# Perspective-taking to Reduce Affective Polarization on Social Media

Martin Saveski111Authors contributed equally., Nabeel Gillani11footnotemark:
1, Ann Yuan, Prashanth Vijayaraghavan, Deb Roy

###### Abstract

The intensification of affective polarization worldwide has raised new
questions about how social media platforms might be further fracturing an
already-divided public sphere. As opposed to ideological polarization,
affective polarization is defined less by divergent policy preferences and
more by strong negative emotions towards opposing political groups, and thus
arguably poses a formidable threat to rational democratic discourse. We
explore if prompting perspective-taking on social media platforms can help
enhance empathy between opposing groups as a first step towards reducing
affective polarization. Specifically, we deploy a randomized field experiment
through a browser extension to 1,611 participants on Twitter, which enables
participants to randomly replace their feeds with those belonging to accounts
whose political views either agree with or diverge from their own. We find
that simply exposing participants to “outgroup” feeds enhances engagement, but
not an understanding of why others hold their political views. On the other
hand, framing the experience in familiar, empathic terms by prompting
participants to recall a disagreement with a friend does not affect
engagement, but does increase their ability to understand opposing views. Our
findings illustrate how social media platforms might take simple steps that
align with business objectives to reduce affective polarization.

##  1 Introduction

Political polarization in America is growing more severe (Pew Research Center
2014), with a “primacy of partyism” and strong negative emotions increasingly
characterizing inter-group discourse (Westwood et al. 2017). This “affective
polarization” (Iyengar, Sood, and Lelkes 2012) is particularly concerning
because partisan affect often only weakly correlates with actual policy
preferences (Iyengar et al. 2019). For these reasons, affective polarization
poses a danger to the democratic process because partisan voters are more
likely to sidestep rational discussion and debate when challenging opposing
views and crediting their own.

Some scholars highlight homophilic self-sorting and geographic balkanization
(Sunstein 2017; Bishop 2008) as accelerants of affective polarization because
they limit exposure to alternative viewpoints. The meteoric rise of social
media platforms and internet-based political discourse has further fueled
self-sorting through the creation and ossification of “echo chambers”
(Sunstein 2002; Bakshy, Messing, and Adamic 2015), despite the potential these
digital tools have to democratize access to diverse social and political
perspectives (Shirky 2011).

While much research has explored how echo chambers on social media platforms
fuel polarization (Bakshy, Messing, and Adamic 2015; Conover et al. 2011) or
provided theoretical explorations around how connections may be formed between
individuals with opposing views in order to reduce controversy (Garimella et
al. 2017), there have been few experimentally-driven efforts to empirically
understand how we might mitigate it. The few randomized experiments that have
been carried out have largely explored whether or not exposure to diverse
content—as opposed to exposure to people with diverse political views—reduces
political animosity, even though prior research has highlighted the importance
of contact between people in opposing groups to reduce inter-group conflict
(Pettigrew and Tropp 2006). For example, several digital tools emerged
following the 2016 U.S. Presidential Election to enable individuals to explore
their own social media echo chambers (PolitEcho 2016), read news from
politically diverse sources (Read Across the Aisle 2017), and even connect
face-to-face with members of opposing political groups (Hi From the Other Side
2016). However, to our knowledge, none of these tools evaluated the causal
effects of their interventions on reducing political animosity between
disparate groups.

There are a few notable exceptions. One is a recent randomized field
experiment by Bail et al. (2018), which revealed that exposing partisan
Twitter users to a bot that retweets content produced by political elites from
the opposing party can actually exacerbate ideological polarization. The
study’s findings echo prior work demonstrating how exposure to politically-
discordant content can strengthen one’s partisan stance and further entrench
political divides (Nyhan and Reifler 2010). Another is a recent randomized
field experiment conducted by Levy (2021), which shows that increasing
exposure to counter-attitudinal news on Facebook by prompting users to “like”
the pages of media outlets with opposing political slants can decrease
affective polarization. The different outcomes of these studies suggest that
different definitions of polarization, coupled with subtle differences in how
people are exposed to opposing views, can have profound effects on our
interpretation of the merits and pitfalls of exposure to diverse perspectives.

In this paper, we build on these prior efforts by designing and deploying a
randomized field experiment on Twitter. Much like Levy (2021), we focus on
affective (not ideological) polarization given both its rampant acceleration
and the threat it poses to rational democratic processes. We differ from prior
experiments, however, by designing a person-centric experience: one that seeks
to scaffold exposure to a more holistic view of people from opposing political
camps, instead of only exposing them to opposing content. This focus is
motivated by work in social psychology that highlights the value of
“perspective-taking”—or stepping into someone else’s shoes to understand their
experiences and worldviews—in promoting cognitive empathy (Selman 1980) and
reducing intergroup conflict (Bruneau and Saxe 2012; Vescio, Sechrist, and
Paolucci 2003; Lamm, Batson, and Decety 2007; Batson, Early, and Salvarani
1997). Our focus on empathy is informed by Feinberg et al. (2015), who argue
that greater empathy for members of disparate groups is one way to reduce
strong negative emotions and promote more productive interactions across
political divides. It is further loosely related to recent work from political
science illustrating how more personal appeals, e.g., priming a shared
American identity (Levandusky 2017) or correcting misconceptions of opposing
groups (Ahler and Sood 2018) can actually reduce affective polarization and
perceived social distance. Importantly, we focus on promoting cognitive
empathy between opposing groups, given that increasing empathy within groups
may actually further fuel partisan conflict (Simas, Clifford, and Kirkland
2020).

