Academic Research

As an academic research institute dedicated to studying how social media impacts politics, policy, and democracy, CSMaP publishes peer-reviewed research in top academic journals and produces rigorous data reports on policy relevant topics.

Search or Filter

  • Working Paper

    Echo Chambers, Rabbit Holes, and Algorithmic Bias: How YouTube Recommends Content to Real Users

    Working Paper, May 2022

    View Article View abstract

    To what extent does the YouTube recommendation algorithm push users into echo chambers, ideologically biased content, or rabbit holes? Despite growing popular concern, recent work suggests that the recommendation algorithm is not pushing users into these echo chambers. However, existing research relies heavily on the use of anonymous data collection that does not account for the personalized nature of the recommendation algorithm. We asked a sample of real users to install a browser extension that downloaded the list of videos they were recommended. We instructed these users to start on an assigned video and then click through 20 sets of recommendations, capturing what they were being shown in real time as they used the platform logged into their real accounts. Using a novel method to estimate the ideology of a YouTube video, we demonstrate that the YouTube recommendation algorithm does, in fact, push real users into mild ideological echo chambers where, by the end of the data collection task, liberals and conservatives received different distributions of recommendations from each other, though this difference is small. While we find evidence that this difference increases the longer the user followed the recommendation algorithm, we do not find evidence that many go down `rabbit holes' that lead them to ideologically extreme content. Finally, we find that YouTube pushes all users, regardless of ideology, towards moderately conservative and an increasingly narrow range of ideological content the longer they follow YouTube's recommendations.

    Date Posted

    May 11, 2022

  • Journal Article

    News Credibility Labels Have Limited Average Effects on News Diet Quality and Fail to Reduce Misperceptions

    Science Advances, 2022

    View Article View abstract

    As the primary arena for viral misinformation shifts toward transnational threats, the search continues for scalable countermeasures compatible with principles of transparency and free expression. We conducted a randomized field experiment evaluating the impact of source credibility labels embedded in users’ social feeds and search results pages. By combining representative surveys (n = 3337) and digital trace data (n = 968) from a subset of respondents, we provide a rare ecologically valid test of such an intervention on both attitudes and behavior. On average across the sample, we are unable to detect changes in real-world consumption of news from low-quality sources after 3 weeks. We can also rule out small effects on perceived accuracy of popular misinformation spread about the Black Lives Matter movement and coronavirus disease 2019. However, we present suggestive evidence of a substantively meaningful increase in news diet quality among the heaviest consumers of misinformation. We discuss the implications of our findings for scholars and practitioners.

    Date Posted

    May 06, 2022

  • Working Paper

    Estimating the Ideology of Political YouTube Videos

    Working Paper, May 2022

    View Article View abstract

    We present a method for estimating the ideology of political YouTube videos. As online media increasingly influences how people engage with politics, so does the importance of quantifying the ideology of such media for research. The subfield of estimating ideology as a latent variable has often focused on traditional actors such as legislators, while more recent work has used social media data to estimate the ideology of ordinary users, political elites, and media sources. We build on this work by developing a method to estimate the ideologies of YouTube videos, an important subset of media, based on their accompanying text metadata. First, we take Reddit posts linking to YouTube videos and use correspondence analysis to place those videos in an ideological space. We then train a text-based model with those estimated ideologies as training labels, enabling us to estimate the ideologies of videos not posted on Reddit. These predicted ideologies are then validated against human labels. Finally, we demonstrate the utility of this method by applying it to the watch histories of survey respondents with self-identified ideologies to evaluate the prevalence of echo chambers on YouTube. Our approach gives video-level scores based only on supplied text metadata, is scalable, and can be easily adjusted to account for changes in the ideological climate. This method could also be generalized to estimate the ideology of other items referenced or posted on Reddit.

