative emotions like anger and disgust in their replies
to members if the opposing partisan group compared to their own group, and
used more toxic language. We also found evidence of in-group favoritism
through greater expression joy in replies to in-group members, but not in all
datasets. Unlike previous research, which measured affective polarization
based on how people talked about out-group members (tyagi2020affective, 59,
41, 50), we examine how people talk to out-group members. Importantly, we also
showed that the degree of affective polarization increased with social
distance in the retweet network, suggesting that emotions play a key role in
organizing network structure. This suggests that disallowing people who are
not directly connected to a user to reply to that user’s posts may help reduce
the toxicity and negativity of online interactions.

Our results also highlight important differences between reply and retweet
interactions. While researchers sometimes conflate both types of interactions
when building social networks, our findings suggest that these interactions
serve a very different purpose and that combining them may obfuscate important
features of network structure. Despite potential limitations like incomplete
observability and data biases, the consistency of results across datasets,
languages and methods, provides confidence in the conclusions of our study
about the presence of affective polarization and the importance of emotions in
shaping online interactions.

###### Acknowledgements.

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##  Appendix A Appendix

(a) Anger (b) Disgust (c) Fear (d) Toxicity (e) Joy (f) No emotion (g) Number
of words

Figure 8. Covid-19: In-group vs Out-group Affect. Boxplot of emotions
expressed in replies between two users with the same ideology (in-group) and
users with different ideology (out-group) in the Roe v Wade data. Out-group
interactions show more (a) anger, (b) disgust, and use more (d) toxic
language, but also slightly less (c) fear and substantially less (e) joy. Out-
group interactions are less likely to have (f) no emotions and generally have
fewer (g) words.

(a) Anger (b) Disgust (c) Fear (d) Toxicity (e) Joy (f) No emotion (g) Number
of words

Figure 9. Partisan Differences in In-group and Out-group Affect in the
COVID-19 data. Boxplots of emotions expressed in replies between same-
partisanship users (in-group interactions) and opposite-partisanship users
(out-group interactions) in the abortion debate, disaggregated by
partisanship. Out-group interactions show more (a) anger, (b) disgust, and use
more (d) toxic language, but also slightly less (c) fear and substantially
less (e) joy. Out-group interactions are less likely to have (f) no emotions
and generally have fewer (g) words, with systematic partisan differences.

(a) Anger (b) Disgust (c) Fear (d) Toxicity (e) Joy (f) No emotion (g) Number
of words

Figure 10. Affective polarization in the retweet network of COVID-19 data.
Density plot shows the number of replies with a specific emotion between two
users a given distance apart. Emotions like (a) anger, (b) disgust and (d)
toxicity increase with Pagerank distance between users, while (c) fear and (e)
joy remain constant.

(a) Anger (b) Guilt (c) Fear (d) Toxicity (e) Joy (f) No emotion (g) Number of
words

Figure 11. Affective polarization in the retweet network of the French
Elections data (2017). Density plot shows the number of replies with a
specific emotion between two users a given distance apart. Emotions like (a)
anger and (d) toxicity increase with Pagerank distance between users, while
(b) guilt, (c) fear and (e) joy decrease with distance.

(a) Anger (b) Disgust (c) Fear (d) Toxicity (e) Joy (f) No emotion (g) Number
of words

Figure 12. Affective polarization in the retweet network of Roe v Wade data,
with shortest path measuring distance between interacting users. Density plot
shows the number of replies with a specific emotion between two users a given
distance apart. Emotions like (a) anger, (b) disgust and (d) toxicity increase
with network distance between users, while (c) fear and (e) joy decrease with
distance, as do (f) no emotions and (g) reply length.

(a) Anger (b) Disgust (c) Fear (d) Toxicity (e) Joy (f) No emotion (g) Number
of words

Figure 13. Affective polarization in the retweet network of Roe v Wade data.
Density plot shows the number of replies with a specific emotion between two
users a given distance apart in the graph embedding space (Fig. 5a). Emotions
like (a) anger, (b) disgust and (d) toxicity increase with distance between
users, while (c) fear and (e) joy decrease with distance, as do (f) no
emotions and (g) reply length.

(a) Anger (b) Disgust (c) Fear (d) Toxicity (e) Joy (f) No emotion (g) Number
of words

Figure 14. Affective polarization in the retweet network of COVID-19 data.
Density plot shows the number of replies with a specific emotion between two
users a given distance apart in the graph embedding space (Fig. 5b). Emotions
like (a) anger, (b) disgust and (d) toxicity increase with distance between
users, while (c) fear and (e) joy remain constant.

(a) Anger (b) Guilt (c) Fear (d) Toxicity (e) Joy (f) No emotion (g) Number of
words

Figure 15. Affective polarization in the retweet network of French Elections
data (2017). Density plot shows the number of replies with a specific emotion
between two users a given distance apart in the graph embedding space (Fig.
5c). Emotions like (a) anger and (d) toxicity increase with distance between
users, while (b) guilt, (c) fear and (e) joy decrease with distance. (a)
Embedding Distance Figure 16. Regression coefficients of affect as a function
of network distance. The bars represent the value of the regression
coefficient of the emotion or toxicity of replies as a function of network
distance measures in terms of graph embedding distance (Fig. 5a) between
interacting users. Error bars show 95% confidence interval. The disgust
emotion applies only to the Roe v Wade and the Covid-19 datasets, while the
guilt emotion applies to the 2017 French Elections dataset. Regression
coefficient of the number of words in replies scaled down by 100.