Concretely, we deploy a randomized field experiment through a browser
extension to 1,611 participants on Twitter, which enables them to “step into
the shoes of another Twitter user” by randomly replacing their feeds with
those belonging to accounts whose political views either agree with or diverge
from their own. We find that exposing participants to opposing feeds increases
engagement on Twitter, but not cognitive empathy, measured as a self-expressed
understanding of why some people might identify with the views represented in
the replacement feed.222The code needed to replicate our analyses are
available at: https://github.com/msaveski/twitter˙perspective˙taking However,
first priming participants in familiar, empathic terms by asking them to
recall a time they disagreed with a friend increases self-expressed cognitive
empathy when browsing opposing feeds. We believe our experimental design and
findings answer a recent call for more behavioral science-inspired
interventions to promote demographic discourse online (Lorenz-Spreen et al.
2020), and in doing so, make the following contributions to researchers and
practitioners interested in mitigating affective polarization in digital
settings:

  1. 1.

A field pilot that enables participants to experience the Twitter feeds of
those whose political views may differ from their own,

  2. 2.

An analysis of how exposure to opposing feeds affects platform engagement
patterns and perceptions of those on different sides of the political aisle,
and

  3. 3.

Insights into the influence empathic framing can have on how social media
users reason about discordant viewpoints and those who hold them

Below, we review related work, describe the details of our experimental design
and analyses, explore key results, and discuss their implications for
researchers and practitioners interested in promoting understanding and
reducing strong negative emotions across partisan divides online.

##  2 Related work

There is a vast and growing literature that explores political polarization
online. Many of these studies fall into two overarching groups: empirical and
theoretical analyses of political polarization, and randomized experiments
that seek to further identify, and perhaps even address, the underlying causal
mechanisms of polarized attitudes and discourse. While an exhaustive review of
these topics is out of scope for this paper, below we discuss several related
studies that ground our work.

### Analyses of political polarization

As discussed in the introduction, political polarization comes in many
flavors: while ideological polarization describes the extent to which views
about specific policies and issues align with one’s political group (Pew
Research Center 2014), affective polarization describes the extent to which
opposing groups feel strong negative emotions for one another (Iyengar et al.
2019). Much of the empirical work exploring political polarization online has
explored a concept that is related to, but distinct from, these concepts:
political homophily (McPherson, Smith-Lovin, and Cook 2001). Political
homophily describes the tendency for partisan actors to connect with and
consume content from people and sources that reflect their political views. It
has likely been of interest to social media and web researchers in part
because it is often easier to measure (e.g., by analyzing social ties and
content sharing patterns) than other notions of polarization, which may
require a more detailed understanding of how members of certain groups
perceive others. While homophily is distinct from polarization, studies have
shown that a greater propensity to engage with politically concordant groups
and ideas can increase both ideological (Sunstein 2002) and affective (Levy
2021) polarization. This is one of the reasons we design our study to explore
the effects of tools that help mitigate such homophily online—and view doing
so as a possible step towards mitigating polarization.

There have been several analyses of political homophily on digital platforms,
especially on social media platforms like Facebook and Twitter, where
politically-active users generally tend to connect with and consume content
from sources that align with their political views (Bakshy, Messing, and
Adamic 2015; Conover et al. 2011; Cossard et al. 2020; Garimella et al. 2021;
Cinelli et al. 2021). There is also evidence suggesting these trends, at least
on Twitter, are intensifying over time (Garimella and Weber 2017). As these
trends unfold on digital platforms, however, there continues to be a debate
about their root causes. On the one hand, several have described the existence
of a “filter bubble” effect, where personalization algorithms on search,
social media, and other platforms increasingly drive people into buckets of
other people and content that reflect their worldviews (Pariser 2011;
Robertson, Lazer, and Wilson 2018; Le et al. 2019; dos Santos et al. 2020).
The recent experiment by Levy (2021) described earlier appears to confirm this
hypothesis and illustrates how such selective exposure can increase affective
polarization. On the other hand, there is a belief that while platforms play a
role in promoting selective exposure through personalized recommendations,
these recommendations are ultimately grounded in user preferences and
psychology, as evidenced by who they follow and what they consume and share
(Bakshy, Messing, and Adamic 2015). Invariably, much more empirical work is
needed to better-understand how both homophilous and heterogeneous political
interactions manifest online (e.g., as in An et al. (2019)), what causes them,
and how these relate to different types of of polarization.

While homophily may be linked to polarization, there is still debate on how to
promote more cross-cutting dialog and exposure to opposing views, if at all.
For example, Gillani et al. (2018) and Garimella et al. (2017) highlight the
importance of thoughtfully selecting accounts that are more likely to bridge
opposing viewpoints. Recent studies have also shown that simply using
political stance labels in content (Gao et al. 2018) or priming participants
with signals that reveal opposing political views (Guildbeault, Becker, and
Centola 2018) can exacerbate existing divides, highlighting the challenges
involved in fostering more exposure and exchanges across partisan groups.
Other studies have investigated the role that media outlets play in political
polarization online and how they can change the language of their promotional
social media posts to engage more politically diverse audiences (Saveski et
al. 2022). Researchers have also qualitatively explored attitudes towards
facilitating more exposure to diverse perspectives online—seeking to uncover
under which conditions, and for which groups, exposure to diverse perspectives
may be favorable and productive for achieving democratic aims (Nelimarkka et
al. 2019; Grön and Nelimarkka 2020).

