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Exposure to opposing views on social media can increase political polarization


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There is mounting concern that social media sites contribute to political polarization by creating "echo chambers" that insulate people from opposing views about current events. We surveyed a large sample of Democrats and Republicans who visit Twitter at least three times each week about a range of social policy issues. One week later, we randomly assigned respondents to a treatment condition in which they were offered financial incentives to follow a Twitter bot for 1 month that exposed them to messages from those with opposing political ideologies (e.g., elected officials, opinion leaders, media organizations, and nonprofit groups). Respondents were resurveyed at the end of the month to measure the effect of this treatment, and at regular intervals throughout the study period to monitor treatment compliance. We find that Republicans who followed a liberal Twitter bot became substantially more conservative posttreatment. Democrats exhibited slight increases in liberal attitudes after following a conservative Twitter bot, although these effects are not statistically significant. Notwithstanding important limitations of our study, these findings have significant implications for the interdisciplinary literature on political polarization and the emerging field of computational social science.
Content may be subject to copyright.
Exposure to opposing views on social media can
increase political polarization
Christopher A. Baila,1, Lisa P. Argyleb, Taylor W. Browna, John P. Bumpusa, Haohan Chenc, M. B. Fallin Hunzakerd,
Jaemin Leea, Marcus Manna, Friedolin Merhouta, and Alexander Volfovskye
aDepartment of Sociology, Duke University, Durham, NC 27708; bDepartment of Political Science, Brigham Young University, Provo, UT 84602; cDepartment
of Political Science, Duke University, Durham, NC 27708; dDepartment of Sociology, New York University, New York, NY 10012; and eDepartment of
Statistical Science, Duke University, Durham, NC 27708
Edited by Peter S. Bearman, Columbia University, New York, NY, and approved August 9, 2018 (received for review March 20, 2018)
There is mounting concern that social media sites contribute to
political polarization by creating “echo chambers” that insulate
people from opposing views about current events. We surveyed
a large sample of Democrats and Republicans who visit Twit-
ter at least three times each week about a range of social
policy issues. One week later, we randomly assigned respon-
dents to a treatment condition in which they were offered
financial incentives to follow a Twitter bot for 1 month that
exposed them to messages from those with opposing political
ideologies (e.g., elected officials, opinion leaders, media orga-
nizations, and nonprofit groups). Respondents were resurveyed
at the end of the month to measure the effect of this treat-
ment, and at regular intervals throughout the study period to
monitor treatment compliance. We find that Republicans who
followed a liberal Twitter bot became substantially more con-
servative posttreatment. Democrats exhibited slight increases
in liberal attitudes after following a conservative Twitter bot,
although these effects are not statistically significant. Notwith-
standing important limitations of our study, these findings have
significant implications for the interdisciplinary literature on polit-
ical polarization and the emerging field of computational social
political polarization |computational social science |social networks |
social media |sociology
Political polarization in the United States has become a central
focus of social scientists in recent decades (1–7). Americans
are deeply divided on controversial issues such as inequality, gun
control, and immigration—and divisions about such issues have
become increasingly aligned with partisan identities in recent
years (8, 9). Partisan identification now predicts preferences
about a range of social policy issues nearly three times as well
as any other demographic factor—such as education or age (10).
These partisan divisions not only impede compromise in the
design and implementation of social policies but also have far-
reaching consequences for the effective function of democracy
more broadly (11–15).
America’s cavernous partisan divides are often attributed to
“echo chambers,” or patterns of information sharing that rein-
force preexisting political beliefs by limiting exposure to oppos-
ing political views (16–20). Concern about selective exposure
to information and political polarization has increased in the
age of social media (16, 21–23). The vast majority of Ameri-
cans now visit a social media site at least once each day, and a
rapidly growing number of them list social media as their primary
source of news (24). Despite initial optimism that social media
might enable people to consume more heterogeneous sources
of information about current events, there is growing concern
that such forums exacerbate political polarization because of
social network homophily, or the well-documented tendency of
people to form social network ties to those who are similar to
themselves (25, 26). The endogenous relationship between social
network formation and political attitudes also creates formidable
challenges for the study of social media echo chambers and
political polarization, since it is notoriously difficult to establish
whether social media networks shape political opinions, or vice
versa (27–29).
Here, we report the results of a large field experiment designed
to examine whether disrupting selective exposure to partisan
information among Twitter users shapes their political attitudes.