    Area of Study

    Date Posted

    May 02, 2022

  • Working Paper

    To Moderate, Or Not to Moderate: Strategic Domain Sharing by Congressional Campaigns

    Working Paper, April 2022

    View Article View abstract

    We test whether candidates move to the extremes before a primary but then return to the center for the general election to appeal to the different preferences of each electorate. Incumbents are now more vulnerable to primary challenges than ever as social media offers a viable pathway for fundraising and messaging to challengers, while homogeneity of districts has reduced general election competitiveness. To assess candidates' ideological trajectories, we estimate the revealed ideology of 2020 congressional candidates (incumbents, their primary challengers, and open seat candidates) before and after their primaries, using a homophily-based measure of domains shared on Twitter. This method provides temporally granular data to observe changes in communication within a single election campaign cycle. We find that incumbents did move towards extremes for their primaries and back towards the center for the general election, but only when threatened by a well-funded primary challenge, though non-incumbents did not.

    Date Posted

    Apr 05, 2022

  • Journal Article

    Why Botter: How Pro-Government Bots Fight Opposition in Russia

    American Political Science Review, 2022

    View Article View abstract

    There is abundant anecdotal evidence that nondemocratic regimes are harnessing new digital technologies known as social media bots to facilitate policy goals. However, few previous attempts have been made to systematically analyze the use of bots that are aimed at a domestic audience in autocratic regimes. We develop two alternative theoretical frameworks for predicting the use of pro-regime bots: one which focuses on bot deployment in response to offline protest and the other in response to online protest. We then test the empirical implications of these frameworks with an original collection of Twitter data generated by Russian pro-government bots. We find that the online opposition activities produce stronger reactions from bots than offline protests. Our results provide a lower bound on the effects of bots on the Russian Twittersphere and highlight the importance of bot detection for the study of political communication on social media in nondemocratic regimes.

    Date Posted

    Feb 21, 2022

    Tags

  • Journal Article

    What’s Not to Like? Facebook Page Likes Reveal Limited Polarization in Lifestyle Preferences

    Political Communication, 2021

    View Article View abstract

    Increasing levels of political animosity in the United States invite speculation about whether polarization extends to aspects of daily life. However, empirical study about the relationship between political ideologies and lifestyle choices is limited by a lack of comprehensive data. In this research, we combine survey and Facebook Page “likes” data from more than 1,200 respondents to investigate the extent of polarization in lifestyle domains. Our results indicate that polarization is present in page categories that are somewhat related to politics – such as opinion leaders, partisan news sources, and topics related to identity and religion – but, perhaps surprisingly, it is mostly not evident in other domains, including sports, food, and music. On the individual level, we find that people who are higher in political news interest and have stronger ideological predispositions have a greater tendency to “like” ideologically homogeneous pages across categories. Our evidence, drawn from rare digital trace data covering more than 5,000 pages, adds nuance to the narrative of widespread polarization across lifestyle sectors and it suggests domains in which cross-cutting preferences are still observed in American life.

    Area of Study

    Date Posted

    Nov 25, 2021

  • Journal Article

    Short of Suspension: How Suspension Warnings Can Reduce Hate Speech on Twitter

    Perspectives on Politics, 2021

    View Article View abstract

    Debates around the effectiveness of high-profile Twitter account suspensions and similar bans on abusive users across social media platforms abound. Yet we know little about the effectiveness of warning a user about the possibility of suspending their account as opposed to outright suspensions in reducing hate speech. With a pre-registered experiment, we provide causal evidence that a warning message can reduce the use of hateful language on Twitter, at least in the short term. We design our messages based on the literature on deterrence, and test versions that emphasize the legitimacy of the sender, the credibility of the message, and the costliness of being suspended. We find that the act of warning a user of the potential consequences of their behavior can significantly reduce their hateful language for one week. We also find that warning messages that aim to appear legitimate in the eyes of the target user seem to be the most effective. In light of these findings, we consider the policy implications of platforms adopting a more aggressive approach to warning users that their accounts may be suspended as a tool for reducing hateful speech online.