### Randomized experiments to understand polarization

Our work also builds on recent studies that have used randomized experiments
to study the causal effects of exposure to opposing views on ideological and
affective polarization.

Bail et al. (2018) conduct a randomized experiment incentivizing participants
to follow a Twitter bot that retweets posts by influential accounts with
opposing political views. While the intervention did not affect Democrats,
they found that Republicans expressed even more conservative views after
following a liberal bot, suggesting that there might be a “backfire” effect.
In contrast, Guess and Coppock (2020) run a series of survey experiments
exposing participants to opposing views but find no evidence of “backfire”
effects. A recent study by Spitz et al. (2021) that focuses on the identity of
the source of opposing views provides some insights on when “backfire” effects
are more likely to occur. Using a randomized survey experiment, they find that
opposing views expressed by celebrities, liked or disliked, did not affect
participants’ willingness to change their opinions but that opposing views
expressed by experts lead to even further issue polarization.

Other studies have sought to understand the effect of exposure to partisan
news on affective polarization. Levy (2021) ran a randomized experiment
encouraging participants to subscribe to the Facebook pages of conservative
and liberal media outlets. He finds that participants are willing to engage
with opposing news on social media and that such exposure leads to a decrease
in affective polarization. In a similar experimental design, Guess et al.
(2021) run a randomized experiment incentivizing participants to set their
browser homepage to a left- (HuffPost) or right-leaning (Fox News) news source
and subscribe to corresponding Facebook pages. In contrast to Levy, they do
not find any significant effects on the participants’ feelings toward the
parties or their perceptions of polarization. Instead, they find a significant
decrease in the participants’ trust in mainstream media up to one year later.

In contrast to previous studies, we design a person-centric intervention that
offers participants more holistic exposure to people with opposing political
views, instead of only exposing them to opposing content.

##  3 Experimental design and recruitment

#### Browser extension.

Figure 1: (A) Illustration of Twitter’s web interface with the Chrome
extension installed. Users can press the “Flip my feed” button and replace
their feed with someone else’s Twitter feed. They can flip their feed multiple
times or return to their own feed at any point. (B) Control and empathic
prompt (added emphasis) shown at random to the users when they first flip
their feed. (C) Blue (left-leaning) or Red (right-leaning) destination feed
shown to the users after they press the “Flip my feed” button (colors shown
only in the illustration).

Our experimental intervention is deployed through a Google Chrome extension
that allows Twitter users to replace the content in their feeds with content
from another real left- or right-leaning Twitter account’s feed. After
installing the extension, the user presses the “Flip my feed” button under
their profile picture, which randomly selects one of 200 pre-curated
“destination feeds” to replace their own.333To select the accounts, we started
with a set of accounts randomly sampled from those that were active in
political discourse on Twitter during the 2016 U.S. Presidential Election
cycle (Vijayaraghavan, Vosoughi, and Roy 2016). Three human annotators
independently classified each account’s political ideology as either left or
right-leaning. We selected the first 100 left and right-leaning accounts that
each of the annotators agreed on, respectively, to arrive at our set of 200.
These destination feeds are approximate chronological reconstructions of a
real Twitter user’s feed in order to cultivate an experience of seeing Twitter
from another user’s point of view with the highest possible fidelity. The user
can then browse and interact (via likes, replies, and retweets) with the new
feed as if it were their own. At any time, the user can load another randomly-
selected feed.

Our extension enables users to “step into someone else’s Twitter feed” to
experience their social media universe, and implicitly, reason about the
content and other media forces that likely shape their political opinions. Our
overarching hypothesis is that this process can activate the user’s theory of
mind (Langdon, Coltheart, and Ward 2006), a critical element in the practice
of perspective-taking: a practice that has proven to promote empathy and
reduce intergroup tension and animosity in other contexts (Bruneau and Saxe
2012).

#### Outcomes.

We measure a series of indicators to better-understand how different aspects
of the extension influence users’ beliefs and behaviors:

  * •

Usage: the total amount of time (in seconds) spent browsing destination feeds.

  * •

Engagement: the number of times participants clicked, liked, or retweeted one
of the tweets shown in the destination feed.

  * •

Survey responses: after a user scrolls through 50 tweets in a given feed, they
are prompted (with 50% probability444With the exception of the first time they
load another feed, in which case we always show users a questionnaire after
they scroll through 50 tweets.) to respond to the following four questions
using a sliding scale from 0 to 100 (0 = Strongly disagree; 100 = Strongly
agree):

    * –

Q1: “This feed is different from what I’m used to seeing”

    * –

Q2: “I learned something new from browsing this person’s feed”

    * –

Q3: “I can understand why some people might identify with the views shown in
this feed”

    * –

Q4: “In the future, I would be interested in having a conversation with this
feed’s owner”

The above questions serve different purposes: Q1 and Q2 are meant to validate
the effectiveness of the experience in actually exposing people to politically
disparate/novel information and people; Q3 seeks to assess the extent to which
stepping into someone else’s social media universe actually enhances their
ability to reason about why someone might hold a particular set of political
views; and Q4 seeks to measure to what extent participants might be open to
deeper in-person engagement with those who hold opposing political views.

