Academic Research
CSMaP faculty, postdoctoral fellows, and students publish rigorous, peer-reviewed research in top academic journals and post working papers sharing ongoing work.
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Journal Article
A Synthesis of Evidence for Policy from Behavioural Science During COVID-19
Nature, 2023
Scientific evidence regularly guides policy decisions, with behavioural science increasingly part of this process. In April 2020, an influential paper proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization.
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Book
Computational Social Science for Policy and Quality of Democracy: Public Opinion, Hate Speech, Misinformation, and Foreign Influence Campaigns
Handbook of Computational Social Science for Policy, 2023
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Journal Article
Tweeting Beyond Tahrir: Ideological Diversity and Political Intolerance in Egyptian Twitter Networks
World Politics, 2021
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.
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Journal Article
Political Knowledge and Misinformation in the Era of Social Media: Evidence From the 2015 UK Election
British Journal of Political Science, 2022
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Working Paper
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Book
Social Media and Democracy: The State of the Field, Prospects for Reform
Cambridge University Press, 2020
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Journal Article
Using Social and Behavioral Science to Support COVID-19 Pandemic Response
Nature Human Behavior, 2020
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Journal Article
Political Psycholinguistics: A Comprehensive Analysis of the Language Habits of Liberal and Conservative Social Media Users.
Journal of Personality and Social Psychology, 2020
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Journal Article
Who Leads? Who Follows? Measuring Issue Attention and Agenda Setting by Legislators and the Mass Public Using Social Media Data
American Political Science Review, 2019
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Journal Article
Moral Discourse in the Twitterverse: Effects of Ideology and Political Sophistication on Language Use Among U.S. Citizens and Members of Congress
Journal of Language and Politics, 2018
We analyzed Twitter language to explore hypotheses derived from moral foundations theory, which suggests that liberals and conservatives prioritize different values. In Study 1, we captured 11 million tweets from nearly 25,000 U.S. residents and observed that liberals expressed fairness concerns more often than conservatives, whereas conservatives were more likely to express concerns about group loyalty, authority, and purity. Increasing political sophistication exacerbated ideological differences in authority and group loyalty. At low levels of sophistication, liberals used more harm language, but at high levels of sophistication conservatives referenced harm more often. In Study 2, we analyzed 59,000 tweets from 388 members of the U.S. Congress. Liberal legislators used more fairness- and harm-related words, whereas conservative legislators used more authority-related words. Unexpectedly, liberal legislators used more language pertaining to group loyalty and purity. Follow-up analyses suggest that liberals and conservatives in Congress use similar words to emphasize different policy priorities.
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Book
Measuring Public Opinion with Social Media Data
The Oxford Handbook of Polling and Survey Methods, 2018