Academic Research

CSMaP is a leading academic research institute studying the ever-shifting online environment at scale. We publish peer-reviewed research in top academic journals and produce rigorous data reports on policy relevant topics.

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  • Working Paper

    Social Media, Information, and Politics: Insights on Latinos in the U.S.

    Working Paper, November 2022

    View Article View abstract

    Social media is used by millions of Americans to acquire political news and information. Most of this research has focused on understanding the way social media consumption affects the political behavior and preferences of White Americans. Much less is known about Latinos’ political activity on social media, who are not only the largest racial/ethnic minority group in the U.S., but they also continue to exhibit diverse political preferences. Moreover, about 30% of Latinos rely primarily on Spanish-language news sources (Spanish-dominant Latinos) and another 30% are bilingual. Given that Spanish-language social media is not as heavily monitored for misinformation than its English-language counterparts (Valencia, 2021; Paul, 2021), Spanish-dominant Latinos who rely on social media for news may be more susceptible to political misinformation than those Latinos who are exposed to English-language social media. We address this contention by fielding an original study that sampled a large number of Latino and White respondents. Consistent with our expectations, Latinos who rely on Spanish-language social media are more likely to believe in election fraud than those who use both English and Spanish social media new sources. We also find that Latinos engage in more political activities on social media when compared to White Americans, particularly on their social media of choice, WhatsApp.

  • Journal Article

    Dictionary-Assisted Supervised Contrastive Learning

    Working Paper, October 2022

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    Text analysis in the social sciences often involves using specialized dictionaries to reason with abstract concepts, such as perceptions about the economy or abuse on social media. These dictionaries allow researchers to impart domain knowledge and note subtle usages of words relating to a concept(s) of interest. We introduce the dictionary-assisted supervised contrastive learning (DASCL) objective, allowing researchers to leverage specialized dictionaries when fine-tuning pretrained language models. The text is first keyword simplified: a common, fixed token replaces any word in the corpus that appears in the dictionary(ies) relevant to the concept of interest. During fine-tuning, a supervised contrastive objective draws closer the embeddings of the original and keyword-simplified texts of the same class while pushing further apart the embeddings of different classes. The keyword-simplified texts of the same class are more textually similar than their original text counterparts, which additionally draws the embeddings of the same class closer together. Combining DASCL and cross-entropy improves classification performance metrics in few-shot learning settings and social science applications compared to using cross-entropy alone and alternative contrastive and data augmentation methods.

    Area of Study

    Date Posted

    Oct 27, 2022

  • Journal Article

    Using Social Media Data to Reveal Patterns of Policy Engagement in State Legislatures

    State Politics & Policy Quarterly, 2022

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    State governments are tasked with making important policy decisions in the United States. How do state legislators use their public communications—particularly social media—to engage with policy debates? Due to previous data limitations, we lack systematic information about whether and how state legislators publicly discuss policy and how this behavior varies across contexts. Using Twitter data and state-of-the-art topic modeling techniques, we introduce a method to study state legislator policy priorities and apply the method to 15 US states in 2018. We show that we are able to accurately capture the policy issues discussed by state legislators with substantially more accuracy than existing methods. We then present initial findings that validate the method and speak to debates in the literature. The paper concludes by discussing promising avenues for future state politics research using this new approach.

    Date Posted

    Oct 18, 2022

  • Journal Article

    Most Users Do Not Follow Political Elites on Twitter; Those Who Do, Show Overwhelming Preferences for Ideological Congruity.

    Science Advances, 2022

    View Article View abstract

    We offer comprehensive evidence of preferences for ideological congruity when people engage with politicians, pundits, and news organizations on social media. Using four years of data (2016-2019) from a random sample of 1.5 million Twitter users, we examine three behaviors studied separately to date: (a) following of in-group vs. out-group elites, (b) sharing in-group vs. out-group information (retweeting), and (c) commenting on the shared information (quote tweeting). We find the majority of users (60%) do not follow any political elites. Those who do, follow in-group elite accounts at much higher rates than out-group accounts (90% vs. 10%), share information from in-group elites 13 times more frequently than from out-group elites, and often add negative comments to the shared out-group information. Conservatives are twice as likely as liberals to share in-group vs. out-group content. These patterns are robust, emerge across issues and political elites, and regardless of users' ideological extremity.

    Date Posted

    Sep 30, 2022

  • Journal Article

    Election Fraud, YouTube, and Public Perception of the Legitimacy of President Biden

    Journal of Online Trust and Safety, 2022

    View Article View abstract

    Skepticism about the outcome of the 2020 presidential election in the United States led to a historic attack on the Capitol on January 6th, 2021 and represents one of the greatest challenges to America's democratic institutions in over a century. Narratives of fraud and conspiracy theories proliferated over the fall of 2020, finding fertile ground across online social networks, although little is know about the extent and drivers of this spread. In this article, we show that users who were more skeptical of the election's legitimacy were more likely to be recommended content that featured narratives about the legitimacy of the election. Our findings underscore the tension between an "effective" recommendation system that provides users with the content they want, and a dangerous mechanism by which misinformation, disinformation, and conspiracies can find their way to those most likely to believe them.

    Date Posted

    Sep 01, 2022

  • Journal Article

    What We Learned About The Gateway Pundit from its Own Web Traffic Data

    Workshop Proceedings of the 16th International AAAI Conference on Web and Social Media, 2022

    View Article View abstract

    To mitigate the spread of false news, researchers need to understand who visits low-quality news sites, what brings people to those sites, and what content they prefer to consume. Due to challenges in observing most direct website traffic, existing research primarily relies on alternative data sources, such as engagement signals from social media posts. However, such signals are at best only proxies for actual website visits. During an audit of far-right news websites, we discovered that The Gateway Pundit (TGP) has made its web traffic data publicly available, giving us a rare opportunity to understand what news pages people actually visit. We collected 68 million web traffic visits to the site over a one-month period and analyzed how people consume news via multiple features. Our referral analysis shows that search engines and social media platforms are the main drivers of traffic; our geo-location analysis reveals that TGP is more popular in counties where more people voted for Trump in 2020. In terms of content, topics related to 2020 US presidential election and 2021 US capital riot have the highest average number of visits. We also use these data to quantify to what degree social media engagement signals correlate with actual web visit counts. To do so, we collect Facebook and Twitter posts with URLs from TGP during the same time period. We show that all engagement signals positively correlate with web visit counts, but with varying correlation strengths. For example, total interaction on Facebook correlates better than Twitter retweet count. Our insights can also help researchers choose the right metrics when they measure the impact of news URLs on social media.

    Date Posted

    Jun 01, 2022

  • Working Paper

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

    Working Paper, May 2022

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    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

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    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

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    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

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    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

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

    Political Communication, 2021

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    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

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    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

  • 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

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

    Research & Politics, 2021

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    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

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    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

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    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

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

    Social Issues and Policy Review, 2021

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    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

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

    Quarterly Journal of Political Science, 2021

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    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
  • 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

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    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

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