    Date Posted

    Nov 22, 2021

  • Working Paper

    Network Embedding Methods for Large Networks in Political Science

    Working Paper, November 2021

    View Article View abstract

    Social networks play an important role in many political science studies. With the rise of social media, these networks have grown in both size and complexity. Analysis of these large networks requires generation of feature representations that can be used in machine learning models. One way to generate these feature representations is to use network embedding methods for learning low-dimensional feature representations of nodes and edges in a network. While there is some literature comparing the advantages and shortcomings of these models, to our knowledge, there has not been any analysis on the applicability of network embedding models to classification tasks in political science. In this paper, we compare the performance of five prominent network embedding methods on prediction of ideology of Twitter users and ideology of Internet domains. We find that LINE provides the best feature representation across all 4 datasets that we use, resulting in the highest performance accuracy. Finally, we provide the guidelines for researchers on the use of these models for their own research.

    Area of Study

    Date Posted

    Nov 12, 2021

    Tags

  • Journal Article

    Moderating with the Mob: Evaluating the Efficacy of Real-Time Crowdsourced Fact-Checking

    Journal of Online Trust and Safety, 2021

    View Article View abstract

    Reducing the spread of false news remains a challenge for social media platforms, as the current strategy of using third-party fact- checkers lacks the capacity to address both the scale and speed of misinformation diffusion. Research on the “wisdom of the crowds” suggests one possible solution: aggregating the evaluations of ordinary users to assess the veracity of information. In this study, we investigate the effectiveness of a scalable model for real-time crowdsourced fact-checking. We select 135 popular news stories and have them evaluated by both ordinary individuals and professional fact-checkers within 72 hours of publication, producing 12,883 individual evaluations. Although we find that machine learning-based models using the crowd perform better at identifying false news than simple aggregation rules, our results suggest that neither approach is able to perform at the level of professional fact-checkers. Additionally, both methods perform best when using evaluations only from survey respondents with high political knowledge, suggesting reason for caution for crowdsourced models that rely on a representative sample of the population. Overall, our analyses reveal that while crowd-based systems provide some information on news quality, they are nonetheless limited—and have significant variation—in their ability to identify false news.

    Date Posted

    Oct 28, 2021

  • Journal Article

    SARS-CoV-2 RNA Concentrations in Wastewater Foreshadow Dynamics and Clinical Presentation of New COVID-19 Cases

    • Fuqing Wu, 
    • Amy Xiao, 
    • Jianbo Zhang, 
    • Katya Moniz, 
    • Noriko Endo, 
    • Federica Armas, 
    • Richard Bonneau
    • Megan A. Brown
    • Mary Bushman, 
    • Peter R. Chai, 
    • Claire Duvallet, 
    • Timothy B. Erickson, 
    • Katelyn Foppe, 
    • Newsha Ghaeli, 
    • Xiaoqiong Gu, 
    • William P. Hanage, 
    • Katherine H. Huang, 
    • Wei Lin Lee, 
    • Mariana Matus, 
    • Kyle A. McElroy, 
    • Jonathan Nagler
    • Steven F. Rhode, 
    • Mauricio Santillana, 
    • Joshua A. Tucker
    • Stefan Wuertz, 
    • Shijie Zhao, 
    • Janelle Thompson, 
    • Eric J. Alm