Our research is governed by three preregistered hypotheses. The
first hypothesis is that disrupting selective exposure to parti-
san information will decrease political polarization because of
intergroup contact effects. A vast literature indicates contact
between opposing groups can challenge stereotypes that develop
in the absence of positive interactions between them (30). Stud-
ies also indicate intergroup contact increases the likelihood of
deliberation and political compromise (31–33). However, all of
these previous studies examine interpersonal contact between
members of rival groups. In contrast, our experiment creates
virtual contact between members of the public and opinion lead-
ers from the opposing political party on a social media site.
It is not yet known whether such virtual contact creates the
Social media sites are often blamed for exacerbating political
polarization by creating “echo chambers” that prevent people
from being exposed to information that contradicts their pre-
existing beliefs. We conducted a field experiment that offered
a large group of Democrats and Republicans financial com-
pensation to follow bots that retweeted messages by elected
officials and opinion leaders with opposing political views.
Republican participants expressed substantially more conser-
vative views after following a liberal Twitter bot, whereas
Democrats’ attitudes became slightly more liberal after fol-
lowing a conservative Twitter bot—although this effect was
not statistically significant. Despite several limitations, this
study has important implications for the emerging field of
computational social science and ongoing efforts to reduce
political polarization online.
Author contributions: C.A.B., L.P.A., T.W.B., J.P.B., H.C., M.B.F.H., J.L., M.M., F.M., and
A.V. designed research; C.A.B., L.P.A., T.W.B., H.C., M.B.F.H., J.L., M.M., and F.M. per-
formed research; C.A.B., T.W.B., H.C., J.L., and A.V. contributed new reagents/analytic
tools; C.A.B., L.P.A., T.W.B., H.C., M.B.F.H., J.L., M.M., F.M., and A.V. analyzed data; and
C.A.B., L.P.A., T.W.B., M.B.F.H., M.M., F.M., and A.V. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This open access article is distributed under Creative Commons Attribution-
NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND).
Data deposition: All data, code, and the markdown file used to create this report
will be available at this link on the Dataverse:
xhtml?alias=chris bail.
1To whom correspondence should be addressed. Email:
This article contains supporting information online at
1073/pnas.1804840115/-/DCSupplemental. PNAS Latest Articles |1 of 6
same type of positive mutual understanding—or whether the
relative anonymity of social media forums emboldens people
to act in an uncivil manner. Such incivility could be partic-
ularly rife in the absence of facial cues and other nonverbal
gestures that might prevent the escalation of arguments in offline
Our second hypothesis builds upon a more recent wave of
studies that suggest exposure to those with opposing politi-
cal views may create backfire effects that exacerbate political
polarization (34–37). This literature—which now spans sev-
eral academic disciplines—indicates people who are exposed
to messages that conflict with their own attitudes are prone to
counterargue them using motivated reasoning, which accentu-
ates perceived differences between groups and increases their
commitment to preexisting beliefs (34–37). Many studies in this
literature observe backfire effects via survey experiments where
respondents are exposed to information that corrects factual
inaccuracies—such as the notion that Saddam Hussein possessed
weapons of mass destruction prior to the 2003 US invasion of
Iraq—although these findings have failed to replicate in two
recent studies (38, 39). Yet our study is not designed to evaluate
attempts to correct factual inaccuracies. Instead, we aim to assess
the broader impact of prolonged exposure to counterattitudinal
messages on social media.
Our third preregistered hypothesis is that backfire effects
will be more likely to occur among conservatives than liberals.
This hypothesis builds upon recent studies that indicate con-
servatives hold values that prioritize certainty and tradition,
whereas liberals value change and diversity (40, 41). We also
build upon recent studies in cultural sociology that examine
the deeper cultural schemas and narratives that create and sus-
tain such value differences (34, 26). Finally, we also build upon
studies that observe asymmetric polarization in roll call voting
wherein Republicans have become substantially more conserva-
tive whereas Democrats exhibit little or no increase in liberal
voting positions (42). Although a number of studies have found
evidence of this trend, we are not aware of any that examine
such dynamics among the broader public—and on social media
in particular.
Research Design
Fig. 1 provides an overview of our research design. We hired
a professional survey firm to recruit self-identified Republi-
cans and Democrats who visit Twitter at least three times each
week to complete a 10-min survey in mid-October 2017 and
1.5 mo later. These surveys measure the key outcome vari-
able: change in political ideology during the study period via a
10-item survey instrument that asked respondents to agree or
disagree with a range of statements about policy issues on a
seven-point scale (α=.91) (10). Our survey also collected infor-
mation about other political attitudes, use of social media and
conventional media sources, and a range of demographic indi-
cators that we describe in SI Appendix. Finally, all respondents
were asked to report their Twitter ID, which we used to mine
additional information about their online behavior, including
the partisan background of the accounts they follow on Twit-
ter. Our research was approved by the Institutional Review
Boards at Duke University and New York University. All respon-
dents provided informed consent before participating in our
We ran separate field experiments for Democratic and Repub-
lican respondents, and, within each group, we used a block ran-
domization design that further stratified respondents according
to two variables that have been linked to political polarization:
(i) level of attachment to political party and (ii) level of interest
in current events. We also randomized assignment according to
respondents’ frequency of Twitter use, which we reasoned would
influence the amount of exposure to the intervention we describe
in the following paragraph and thereby the overall likelihood of
opinion change.