#### Treatments.

We use a two-by-two factorial design. The first factor varies the political
leaning of the feed served to the user: same or opposing (as inferred using
the political alignment metric described in Section 4). The second factor
varies the type of initial prompt shown to the user: control vs. empathic.
Participants are assigned to a prompt condition only once, but are assigned to
a different feed condition every time they load another feed. In both cases,
participants / sessions were assigned to experimental conditions using
Bernoulli randomization with probability of 0.5.

Figure 1 provides a visual depiction of these treatments, which we describe in
more detail below.

  1. 1.

Feed ideology: Each time a user loads another feed, they are randomly served
the feed of one of the 200 pre-selected accounts. On average, 50% of the time
they are shown a left-leaning (“blue”) feed; the other 50%, a right-leaning
(“red”) feed (Figure 1C). As a result, on average, 50% of the time
participants see a feed that approximately aligns with their political views,
and the other 50% of the time they see one depicting opposing views.

  2. 2.

Empathic prompt: Prior work (Bruneau and Saxe 2012; Broockman and Kalla 2016;
Adida, Lo, and Platas 2018) has demonstrated how prompting “perspective-
taking”, i.e., putting one’s self in the shoes of an “outgroup” member, can
reduce feelings of animosity towards that group. With this in mind, we show
the following “empathic prompt” to a random 50% of users right before they
load another feed for the first time: “If you see something you disagree with,
think of a time you and a friend had a disagreement” (Figure 1B). The
remaining users are enrolled in the control group, where they only see
instructions on how to use the tool. The motivation for including this
treatment is to explore if we can trigger framing effects (Kahneman and
Tversky 1979) through the use of familiar, non-adversarial language to help
mitigate inter-group animosity.

#### Participant Recruitment.

We sent Twitter Direct Messages to 1,600 potential participants in early
2017.555Only 1.4% of Direct Message recipients ended up using the extension,
suggesting media coverage and word-of-mouth drove most participation.
Potential participants were sampled from a wider set of 1M Twitter accounts
who discussed the 2016 U.S. Presidential Election on the platform between June
and mid-September 2016. Media coverage of the extension also played a key role
in driving participation. The nature of our data makes it impossible to know
exactly how many participants discovered and used the extension through each
recruitment channel.

We note that all results reported subsequently in the paper are conditional on
a person’s decision to use the extension, and so, we cannot generalize
findings to a wider population (e.g., all Twitter users in the US, or even
more broadly, all adult members of the U.S. population). However, we argue
that this self-selected group is an important and relevant population to
study. A large-scale application of a perspective-taking intervention on
social media platforms like Twitter and Facebook is likely to be deployed as a
new platform feature that users can choose to use, rather than being imposed
upon all users.666Note that this is different from content-centric
interventions (e.g., as in Bail et al. (2018)) where platforms, with minimal
user interface change, can decide to promote or add out-of-network cross-
cutting content to the users’ feeds. Furthermore, achieving progress towards
our ultimate goals of reducing intergroup animosity and promoting
understanding does not require everyone in the population to participate:
instead, engaging even a small subset of committed actors may be sufficient.

##  4 Methodology

#### Measuring Political Alignment.

We infer participants’ political alignment using their historical sharing
patterns. Previous studies have shown that user’s sharing patterns (i.e.,
which websites they link to in their posts) are predictive of their political
alignment (Bakshy, Messing, and Adamic 2015). We collect all tweets posted by
each participant one month prior to their first use of the extension and
define their political alignment as the average score of the political
alignment of the domains linked to in their tweets. The score ranges from -2
(very liberal) to 2 (very conservative). To determine the users’ political
leaning, we threshold their alignment score at zero. We use the domain
political alignment scores published by Bakshy et al. (2015) obtained by
analyzing the sharing patterns of 10.1M Facebook users who self-reported their
political affiliation. Finally, we compared the inferred political leanings
based on the users’ content sharing patterns with the political leanings
inferred from the users’ following patterns using the method proposed by
Barberá et al. (2015). We find that the predictions of the two methods agree
in 88.62% of the cases. However, the method based on the users’ content
sharing patterns allows us to estimate the political leaning of 40% more
users.

#### Reconstructing Feeds.

To obtain the data needed to reconstruct the Twitter feeds of the 200 “feed
giver” accounts we used in our experiment (100 left-leaning, 100 right-
leaning), we run two processes. The first process runs daily and downloads the
list of accounts followed by each of the feed givers. The second runs
continuously and downloads any new tweets posted by these followed accounts.
We used the Twitter REST API to collect this information in a timely fashion.