    Science of the Total Environment, 2022

    View Article View abstract

    Current estimates of COVID-19 prevalence are largely based on symptomatic, clinically diagnosed cases. The existence of a large number of undiagnosed infections hampers population-wide investigation of viral circulation. Here, we quantify the SARS-CoV-2 concentration and track its dynamics in wastewater at a major urban wastewater treatment facility in Massachusetts, between early January and May 2020. SARS-CoV-2 was first detected in wastewater on March 3. SARS-CoV-2 RNA concentrations in wastewater correlated with clinically diagnosed new COVID-19 cases, with the trends appearing 4–10 days earlier in wastewater than in clinical data. We inferred viral shedding dynamics by modeling wastewater viral load as a convolution of back-dated new clinical cases with the average population-level viral shedding function. The inferred viral shedding function showed an early peak, likely before symptom onset and clinical diagnosis, consistent with emerging clinical and experimental evidence. This finding suggests that SARS-CoV-2 concentrations in wastewater may be primarily driven by viral shedding early in infection. This work shows that longitudinal wastewater analysis can be used to identify trends in disease transmission in advance of clinical case reporting, and infer early viral shedding dynamics for newly infected individuals, which are difficult to capture in clinical investigations.

    Area of Study

    Date Posted

    Sep 14, 2021

  • Journal Article

    Twitter Flagged Donald Trump’s Tweets with Election Misinformation: They Continued to Spread Both On and Off the Platform

    Harvard Kennedy School (HKS) Misinformation Review, 2021

    View Article View abstract

    We analyze the spread of Donald Trump’s tweets that were flagged by Twitter using two intervention strategies—attaching a warning label and blocking engagement with the tweet entirely. We find that while blocking engagement on certain tweets limited their diffusion, messages we examined with warning labels spread further on Twitter than those without labels. Additionally, the messages that had been blocked on Twitter remained popular on Facebook, Instagram, and Reddit, being posted more often and garnering more visibility than messages that had either been labeled by Twitter or received no intervention at all. Taken together, our results emphasize the importance of considering content moderation at the ecosystem level.

  • Journal Article

    Testing the Effects of Facebook Usage in an Ethnically Polarized Setting

    Proceedings of the National Academy of Sciences, 2021

    View Article View abstract

    Despite the belief that social media is altering intergroup dynamics—bringing people closer or further alienating them from one another—the impact of social media on interethnic attitudes has yet to be rigorously evaluated, especially within areas with tenuous interethnic relations. We report results from a randomized controlled trial in Bosnia and Herzegovina (BiH), exploring the effects of exposure to social media during 1 wk around genocide remembrance in July 2019 on a set of interethnic attitudes of Facebook users. We find evidence that, counter to preregistered expectations, people who deactivated their Facebook profiles report lower regard for ethnic outgroups than those who remained active. Moreover, we present additional evidence suggesting that this effect is likely conditional on the level of ethnic heterogeneity of respondents’ residence. We also extend the analysis to include measures of subjective well-being and knowledge of news. Here, we find that Facebook deactivation leads to suggestive improvements in subjective wellbeing and a decrease in knowledge of current events, replicating results from recent research in the United States in a very different context, thus increasing our confidence in the generalizability of these effects.

    Area of Study

    Date Posted

    Jun 22, 2021

  • Journal Article

    Accessibility and Generalizability: Are Social Media Effects Moderated by Age or Digital Literacy?

    Research & Politics, 2021

    View Article View abstract

    An emerging empirical regularity suggests that older people use and respond to social media very differently than younger people. Older people are the fastest-growing population of Internet and social media users in the U.S., and this heterogeneity will soon become central to online politics. However, many important experiments in this field have been conducted on online samples that do not contain enough older people to be useful to generalize to the current population of Internet users; this issue is more pronounced for studies that are even a few years old. In this paper, we report the results of replicating two experiments involving social media (specifically, Facebook) conducted on one such sample lacking older users (Amazon’s Mechanical Turk) using a source of online subjects which does contain sufficient variation in subject age. We add a standard battery of questions designed to explicitly measure digital literacy. We find evidence of significant treatment effect heterogeneity in subject age and digital literacy in the replication of one of the two experiments. This result is an example of limitations to generalizability of research conducted on samples where selection is related to treatment effect heterogeneity; specifically, this result indicates that Mechanical Turk should not be used to recruit subjects when researchers suspect treatment effect heterogeneity in age or digital literacy, as we argue should be the case for research on digital media effects.