We received 1,652 responses to our pretreatment survey (901
Democrats and 751 Republicans). One week later, we randomly
assigned respondents to a treatment condition, thus using an
“ostensibly unrelated” survey design (43). At this time, respon-
dents in the treatment condition were offered $11 to follow a
Twitter bot, or automated Twitter account, that they were told
would retweet 24 messages each day for 1 mo. Respondents
were not informed of the content of the messages the bots would
retweet. As Fig. 2 illustrates, we created a liberal Twitter bot and
a conservative Twitter bot for each of our experiments. These
bots retweeted messages randomly sampled from a list of 4,176
political Twitter accounts (e.g., elected officials, opinion lead-
ers, media organizations, and nonprofit groups). These accounts
were identified via a network-sampling technique that assumes
those with similar political ideologies are more likely to follow
each other on Twitter than those with opposing political ideolo-
gies (44). For further details about the design of the study’s bots,
please refer to SI Appendix.
To monitor treatment compliance, respondents were offered
additional financial incentives (up to $18) to complete weekly
surveys that asked them to answer questions about the content
of the tweets produced by the Twitter bots and identify a picture
of an animal that was tweeted twice a day by the bot but deleted
immediately before the weekly survey. At the conclusion of the
study period, respondents were asked to complete a final survey
with the same questions from the initial (pretreatment) survey.
Of those invited to follow a Twitter bot, 64.9% of Democrats and
57.2% of Republicans accepted our invitation. Approximately
62% of Democrats and Republicans who followed the bots were
able to answer all substantive questions about the content of mes-
sages retweeted each week, and 50.2% were able to identify the
animal picture retweeted each day.
Fig. 3 reports the effect of being assigned to the treatment condi-
tion, or the Intent-to-Treat (ITT) effects, as well as the Complier
Average Causal Effects (CACE) which account for the differen-
tial rates of compliance among respondents we observed. These
estimates were produced via multivariate models that predict
respondents’ posttreatment scores on the liberal/conservative
scale described above, controlling for pretreatment scores on this
scale as well as 12 other covariates described in SI Appendix.
We control for respondents’ pretreatment liberal/conservative
scale score to mitigate the influence of period effects. Negative
scores indicate respondents became more liberal in response to
treatment, and positive scores indicate they became more con-
servative. Circles describe unstandardized point estimates, and
the horizontal lines in Fig. 3 describe 90% and 95% confidence
intervals. We measured compliance with treatment in three ways.
“Minimally Compliant Respondents” describes those who fol-
lowed our bot throughout the entire study period. “Partially
Compliant Respondents” are those who were able to answer
at least one—but not all—questions about the content of one
of the bots’ tweets administered each week during the survey
period. “Fully Compliant Respondents” are those who success-
fully answered all of these questions. These last two categories
are mutually exclusive.
Although treated Democrats exhibited slightly more liberal
attitudes posttreatment that increase in size with level of compli-
ance, none of these effects were statistically significant. Treated
Republicans, by contrast, exhibited substantially more conserva-
tive views posttreatment. These effects also increase with level
of compliance, but they are highly significant. Our most cautious
estimate is that treated Republicans increased 0.12 points on a
seven-point scale, although our model that estimates the effect of
treatment upon fully compliant respondents indicates this effect
2 of 6 | Bail et al.
Oered $11 to
Oered $11 to
follow Twitter
follow Twitter
bot that retweets
bot that retweets
24 messages from
24 messages from
liberal accounts each day for
liberal accounts each day for
1 month
1 month
Oered $11 to
Oered $11 to
follow Twitter
follow Twitter
bot that retweets
bot that retweets
24 messages from
24 messages from
conservative accounts each day for
conservative accounts each day for
1 month
1 month
Respondents were oered $11
to provide their Twitter ID and
complete a 10-minute survey
about their political attitudes,
social media use, and
media consumption habits
(demographics provided by
survey rm).
Respondents were
oered $12 to repeat
the pre-treatment
survey one month
after initial survey.