When a participant loads another their feed, we do the following: ($i$) sample
one of the 200 feed givers, ($ii$) fetch the feed giver’s followees from the
local database, ($iii$) fetch the tweets of the feed giver’s followees from
the local database, and ($iv$) sort tweets in reverse chronological order. To
increase the chances that participants see content from a wide range of
accounts, we make sure that no more than three tweets posted by the same
author appear sequentially. To ensure that tweets are up-to-date and have not
been deleted since they were downloaded initially, we download them again
using the Twitter API right before serving them to the participant. Finally,
to prevent exposing users to any inappropriate content, we filter out tweets
flagged as “possibly sensitive” by Twitter. We note that we opted for reverse-
chronological ordering of the reconstructed feeds as we were unable to
replicate the algorithmic curation used by Twitter to produce personalized
feeds.

#### Model Specifications.

To estimate the causal effects of the two treatments, we use Bayesian linear
mixed-effects models (Gelman and Hill 2006; McElreath 2020) which allow us to
simultaneously account for two key dependencies in the data: ($i$) that a
participant may load another feed multiple times, which will result in
multiple correlated observations; and ($ii$) that the same feed giver may be
assigned to multiple participants.

To avoid any biases that arise from the flexibility of choosing any model
specification, we restricted ourselves to using only the maximal random
effects structure that is justified by our design (Barr et al. 2013). For
analyses with this type of hierarchical structure, it is common to use
frequentist linear mixed effects models (e.g., using the lmer package in R
(Bates et al. 2015)). However, we found that the maximal frequentist linear
mixed effects models did not converge using this method, likely due to data
sparsity, leading us to use their Bayesian counterparts.777We note that such
convergence issues are common when fitting (frequentist) maximal mixed effects
(Barr et al. 2013). The recommendation in these cases is to either use
Bayesian approaches or fit simpler models that do converge. As we discuss
later, our robustness analyses show that fitting simpler frequentist models
leads to very similar results as fitting the full Bayesian models.

Given the by-participant and by-destination-feed-owner dependencies described
above, the model with the maximal random effects structure is:

$\displaystyle Y_{pg}=\beta_{0}$
$\displaystyle+\beta_{1}feed+\beta_{2}prompt+\beta_{3}(feed\cdot prompt)$
$\displaystyle+\beta_{x:feed}(feed\cdot\mathbf{X_{p}})+\beta_{x:prompt}(prompt\cdot\mathbf{X_{p}})$
$\displaystyle+P_{0p}+P_{1p}feed+G_{0g}+G_{1g}prompt+\epsilon_{pg},$

where $Y_{pg}$ is the outcome for participant $p$ who was shown user $g$’s
feed; $\beta_{0}$ is the baseline outcome, $\beta_{1}$ and $\beta_{2}$ are the
main effects of the opposing feed and empathic prompt conditions,
respectively; and $\beta_{3}$ is the interaction effect of the two conditions.
This interaction term allows us to model how the two treatments interact with
each other. For instance, one hypothesis is that the empathic prompt might be
more effective when the user is shown a feed of someone with an opposite
political leaning.

The coefficients $\beta_{x:feed}$ and $\beta_{x:prompt}$ are associated with
the terms that interact the two treatment assignments with the participants’
covariates ($\mathbf{X_{p}}$). We considered five participant covariates:
number of posts and number of likes made throughout their history of Twitter
use, number of accounts they are following, number of accounts following them,
and length of tenure on Twitter.888We logged and scaled all covariates expect
for length of tenure as their distributions were highly-skewed. We included
these covariates to adjust for any imbalance between the treatment and control
groups and potentially gain statistical power. The covariates serve as proxies
for various indicators of how active and well-known the accounts were on
Twitter, which presumably correlate with other off-platform characteristics
likely to influence response to treatment like profession, demographics, and
other characteristics, which we do not have access to.

The by-participant random effects include random intercepts ($P_{0p}$) and
random (feed opposite) slopes ($P_{1p}feed$) that allow the participants’
responses to vary according to which feed ideology condition they were
assigned to. Similarly, the by-feed-giver random effect include random
intercepts ($G_{0g}$) and random (prompt) slopes ($G_{1g}prompt$) that allow
the participants’ responses to vary according to the prompt condition they are
assigned to.

To fit the models, we use the R package brms (Bürkner 2017, 2018), which uses
Markov Chain Monte Carlo to perform posterior Bayesian inference on the
coefficients and intercepts highlighted above. We specify $\mathcal{N}(0,30)$
priors for the coefficients and $\mathcal{N}(0,1)$ for the standard
deviations. We use four chains to estimate each model and run each chain for
10,000 iterations.

#### Assessing Model Robustness.

To test the robustness of our results to variations of the model specification
we fit eight different model specifications, varying three key aspects of the
models: ($i$) whether the model includes a term interacting the two treatment
assignments (feed and prompt), ($ii$) whether the model includes the user
covariates, ($iii$) whether the model is frequentist (lmer (Bates et al.
2015))—which is less-than-maximal and includes only random intercept terms for
feed giver and participant (instead of random intercepts and slopes)—or
Bayesian (brms (Bürkner 2017, 2018)).

In most cases, all eight model specifications lead to the same substantive
conclusions. The estimates and credible/confidence intervals (CIs) of the
corresponding brms and lmer models are very close. Similarly, including the
covariates in the models leads to very small changes in the estimates. The
most consequential modeling decision is whether to include a term that
interacts the two treatment assignments. While including an interaction term
does not drastically affect the point estimates for the inferred slopes and
intercepts, it does lead to significantly wider CIs. This is particularly the
case for the CIs of the prompt term for the survey question responses. We note
that the model specification used for our main results (brms, with
interactions, with covariates) leads to the most conservative results. We omit
the results from our robustness checks in the body of this paper due to space
constraints, but refer interested readers to Section 8 for more information on
how to access the supplementary and replication materials.