    Area of Study

    Date Posted

    Jun 09, 2021

  • Journal Article

    Cracking Open the News Feed: Exploring What U.S. Facebook Users See and Share with Large-Scale Platform Data

    Journal of Quantitative Description: Digital Media, 2021

    View Article View abstract

    In this study, we analyze for the first time newly available engagement data covering millions of web links shared on Facebook to describe how and by which categories of U.S. users different types of news are seen and shared on the platform. We focus on articles from low-credibility news publishers, credible news sources, purveyors of clickbait, and news specifically about politics, which we identify through a combination of curated lists and supervised classifiers. Our results support recent findings that more fake news is shared by older users and conservatives and that both viewing and sharing patterns suggest a preference for ideologically congenial misinformation. We also find that fake news articles related to politics are more popular among older Americans than other types, while the youngest users share relatively more articles with clickbait headlines. Across the platform, however, articles from credible news sources are shared over 5.5 times more often and viewed over 7.5 times more often than articles from low-credibility sources. These findings offer important context for researchers studying the spread and consumption of information — including misinformation — on social media.

    Date Posted

    Apr 26, 2021

  • Journal Article

    The Times They Are Rarely A-Changin': Circadian Regularities in Social Media Use

    Journal of Quantitative Description: Digital Media, 2021

    View Article View abstract

    This paper uses geolocated Twitter histories from approximately 25,000 individuals in 6 different time zones and 3 different countries to construct a proper time-zone dependent hourly baseline for social media activity studies.  We establish that, across multiple regions and time periods, interaction with social media is strongly conditioned by traditional bio-rhythmic or “Circadian” patterns, and that in the United States, this pattern is itself further conditioned by the ideological bent of the user. Using a time series of these histories around the 2016 U.S. Presidential election, we show that external events of great significance can disrupt traditional social media activity patterns, and that this disruption can be significant (in some cases doubling the amplitude and shifting the phase of activity up to an hour). We find that the disruption of use patterns can last an extended period of time, and in many cases, aspects of this disruption would not be detected without a circadian baseline.

    Area of Study

    Date Posted

    Apr 26, 2021

  • Journal Article

    YouTube Recommendations and Effects on Sharing Across Online Social Platforms

    Proceedings of the ACM on Human-Computer Interaction, 2021

    View Article View abstract

    In January 2019, YouTube announced it would exclude potentially harmful content from video recommendations but allow such videos to remain on the platform. While this step intends to reduce YouTube's role in propagating such content, continued availability of these videos in other online spaces makes it unclear whether this compromise actually reduces their spread. To assess this impact, we apply interrupted time series models to measure whether different types of YouTube sharing in Twitter and Reddit changed significantly in the eight months around YouTube's announcement. We evaluate video sharing across three curated sets of potentially harmful, anti-social content: a set of conspiracy videos that have been shown to experience reduced recommendations in YouTube, a larger set of videos posted by conspiracy-oriented channels, and a set of videos posted by alternative influence network (AIN) channels. As a control, we also evaluate effects on video sharing in a dataset of videos from mainstream news channels. Results show conspiracy-labeled and AIN videos that have evidence of YouTube's de-recommendation experience a significant decreasing trend in sharing on both Twitter and Reddit. For videos from conspiracy-oriented channels, however, we see no significant effect in Twitter but find a significant increase in the level of conspiracy-channel sharing in Reddit. For mainstream news sharing, we actually see an increase in trend on both platforms, suggesting YouTube's suppressing particular content types has a targeted effect. This work finds evidence that reducing exposure to anti-social videos within YouTube, without deletion, has potential pro-social, cross-platform effects. At the same time, increases in the level of conspiracy-channel sharing raise concerns about content producers' responses to these changes, and platform transparency is needed to evaluate these effects further.