One week later, respondents
were assigned to treatment
and control conditions within
strata created using pre-
treatment covariates that
describe attachment to party,
frequency of Twitter use,
and overall interest in
current events.
Respondents in treatment
conditions informed they are
eligible to receive up to $6 each
week during the study period
for correctly answering
questions about the content of
messages retweeted by Twitter
Initial Survey Post-Survey Randomization Weekly Surveys
Fig. 1. Overview of research design.
is substantially larger (0.60 points). These estimates correspond
to an increase in conservatism between 0.11 and 0.59 standard
Discussion and Conclusion
Before discussing the implications of these findings, we first note
important limitations of our study. Readers should not interpret
Bail et al. PNAS Latest Articles |3 of 6
Lisa Murkowski (R-AK) @lisamurkowski Ben Carson @RealBenCarson
Don Young (R-AK) @repdonyoung Hillary Clinton @HillaryClinton
Jon Tester (D-MT) @SenatorTester Carly Fiorina @CarlyFiorina
Steve Daines (R-MT) @stevedaines Lawrence Lessig @Lessig
Mike Enzi (R-WY) @SenatorEnzi Mar tin O’Malley @martinomalley
John Barrasso (R-WY) @SenJohnBarrasso Donald Trump @realDonaldTrump
...etc ...etc ...etc ...etc
Extract the names of
all Twitter accounts
that these 563 elected
candidates follow
Create directed
network of all elected
candidates, and
everyone they follow;
dropping non-elected
less than 15 as well as
Twitter accounts from
U.S. government agencies,
and accounts that
originate outside the U.S.
Create adjacency
matrix that describes
following patterns of
the 4,176 “opinion
leaders” and conduct
Analysis. Adjust
scores of accounts with
large no. of followers
(see Supp. Materials).
component to create
ideology score for
4,176 opinion leaders.
Create bots that
tweet a random
sample of tweets
from the 1-3 (liberal)
and 5-7 (conservative)
quantiles of the
distribution .
Bot #1 Bot #2
Collect Twitter handles
and presidential
Donald Trump
Donald Trump
Steve Daines
Steve Daines
Hillary Clinton
Hillary Clinton
Ivanka Trump
Tim Kaine
Tim Kaine
Ivanka Trump
Sarah Sanders Sarah Sanders
Sarah Sanders
Mike Pence Mike Pence
Mike Pence
Mike Pence
Lisa Murkowski
Lisa Murkowski
Liberal Conservative
(Small Network Component Pictured)
.42 .57
.28 .71 .85
Donald Trump
Donald Trump
Hillary Clinton
Hillary Clinton
Steve Daines
Steve Daines
Mike Pence
Ivanka Trump
Sarah Sanders
Tim Kaine
Lisa Murkowski
Lisa Murkowski
Tucker Carlson
Parenthood Heritage
(Small Network Component Pictured)
Liberal/Conservative Scale
Fig. 2. Design of study’s Twitter bots.
our findings as evidence that exposure to opposing political views
will increase polarization in all settings. Although ours is among
the largest field experiments conducted on social media to date,
the findings above should not be generalized to the entire US
population, because a majority of Americans do not use Twit-
ter (24). It is also unclear how exposure to opposing views might
shape political polarization in other parts of the world. In addi-
tion, we did not study people who identify as independents, or
those who use Twitter but do so infrequently. Such individuals
might exhibit quite different reactions to an intervention such
as ours. Future studies are needed to further evaluate the exter-
nal validity of our findings, because we offered our respondents
4 of 6 | Bail et al.
More ConservativeMore Liberal
More ConservativeMore Liberal
Fig. 3. Effect of following Twitter bots that retweet messages by elected officials, organizations, and opinion leaders with opposing political ideologies for
1 mo, on a seven-point liberal/conservative scale where larger values indicate more conservative opinions about social policy issues, for experiments with
Democrats (n= 697) and Republicans (n= 542). Models predict posttreatment liberal/conservative scale score and control for pretreatment score on this
scale as well as 12 other covariates described in SI Appendix. Circles describe unstandardized point estimates, and bars describe 90% and 95% confidence
intervals. “Respondents Assigned to Treatment” describes the ITT effect for Democrats (ITT = 0.02, t=0.76, p= 0.45, n= 416) and Republicans (ITT = 0.12,
t= 2.68, p= 0.008, n= 316). “Minimally-Compliant Respondents” describes the CACE for respondents who followed one of the study’s bots for Democrats
(CACE = 0.04, t=0.75, p= 0.45, nof compliant respondents = 271) and Republicans (CACE = 0.19, t= 2.73, p<0.007, nof compliant respondents =
181). “Partially-Compliant Respondents” describes the CACE for respondents who correctly answered at least one question, but not all questions, about the
content of a bot’s tweets during weekly surveys throughout the study period for Democrats (CACE = 0.05, t=0.75, p= 0.45, nof compliant respondents =
211) and Republicans (CACE = 0.31, t= 2.73, p<.007, nof compliant respondents = 121). “Fully-Compliant Respondents” describes the CACE for respondents
who answered all questions about the content of the bot’s tweets correctly for Democrats (CACE = 0.14, t=0.75, p= 0.46, nof compliant respondents =
66) and Republicans (CACE = 0.60, t= 2.53, p<0.01, nof compliant respondents = 53). Although treated Democrats exhibited slightly more liberal attitudes
posttreatment that increase in size with level of compliance, none of these effects were statistically significant. In contrast, treated Republicans exhibited
substantially more conservative views posttreatment that increase in size with level of compliance, and these effects are highly significant.