#### Ethics and Protection of Human Subjects.

Our research protocol was approved by the MIT’s Institutional Review Board.
All participants downloaded and installed the extension through the Google
Chrome Extension store. The store included a link to the project’s webpage,
which contained information about the extension’s purpose, function, and
developers. Furthermore, the webpage contained a link to a privacy policy
describing which data would be collected from participants. Participants could
email an alias designated for the project to ask any additional questions, and
also, request that their data be removed from the research study at any point.
No such requests were received. We did not ask for informed consent from
participants, nor did we detail the specific research design or motivating
research questions, as doing so could introduce bias and compromise the
integrity of the study.

##  5 Results

Below, we present the effects of our two main treatments (Same vs. Opposite
Feed, Empathic vs. Control Prompt) and their interactions on the engagement
and survey measures described earlier. Figure 2 shows the treatment effects of
each of the two treatments, and Figure 3 shows the absolute values of the
outcomes across the four treatment conditions.

In total, 1,611 users whose political leaning we could infer participated in
the randomized experiment. On average, participants replaced their feed with a
feed of another Twitter user 3.14 times. All participants replaced their feed
at least once and were not required to do so more than once. They skewed left
(91%), had an average of 5.9 years tenure on Twitter at the time, and a median
of 2,039 posts, 736 favorites, 439 followees, and 294 followers. In the online
supplementary material, we show that participants assigned to different
treatment conditions did not have significantly different characteristics.

Figure 2: Effects of different experimental conditions on users’ behavior
(time spent and engagement) and answers to survey questions. (A) Showing a
feed that aligns with the users’ political views (same) vs. one that does not
(opposite); (B) Showing the control vs. empathic prompt to the user after they
load another feed for the first time. Error bars show 95% credible intervals.

#### Same vs. Opposite Feed.

Figures 2 A and B show changes in platform engagement (including time spent
browsing feeds) and responses to the survey questions, respectively, as a
result of each treatment. Participants spend 29% more time (27.4 average
increase in seconds, 95% CIs [17.6, 37.4]) browsing and are 37% more likely to
click on or engage with (like, retweet, or reply to) tweets shown in
politically-opposed destination feeds (0.11 average increase in engagements,
95% CIs [0.05, 0.17]), suggesting these feeds enhance engagement. One
possibility is that participants are more curious to explore novel information
when they browse an opposing feed.

When asked whether a destination feed contains content that is very different
from what they are used to seeing on Twitter (Survey: Q1), users who see a
politically-aligned feed provide a neutral answer (on average, 58 out of 100),
while users who see politically-opposed feeds are more likely to agree (26.1
point increase, 95% CIs [16.2, 35.9]) that the feed looks very different than
what they are used to seeing. These results confirm that our treatment is
valid and has the desired primary effect of exposing users to political views
outside of their social media bubbles.

Participants who view politically-opposed feeds are also more likely to report
that they learned something new by browsing the feed, though this effect is
marginally statistically insignificant (Survey: Q2, 8.8 points increase, 95%
CIs [-0.9, 18.5]); however, they are less likely to say that they understand
why others may hold such views (Survey: Q3, -11.2 points change, 95% CIs
[-21.3, -1.3]). The exposure to opposing views, however, triggers an interest
in learning more about the other side: compared to participants who saw a feed
that aligns with their views, participants who see politically-opposed feeds
are more likely to report that they would like to have a conversation with the
feed owner in the future (Survey: Q4, 16.4 points increase, 95% CIs [5.3,
27.4]).

The results suggest that using social media platforms to simply expose users
to those who hold different political perspectives can help increase
engagement, but on their own, are insufficient in bridging understanding gaps
and promoting empathy between disparate groups.

#### Empathic Prompt.

Showing participants an empathic prompt does not have a significant effect on
usage or engagement: participants spend slightly more time browsing feeds (3.3
seconds change, 95% CIs [-6.5, 12.9]) and are slightly less likely to engage
with feeds (-0.01 engagements decrease, 95% CIs [-0.06, 0.04])—though the
differences are not statistically significant.

While the prompt increases the extent to which participants indicate that they
learned something new by browsing the feed (Survey: Q2, 3.2 points increase,
95% CIs [-8.2, 14.4]), it causes recipients to be slightly less-likely to
report that what they saw was different than what they are used to seeing
(Survey: Q1, -4.2 points change, 95% CIs [-15.7, 7.3]), even after controlling
for the feed’s political alignment (i.e., same vs. opposite). Furthermore,
participants who receive the prompt are slightly more likely to report that
they understand why others might identify with the opinions shown in the feed
(Survey: Q3, 6.7 points increase, 95% CIs [-5.1, 18.4]). None of these results
are statistically significant.

Interestingly, accounts receiving the prompt treatment appear to be more
interested in having a conversation with the feed owner in the future (Survey:
Q4, 15.9 points increase, 95% CIs [2.9, 28.8]). The presence of an effect here
is surprising for two reasons: ($i$) the treatment constituted a minor change
in the default experience, and ($ii$) the users were shown the prompt only
once: immediately before they load another feed for the first time. The
results suggest the potential power of framing effects when conducting
interventions on social media platforms, and how certain types of framing can
alter how we perceive others, particularly in the context of an environment
like social media that offers limited context about other users.