    Date Posted

    Apr 22, 2021

  • Journal Article

    Tweeting Beyond Tahrir: Ideological Diversity and Political Intolerance in Egyptian Twitter Networks

    World Politics, 2021

    View Article View abstract

    Do online social networks affect political tolerance in the highly polarized climate of postcoup Egypt? Taking advantage of the real-time networked structure of Twitter data, the authors find that not only is greater network diversity associated with lower levels of intolerance, but also that longer exposure to a diverse network is linked to less expression of intolerance over time. The authors find that this relationship persists in both elite and non-elite diverse networks. Exploring the mechanisms by which network diversity might affect tolerance, the authors offer suggestive evidence that social norms in online networks may shape individuals’ propensity to publicly express intolerant attitudes. The findings contribute to the political tolerance literature and enrich the ongoing debate over the relationship between online echo chambers and political attitudes and behavior by providing new insights from a repressive authoritarian context.

  • Journal Article

    Political Psychology in the Digital (mis)Information age: A Model of News Belief and Sharing

    Social Issues and Policy Review, 2021

    View Article View abstract

    The spread of misinformation, including “fake news,” propaganda, and conspiracy theories, represents a serious threat to society, as it has the potential to alter beliefs, behavior, and policy. Research is beginning to disentangle how and why misinformation is spread and identify processes that contribute to this social problem. We propose an integrative model to understand the social, political, and cognitive psychology risk factors that underlie the spread of misinformation and highlight strategies that might be effective in mitigating this problem. However, the spread of misinformation is a rapidly growing and evolving problem; thus scholars need to identify and test novel solutions, and work with policymakers to evaluate and deploy these solutions. Hence, we provide a roadmap for future research to identify where scholars should invest their energy in order to have the greatest overall impact.

    Date Posted

    Jan 22, 2021

  • Journal Article

    You Won’t Believe Our Results! But They Might: Heterogeneity in Beliefs About the Accuracy of Online Media

    Journal of Experimental Political Science, 2021

    View Article View abstract

    “Clickbait” media has long been espoused as an unfortunate consequence of the rise of digital journalism. But little is known about why readers choose to read clickbait stories. Is it merely curiosity, or might voters think such stories are more likely to provide useful information? We conduct a survey experiment in Italy, where a major political party enthusiastically embraced the esthetics of new media and encouraged their supporters to distrust legacy outlets in favor of online news. We offer respondents a monetary incentive for correct answers to manipulate the relative salience of the motivation for accurate information. This incentive increases differences in the preference for clickbait; older and less educated subjects become even more likely to opt to read a story with a clickbait headline when the incentive to produce a factually correct answer is higher. Our model suggests that a politically relevant subset of the population prefers Clickbait Media because they trust it more.

    Date Posted

    Jan 20, 2021

    Tags

  • Journal Article

    Trumping Hate on Twitter? Online Hate Speech in the 2016 U.S. Election Campaign and its Aftermath.

    Quarterly Journal of Political Science, 2021

    View Article View abstract

    To what extent did online hate speech and white nationalist rhetoric on Twitter increase over the course of Donald Trump's 2016 presidential election campaign and its immediate aftermath? The prevailing narrative suggests that Trump's political rise — and his unexpected victory — lent legitimacy to and popularized bigoted rhetoric that was once relegated to the dark corners of the Internet. However, our analysis of over 750 million tweets related to the election, in addition to almost 400 million tweets from a random sample of American Twitter users, provides systematic evidence that hate speech did not increase on Twitter over this period. Using both machine-learning-augmented dictionary-based methods and a novel classification approach leveraging data from Reddit communities associated with the alt-right movement, we observe no persistent increase in hate speech or white nationalist language either over the course of the campaign or in the six months following Trump's election. While key campaign events and policy announcements produced brief spikes in hateful language, these bursts quickly dissipated. Overall we find no empirical support for the proposition that Trump's divisive campaign or election increased hate speech on Twitter.