financial incentives to read messages from people or organiza-
tions with opposing views. It is possible that Twitter users may
simply ignore such counterattitudinal messages in the absence
of such incentives. Perhaps the most important limitation of our
study is that we were unable to identify the precise mechanism
that created the backfire effect among Republican respondents
reported above. Future studies are thus urgently needed not only
to determine whether our findings replicate in different popula-
tions or within varied social settings but to further identify the
precise causal pathways that create backfire effects more broadly.
Future studies are also needed because we cannot rule out
all alternative explanations of our findings. In SI Appendix, we
present additional analyses that give us confidence that our results
are not driven by Hawthorne effects, partisan “learning” pro-
cesses, variation in the ideological extremity of messages by party,
or demographic differences in social media use by age. At the
same time, we are unable to rule out other alternative explana-
tions discussed in SI Appendix. For example, it is possible that our
findings resulted from increased exposure to information about
politics, and not exposure to opposing messages per se. Similarly,
increases in conservatism among Republicans may have resulted
from increased exposure to women or racial and ethnic minori-
ties whose messages were retweeted by our liberal bot. Finally,
our intervention only exposed respondents to high-profile elites
with opposing political ideologies. Although our liberal and con-
servative bots randomly selected messages from across the liberal
and conservative spectrum, previous studies indicate such elites
are significantly more polarized than the general electorate (45).
It is thus possible that the backfire effect we identified could be
exacerbated by an antielite bias, and future studies are needed to
examine the effect of online intergroup contact with nonelites.
Despite these limitations, our findings have important impli-
cations for current debates in sociology, political science, social
psychology, communications, and information science. Although
we found no evidence that exposing Twitter users to opposing
views reduces political polarization, our study revealed signif-
icant partisan differences in backfire effects. This finding is
important, since our study examines such effects in an exper-
imental setting that involves repeated contact between rival
groups across an extended time period on social media. Our
field experiment also disrupts selective exposure to informa-
tion about politics in a real-world setting through a combina-
tion of survey research, bot technology, and digital trace data
collection. This methodological innovation enabled us to col-
lect information about the nexus of social media and politics
with high granularity while developing techniques for measuring
treatment compliance, mitigating causal interference, and veri-
fying survey responses with behavioral data—as we discuss in SI
Appendix. Together, we believe these contributions represent an
important advance for the nascent field of computational social
science (46).
Although our findings should not be generalized beyond party-
identified Americans who use Twitter frequently, we note that
recent studies indicate this population has an outsized influence
on the trajectory of public discussion—particularly as the media
itself has come to rely upon Twitter as a source of news and a
window into public opinion (47). Although limited in scope, our
findings may be of interest to those who are working to reduce
political polarization in applied settings. More specifically, our
study indicates that attempts to introduce people to a broad
range of opposing political views on a social media site such as
Twitter might be not only be ineffective but counterproductive—
particularly if such interventions are initiated by liberals. Since
previous studies have produced substantial evidence that inter-
group contact produces compromise and mutual understanding
in other contexts, however, future attempts to reduce political
Bail et al. PNAS Latest Articles |5 of 6
polarization on social media will most likely require learning
which types of messages, tactics, or issue positions are most likely
to create backfire effects and whether others—perhaps deliv-
ered by nonelites or in offline settings—might be more effective
vehicles to bridge America’s partisan divides.