Figure 3: Interactions between the effects of the two treatments. The lack of
significant interaction effects in this case means that the slopes of each
line within each subfigure are not statistically significantly different from
the other. However, using non-linear hypothesis tests, we find significant
differences across some pairs of treatments. In particular, we see that those
who see the empathic prompt and an opposing feed are less likely to view the
feed as different from what they are used to seeing (C) and more likely to
understand why some people might identify with the views represented in the
feed (E) compared to when they see the control prompt. Error bars show 95%
credible intervals.

#### Interaction Effects.

We do not find any significant interaction effects between the two treatments,
i.e., the directional effects of administering one treatment (e.g., showing an
opposing feed) is consistent across all outcomes, regardless of administering
the other (e.g., showing the empathic prompt). Figure 3 shows these results.

However, by observing Figure 3, we can see several notable differences in
responses between participants who are browsing politically-opposing feeds,
depending on whether they first saw the empathic or control prompt. We use
non-linear hypothesis tests (Bürkner 2017) to assess the significance of these
differences. The results reveal that participants who view the empathic prompt
followed by a politically-opposed feed are significantly less likely to
perceive the feed as different from what they’re used to seeing, compared to
browsing an opposing feed after seeing the control prompt (10.3 points
decrease, CIs [-18.0,-2.5], Figure 3C). This finding may help explain part of
the results depicted in Figure 3E: namely, that participants who see the
empathic prompt and subsequently browse a politically-opposed feed are
significantly more likely to understand why some people might identify with
the views shown in the feed, compared to browsing an opposing feed after
seeing the control prompt (7.9 points increase, CIs [0.4, 15.3]). Together,
these results suggest that the prompt may be effective in bridging perceived
psychological divides and promoting an understanding of why a member of the
political outgroup holds the views they do.

Interestingly, participants who see the empathic prompt and subsequently
browse a politically similar feed are also more likely to want to have a
conversation with the feed owner (15.9 points increase, CIs [2.9, 28.8],
Figure 3F); we do not see this effect for those who view the empathic prompt
and then browse opposing feeds, perhaps because there is already a high
baseline level of interest in having a conversation with those across the
political aisle, and so, the prompt has a limited additional effect. This high
baseline interest is sensible given the purpose of the browser extension and
thus likely associated selection biases among participants.

##  6 Discussion

Our experiment demonstrates that, while exposure to politically-opposed
Twitter feeds fuels engagement, it does not enhance participants’
understanding of why others might hold opposing views, unless this exposure is
framed (a priori) in terms of a familiar experience like disagreeing with a
friend.

Information novelty might help explain why politically-opposite feeds increase
engagement. The fact that respondents indicate politically-opposite feeds as
different from what they are used to seeing in their own feeds implies these
opposing feeds contained novel content. Prior studies have demonstrated
increased user engagement with content that is novel, for example, in the
context of online news consumption and sharing (Berger and Milkman 2012).
Furthermore, as novel information is often surprising, it also attracts more
attention (Wu and Huberman 2007; Itti and Baldi 2009). In the context of
social media platforms, this enhanced engagement is encouraging as it
demonstrates a consumer appetite for accessing alternative viewpoints and may
actually dovetail with key business objectives such as increasing user
engagement or time spent on the platform.

Prior work, however, has illuminated that a greater quantity of engagement
does not always translate into a higher quality of engagement, especially, in
the context of politics (Vosoughi, Roy, and Aral 2018). In particular,
explicit partisan framing has been shown to adversely affect open-mindedness
and the civility of ensuing public discourse (Guildbeault, Becker, and Centola
2018). These studies may help illuminate why, on its own, viewing politically-
opposite feeds does not promote understanding and bridge empathy gaps between
disparate group despite enhancing engagement.

The strong influence of the empathic prompt on promoting understanding can, in
part, be explained by framing effects and their ability to alter the nature
and quality of engagement and decision-making (Kahneman and Tversky 1979).
Framing effects are a foundational concept in behavioral science and have been
successfully used to drive behavior change across a range of domains like
marketing and advertising (Maheswaran and Meyers-Levy 1990), energy
consumption (Allcott 2011), and education (Kizilcec et al. 2017), to name a
few. Prior work in social psychology and perspective taking sheds some light
on the possible drivers of these results. For example, a study by Lamm et al.
(2007) involved showing participants videos of facial expressions produced by
patients receiving medical treatment, along with whether or not the treatment
was effective. The researchers found that participants who viewed videos of
patients undergoing ineffective treatments experienced more distress and less
empathy. In this vein, thinking of the empathic prompt as describing an
unsettling encounter that is, ultimately, non-adversarial (the nature of many
disagreements with friends) may help explain why prompt recipients were more
likely to respond with empathetic attitudes towards politically disparate feed
owners. It is also possible that the friendship framing enhanced perceived
commonality between the participant and feed owner. Indeed, prior work
suggests that greater perceived commonality between members of opposing groups
may serve as one channel through which to reduce affective polarization
(Levandusky 2017; Wojcieszak and Warner 2020), suggesting that the increases
in cognitive empathy we observed may offer a step towards reducing this
particularly-divisive form of political discordance.