    Date Posted

    Jan 11, 2021

  • Data Report
  • Journal Article

    Political Knowledge and Misinformation in the Era of Social Media: Evidence From the 2015 UK Election

    British Journal of Political Science, 2022

    View Article View abstract

    Does social media educate voters, or mislead them? This study measures changes in political knowledge among a panel of voters surveyed during the 2015 UK general election campaign while monitoring the political information to which they were exposed on the Twitter social media platform. The study's panel design permits identification of the effect of information exposure on changes in political knowledge. Twitter use led to higher levels of knowledge about politics and public affairs, as information from news media improved knowledge of politically relevant facts, and messages sent by political parties increased knowledge of party platforms. But in a troubling demonstration of campaigns' ability to manipulate knowledge, messages from the parties also shifted voters' assessments of the economy and immigration in directions favorable to the parties' platforms, leaving some voters with beliefs further from the truth at the end of the campaign than they were at its beginning.

  • Data Report

    Influential Users in the Common Core and Black Lives Matter Social Media Conversation

    Data Report, NYU's Center for Social Media and Politics, 2020

    View Report View abstract

    This Data Report aims to map the pathways leading to issue politicization through identifying influential users within politically contentious topics on Twitter, using the online discussion over the Common Core State Standards (CCSS) and the Black Lives Matter (BLM) movement. We find that politically motivated popular users are the most influential users in both CCSS and BLM online conversations.

    Date Posted

    Dec 16, 2020

  • Working Paper

    News Sharing on Social Media: Mapping the Ideology of News Media Content, Citizens, and Politicians

    Working Paper, November 2020

    View Article View abstract

    This article examines the news sharing behavior of politicians and ordinary users by mapping the ideological sharing space of political information on social media. As data, we use the near-universal currency of online political information exchange: URLs (i.e. web links). We introduce a methodological approach (and statistical software) that unifies the measurement of political ideology online, using social media sharing data to jointly estimate the ideology of: (1) politicians; (2) social media users, and (3) the news sources that they share online. Second, we validate the measure by comparing it to well-known measures of roll call voting behavior for members of congress. Third, we show empirically that legislators who represent less competitive districts are more likely to share politically polarizing news than legislators with similar voting records in more competitive districts. Finally, we demonstrate that it is nevertheless not politicians, but ordinary users who share the most ideologically extreme content and contribute most to the polarized online news-sharing ecosystem. Our approach opens up many avenues for research into the communication strategies of elites, citizens, and other actors who seek to influence political behavior and sway public opinion by sharing political information online.

  • Journal Article

    Political Sectarianism in America

    • Eli J. Finkel, 
    • Christopher A. Bail, 
    • Mina Cikara, 
    • Peter H. Ditto, 
    • Shanto Iyengar, 
    • Samara Klar, 
    • Lilliana Mason, 
    • Mary C. McGrath, 
    • Brendan Nyhan, 
    • David G. Rand, 
    • Linda J. Skitka, 
    • Joshua A. Tucker
    • Jay J. Van Bavel
    • Cynthia S. Wang, 
    • James N. Druckman

    Science, 2020

    View Article View abstract

    Political polarization, a concern in many countries, is especially acrimonious in the United States. For decades, scholars have studied polarization as an ideological matter — how strongly Democrats and Republicans diverge vis-à-vis political ideals and policy goals. Such competition among groups in the marketplace of ideas is a hallmark of a healthy democracy. But more recently, researchers have identified a second type of polarization, one focusing less on triumphs of ideas than on dominating the abhorrent supporters of the opposing party. This literature has produced a proliferation of insights and constructs but few interdisciplinary efforts to integrate them. We offer such an integration, pinpointing the superordinate construct of political sectarianism and identifying its three core ingredients: othering, aversion, and moralization. We then consider the causes of political sectarianism and its consequences for U.S. society — especially the threat it poses to democracy. Finally, we propose interventions for minimizing its most corrosive aspects.

    Area of Study

    Date Posted

    Oct 30, 2020