Materials and Methods
See SI Appendix for a detailed description of all materials and methods
used within this study as well as links to our preregistration statement,
replication materials, additional robustness checks, and an extended discus-
sion of alternative explanations of our findings. Our research was approved
by the Institutional Review Boards at Duke University and New York
ACKNOWLEDGMENTS. We thank Paul DiMaggio, Sunshine Hillygus, Gary
King, Fan Li, Arthur Lupia, Brendan Nyhan, and Samantha Luks for helpful
conversations about this study prior to our research. Our work was sup-
ported by the Carnegie Foundation, the Russell Sage Foundation, and the
National Science Foundation.
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... For example, a recent survey by Biondi et al. (2022) presents generalizations of the FJ model and (relevantly) assesses if polarization can occur. A fundamental feature of the FJ model is that individuals are always drawn toward the opinions of their neighbors -but experimental evidence of this feature is inconclusive and contextual (Bail et al., 2018;Balietti et al., 2021). Therefore, there are extensions of the FJ model in which individuals have bounded confidence (Hegselmann et al., 2002) or are even repelled (Cornacchia et al., 2020;Rahaman and Hosein, 2021). ...
... We also note that this paper is limited by the model of opinion dynamics. Experimental research has shown that exposure to differing opinions may increase polarization (Bail et al., 2018). There are recent extensions to the Friedkin-Johnsen model that incorporate opinion repulsion, such as Cornacchia et al. (2020) and Rahaman and Hosein (2021). ...
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This paper studies how a centralized planner can modify the structure of a social or information network to reduce polarization. First, polarization is found to be highly dependent on degree and structural properties of the network. We then formulate the planner's problem under full information, and motivate disagreement-seeking and coordinate descent heuristics. A novel setting for the planner in which the population's innate opinions are adversarially chosen is introduced, and shown to be equivalent to maximization of the Laplacian's spectral gap. We prove bounds for the effectiveness of a strategy that adds edges between vertices on opposite sides of the cut induced by the spectral gap's eigenvector. Finally, these strategies are evaluated on six real-world and synthetic networks. In several networks, we find that polarization can be significantly reduced through the addition of a small number of edges.
... En un contexto de creciente polarización política (Waisbord, 2020), de construcción de relatos alternativos sobre hechos históricos (Koselleck, 2016), de uso de las redes sociales como lugar de confrontación (Bail et al., 2018) y de descortesía (Kaul de Marlangeon y Cordisco, 2014), el debate generado tras las afirmaciones de Vox sobre Lorca permite analizar las dinámicas y los comportamientos de los usuarios ante este tipo de estrategias políticas. ...
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En agosto de 2021, dirigentes del partido de ultraderecha Vox afirman que Federico García Lorca votaría a su fuerza política. El poeta, fusilado en 1936 por franquistas acusado de homosexual, masón y socialista, enterrado en una fosa común es un símbolo de lo español y de la Memoria Histórica que, en España, ha capitalizado la izquierda. La relectura de su figura, la construcción de una nueva narrativa de la memoria en torno a la polémica de Vox y su debate digital es el tema de este trabajo. Así, se pretende medir el impacto que alcanzaron estas afirmaciones en Twitter y la reacción de los usuarios digitales, indagando en la polarización y la desinformación. A través de una metodología de análisis de contenido cuantitativo-cualitativo aplicada a 1.311 tuits publicados sobre este episodio se estudian variables de tono, viralidad, enfoque o presencia de descalificaciones en el discurso. Los resultados avanzan un rechazo de los usuarios ante las declaraciones de Vox, una presencia relevante de perfiles anónimos, así como un alto porcentaje de insultos hacia Vox. También evidencian la politización de la memoria, la creación de una historia virtual interactiva y el avance de la desinformación y el olvido.
... Exposure different viewpoints does relate to polarization, but the type of relationships in the network matter. For example, exposure to opposing political information on social media does not reduce polarization (Bail et al. 2018) because following different Twitter accounts do not provide an opportunity for meaningful dialogue and reducing polarization through mutual understanding. Facciani (2020) found that political heterogeneity within close networks was associated with reduced polarization. ...
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Growing levels of political polarization in the United States have been associated with political homogeneity in the personal networks of American adults. The 2016 Presidential Election in the United States was a polarizing event that may have caused further loss of connections to alters who had different politics. Kinship may protect against loss of politically different ties. Additionally, loss of ties with different political views may be particularly pronounced among LGBTQ+ people as they are more likely to be impacted by public policy decisions compared to their heterosexual counterparts. We analyzed two waves of the University of California, Berkeley Social Networks Study's (UCNets) Main Sample and LGBTQ+ Oversample of older adults that occurred in 2015 and 2017, which provided an opportunity to assess alter loss after the 2016 Presidential Election. When evaluating all adults, we found that politically different alters were more likely to reflect kin ties than partner or friend ties. We also found that politically different kin are less likely to be dropped suggesting that kinship acts as a moderating effect of different political views on alter loss. LGBTQ+ respondents were more likely to drop kin alters with different political views than their cisgender heterosexual counterparts. We discuss the implications these results have for political polarization interventions as well as the social networks impact politics can have on LGBTQ+ individuals.