Both the software and framing mechanism we developed in our study are simple
to design and deploy, suggesting social media platforms could realistically
build them into their platforms if they wish to reduce affective polarization
among their users. For example, platforms might create tools like our
extension and make them easily discoverable and accessible to users, who can
exercise their individual agency in deciding whether to use them in order to
explore viewpoints that differ from their own. These tools may even be issue-
specific: for example, tools that enable users to easily browse the different
sides of a trending issue or conversation unfolding on the platform. They
could also enable further filtering by enabling users to browse the viewpoints
of specific groups of people vis-a-vis certain issues (e.g., those in rural
areas; those in religious groups that differ from their own; etc). Critically,
these tools must be designed—through the use of empathic prompts or other
features—to scaffold exposure to diverse views in ways that promote
understanding without triggering backfire effects. Striking this balance will
likely require a commitment to continuous iteration and research on the part
of platforms. Our results suggest that making investments in developing these
types of features may actually align with business objectives like increasing
platform engagement, especially among the subsets of users who take interest
in them.

We note that the development of tools and prompts for scaffolding exposure to
diverse viewpoints is orthogonal to changes in the underlying algorithms used
to personalize content exposure. We are not arguing against changes to content
personalization algorithms—especially given the influence they have on which
content people access and the opinions they form—but emphasize that they are
distinct from optional user experiences to illustrate the multiple levers that
exist when determining how to promote exposure and reduce negative inter-group
affect. Tight-knit collaborations between platforms and academic researchers
from different disciplinary backgrounds may help inform the design and
evaluation of experiments that increase our collective understanding of the
relative merits and drawbacks of targeting these two (and other) levers for
reducing affective polarization online.

Before concluding, we note several limitations in our approach and analysis.
For one, selection bias in participation prevents our ability to generalize
findings to wider audiences (e.g., all Twitter users in the U.S., or more
broadly, all social media users.999Though arguably, given the gradual and
optional nature in which features like those offered through the extension
might be deployed to wider audiences in the future, this selection bias may
actually strengthen the external validity of our results.) Many of these
selection biases stem from media coverage of the extension during the specific
moment in time that we launched the experiment: in the months following the
2016 US Presidential Election, when there was particularly high interest
(especially among left-leaning groups) to better-understand the political
philosophies and motivations of those who voted for Donald Trump. Furthermore,
we are also only able to recover political ideologies for a subset of
participants, which may further bias towards those who are more likely to
share content and be active on Twitter. Our selection of feeds may have also
influenced the results: by selecting users who appear to follow unambiguously
left- or right-leaning Twitter accounts, we may have exacerbated the
likelihood of inciting backfire effects (as suggested in Garimella et al.
(2017) and Gillani et al. (2018)) that impede understanding and empathy.
Finally, our research design prevents us from understanding the precise causal
mechanisms at play in making the empathic prompt effective in enabling
participants to understand why feed owners hold the views they do, although
some of the studies highlighted earlier offer hypotheses (e.g., increasing
perceived commonality) that may serve as the basis of future experiments. We
believe these limitations offer promising avenues for future work on this
emerging topic.

##  7 Conclusion

In this paper, we describe results from a randomized field experiment
conducted on Twitter to investigate how social media platforms might help
reduce affective polarization and enhance understanding across political
divides. We deploy the experiment through a Google Chrome extension that
enables users to replace their Twitter feeds with a feed from a different
politically-active Twitter user. We find that feeds containing information
that contrasts with participants’ political views enhance engagement but not
an understanding of why someone might hold these divergent views. On the other
hand, prompting users to think of politically-opposing content in the context
of a prior disagreement with a friend does increase understanding, but not
engagement. Together, these findings illustrate how social media platforms can
take simple steps to reduce affective polarization on their platforms, and how
these steps may, in some cases, align with business objectives like enhancing
user engagement and time spent on the platform.

There are several opportunities for future work. Experimenting with different
prompts, for example, may shed additional light on which types of re-framing
are more or less effective in reducing affective polarization. These
experiments may also help shed light on how social media platforms can adopt
and deploy such changes through their platforms. Furthermore, the fact that
many participants indicated a desire to have a conversation with individuals
who hold opposing views suggests a future where social media platforms might
help extend online conversations into deeper, offline engagements between
individuals and/or groups. While much of the current public discourse
attributes worsening affective polarization to the form and function of
existing social media platforms, we hope our findings will motivate new
efforts to explore how these powerful technologies can help mitigate
polarization and promote empathy across divides.

##  8 Replication and Supplementary Materials

Supplementary materials and the code needed to replicate the analyses
presented in this paper can be accessed in the following Github repository:
https://github.com/msaveski/twitter˙perspective˙taking.

The supplementary
materials101010https://github.com/msaveski/twitter˙perspective˙taking/raw/main/supplementary-
material.pdf contain: (1) further description and justification of the mixed-
effects models used in the main analyses, (2) checks for the robustness of
results across different model specifications, (3) checks for covariate
balance to ensure that the users assigned to treatment and control have
similar background characteristics, and (4) additional analyses of results
accounting for selective attrition in the survey responses.

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