... Our findings revealed that people with polarized abortion views (i.e., those who indicated strong support of or opposition to abortion) are more rigid or firm in their thinking; people with complex and nuanced views are less rigid or firm in their thinking. These findings align with public opinion literature that speaks to the political polarization in the USA and the growing ideological distance between political parties [40]. Indeed, over the last 50 years, the US electorate has slowly pulled candidates and, by extension, the government to the fringe of either party [41]. ...
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Introduction Although much work has been done on US abortion ideology, less is known relative to the psychological processes that distinguish personal abortion beliefs or how those beliefs are communicated to others. As part of a forthcoming probability-based sampling designed study on US abortion climate, we piloted a study with a controlled sample to determine whether psychological indicators guiding abortion beliefs can be meaningfully extracted from qualitative interviews using natural language processing (NLP) substring matching. Of particular interest to this study is the presence of cognitive distortions—markers of rigid thinking—spoken during interviews and how cognitive distortion frequency may be tied to rigid, or firm, abortion beliefs. Methods We ran qualitative interview transcripts against two lexicons. The first lexicon, the cognitive distortion schemata (CDS), was applied to identify cognitive distortion n-grams (a series of words) embedded within the qualitative interviews. The second lexicon, the Linguistic Inquiry Word Count (LIWC), was applied to extract other psychological indicators, including the degrees of (1) analytic thinking, (2) emotional reasoning, (3) authenticity, and (4) clout. Results People with polarized abortion views (i.e., strongly supportive of or opposed to abortion) had the highest observed usage of CDS n-grams, scored highest on authenticity, and lowest on analytic thinking. By contrast, people with moderate or uncertain abortion views (i.e., people holding more complex or nuanced views of abortion) spoke with the least CDS n-grams and scored slightly higher on analytic thinking. Discussion and conclusion Our findings suggest people communicate about abortion differently depending on their personal abortion ideology. Those with strong abortion views may be more likely to communicate with authoritative words and patterns of words indicative of cognitive distortions—or limited complexity in belief systems. Those with moderate views are more likely to speak in conflicting terms and patterns of words that are flexible and open to change—or high complexity in belief systems. These findings suggest it is possible to extract psychological indicators with NLP from qualitative interviews about abortion. Findings from this study will help refine our protocol ahead of full-study launch.
... This tendency can be conducive to the spread of online hate crime. These findings are in line with observed general trends (Bail et al., 2018;Boxell et al., 2017;Marks et al., 2019) pointing to the polarizing effect of the internet. A plausible policy implication of this finding is that experimental programs could be applied that confront users who are caught in echo chambers and ideologically cocooned networks (Gillani et al., 2018) to mitigate social media's polarization effect. ...
Criminal offending and victimization often overlap in both the virtual and offline worlds. However, scholars are still unsure how the offending victimization relationship plays out between the online and offline worlds. Using a sample of 2,491 adults, four clusters are discovered: 1) those unlikely to have offended or been victimized, 2) those who had online victimization and offending experiences, 3) Those who have been victimized offline and online, but who are unlikely to have offended, and 4) individuals who were victims both online and offline and offended online. Thus, the offending-victimization overlap may be common, but it is certainly not exclusive.
Organizations, activists, and scholars hope that conversations between outpartisans (supporters of opposing political parties) can reduce affective polarization (dislike of outpartisans) and bolster democratic accountability (e.g., support for democratic norms). We argue that such conversations can reduce affective polarization but that these effects are likely to be conditional on topic, being especially likely if the conversations topics avoid discussion of areas of disagreement; usually not persist long-term; and be circumscribed, not affecting attitudes toward democratic accountability. We support this argument with two unique experiments where we paired outpartisan strangers to discuss randomly assigned topics over video calls. In study 1, we found that conversations between outpartisans about their perfect day dramatically decreased affective polarization, although these impacts decayed long-term. Study 2 also included conversations focusing on disagreement (e.g., why each supports their own party), which had no effects. Both studies found little change in attitudes related to democratic accountability.
Civility in political discourse is often thought to be necessary for deliberation and a healthy democracy. However, incivility is on the rise in political discourse in the United States—even from members of Congress—suggesting that political incivility may in fact be a tool to be used strategically. When and why, then, do members of Congress use incivility in their rhetoric? We develop and test expectations for the usage of political incivility by members of Congress on Twitter, using every tweet sent by a member of Congress from 2009–2020 coded for the presence of uncivil rhetoric via a novel application of transformer models for natural language processing. We find that more ideologically extreme members, those in safer electoral situations, and those who are in a position of political opposition are more likely to use incivility in their tweets, and that uncivil tweets increase engagement with members’ messages.
The majority of internet users today find their news on social media (Gil de Zúñiga et al. 2017), however, media trust, and especially trust in social media is low. (Edelman 2019) In growing political polarization the effects of perceived media hostility are also gaining more importance. In this research internet users of international news participated in an online experiment to assess how issue involvement on the 2020 military conflict between the United States and Iran correlates with general trust in the media and with the credibility of the largest social media network, Facebook, as a news source. The current research investigated whether the hostile media effect still occurs in a purely social media context and results showed that partisans (those with a strong supporting or opposing opinion on the military conflict) perceive news content on Facebook as hostile along the same lines as they do in a traditional media context. Current study fills the literature gap of the hostile media effect in a social media context. Findings may also have implications for the news industry as to how journalist roles influence users’ perceptions.
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Can citizens heed factual information, even when such information challenges their partisan and ideological attachments? The “backfire effect,” described by Nyhan and Reifler (Polit Behav 32(2):303–330., 2010), says no: rather than simply ignoring factual information, presenting respondents with facts can compound their ignorance. In their study, conservatives presented with factual information about the absence of Weapons of Mass Destruction in Iraq became more convinced that such weapons had been found. The present paper presents results from five experiments in which we enrolled more than 10,100 subjects and tested 52 issues of potential backfire. Across all experiments, we found no corrections capable of triggering backfire, despite testing precisely the kinds of polarized issues where backfire should be expected. Evidence of factual backfire is far more tenuous than prior research suggests. By and large, citizens heed factual information, even when such information challenges their ideological commitments.
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We demonstrate that exposure to the news media causes Americans to take public stands on specific issues, join national policy conversations, and express themselves publicly—all key components of democratic politics—more often than they would otherwise. After recruiting 48 mostly small media outlets, we chose groups of these outlets to write and publish articles on subjects we approved, on dates we randomly assigned. We estimated the causal effect on proximal measures, such as website pageviews and Twitter discussion of the articles’ specific subjects, and distal ones, such as national Twitter conversation in broad policy areas. Our intervention increased discussion in each broad policy area by ~62.7% (relative to a day’s volume), accounting for 13,166 additional posts over the treatment week, with similar effects across population subgroups.
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In this study we investigate how social media shape the networked public sphere and facilitate communication between communities with different political orientations. We examine two networks of political communication on Twitter, comprised of more than 250,000 tweets from the six weeks leading up to the 2010 U.S. congressional midterm elections. Using a combination of network clustering algorithms and manually-annotated data we demonstrate that the network of political retweets exhibits a highly segregated partisan structure, with extremely limited connectivity between left- and right-leaning users. Surprisingly this is not the case for the user-to-user mention network, which is dominated by a single politically heterogeneous cluster of users in which ideologically-opposed individuals interact at a much higher rate compared to the network of retweets. To explain the distinct topologies of the retweet and mention networks we conjecture that politically motivated individuals provoke interaction by injecting partisan content into information streams whose primary audience consists of ideologically-opposed users. We conclude with statistical evidence in support of this hypothesis.
We combine eight previously proposed measures to construct an index of political polarization among US adults. We find that polarization has increased the most among the demographic groups least likely to use the Internet and social media. Our overall index and all but one of the individual measures show greater increases for those older than 65 than for those aged 18-39. A linear model estimated at the age-group level implies that the Internet explains a small share of the recent growth in polarization.
Existing research depicts intergroup prejudices as deeply ingrained, requiring intense intervention to lastingly reduce. Here, we show that a single approximately 10-minute conversation encouraging actively taking the perspective of others can markedly reduce prejudice for at least 3 months. We illustrate this potential with a door-to-door canvassing intervention in South Florida targeting antitransgender prejudice. Despite declines in homophobia, transphobia remains pervasive. For the intervention, 56 canvassers went door to door encouraging active perspective-taking with 501 voters at voters’ doorsteps. A randomized trial found that these conversations substantially reduced transphobia, with decreases greater than Americans’ average decrease in homophobia from 1998 to 2012. These effects persisted for 3 months, and both transgender and nontransgender canvassers were effective. The intervention also increased support for a nondiscrimination law, even after exposing voters to counterarguments.