Studying Polarization, Misinformation, and Manipulation across Multiple Platforms and the Larger Information Ecosystem

New York University


Malevolent actors have exploited online platforms to spread misinformation and influence political processes. Though platforms are responding to these concerns, responses are rarely coordinated, and addressing issues on a single platform does not address the underlying threats. Rather, a macro view across these platforms is critical since users engage and information spreads across multiple platforms. We therefore propose a study of political, polarizing, and manipulative content spread across the social media information ecosystem in the context of the 2018 US midterm election; the models and methods we develop are applicable to other electoral contexts as well (e.g., the 2018 Mexican election). Building on communications theory, social contagion, and complex network formation, we will test hypotheses and research questions about how this content spreads across platforms by combining existing SMaPP lab data collections with analyses from the Social Science One Facebook URLs dataset.

Research Team

Principal Investigator

Joshua Tucker

Professor of Politics, Affiliated Professor of Russian and Slavic Studies, and Affiliated Professor of Data Science, New York University

  • Bio ▾

    Joshua A. Tucker is a professor of politics, affiliated professor of Russian and Slavic studies, and affiliated professor of data science at New York University. He is codirector of the NYU Social Media and Political Participation (SMaPP) laboratory, director of NYU’s Jordan Center for Advanced Study of Russia, and a coauthor/editor of the award-winning politics and policy blog the Monkey Cage at the Washington Post. He serves on the advisory boards of the American National Election Study, the Comparative Study of Electoral Systems, and numerous academic journals. His original research was on mass political behavior in post-communist countries, including voting and elections, partisanship, public opinion formation, and protest participation. More recently, he has focused on the relationship between social media and politics. His research in this area has included studies on the effects of network diversity on tolerance, partisan echo chambers, online hate speech, the effects of exposure to social media on political knowledge, online networks and protest, disinformation and fake news, how authoritarian regimes respond to online opposition, and Russian bots and trolls. His research has appeared in over two dozen scholarly journals, and he is the coauthor of Communism’s Shadow: Historical Legacies and Contemporary Political Attitudes (Princeton University Press, 2017).


Richard Bonneau

Professor of Biology and Computer Science, New York University

  • Bio ▾

    Richard Bonneau focuses on three main areas of data science: (1) systems biology, e.g., learning biological networks from genomics data, (2) designing and predicting protein and protein-mimetic molecular structure, and (3) computational social science with a focus on social network enabled science. In the area of genomics and systems biology he has played key roles in achieving critical field-wide milestones. In the area of structure prediction, he was an initial author on the state-of-the art Rosetta Code, which was the first code to demonstrate accurate and comprehensive ability to predict protein structure in the absence of sequence homology. Dr. Bonneau is a codirector of the SMaPP lab at NYU. His expertise in data science, leading large-scale systems biology consortia motivates many contributions to SMaPP lab. His experience with lab-based science, industry collaboration, and network science are key to SMaPP lab’s innovative construction. Dr. Bonneau was selected by Discover magazine as one of the top 20 scientific minds under 40, and a recent review in the top biology journal, Cell, lists Dr. Bonneau's 2007 paper on the prediction of global dynamic regulatory networks as a landmark paper in field of systems biology. Dr. Bonneau is a founding member of the Flatiron Institute, a new large-scale effort to create an intramural data science center at the Simons Foundation. Dr. Bonneau is a PI on the initial Moore-Sloan data science environments grant and part of the group of faculty at NYU that created the new Center for Data Science at NYU.

Cody Buntain

Postdoctoral Researcher, SMaPP Lab, New York University

  • Bio ▾

    Cody Buntain is a postdoctoral researcher with SMaPP. His primary research areas exist at the intersection of data science in social media and the social sciences, specifically how individuals engage socially and politically and respond to crises and disaster in online spaces. Current problems he is studying include cross-platform information flows, temporal evolution/politicization of topics, misinformation, and information/interaction quality. Recent publications include papers on influencing credibility assessment in social media, consistencies in social media's response to crises, and characterizing gender and directedness in online harassment.

Andrew Guess

Assistant Professor of Politics and Public Affairs, Princeton University

  • Bio ▾

    Andy Guess (PhD, Columbia University) is an assistant professor of politics and public affairs at Princeton University. His research and teaching interests lie at the intersection of political communication, public opinion, and political behavior.

    Via a combination of experimental methods, large datasets, machine learning, and innovative measurement, he studies how people choose, process, spread, and respond to information about politics. Recent work investigates the extent to which online Americans' news habits are polarized (the popular "echo chambers" hypothesis), patterns in the consumption and spread of online misinformation, and the effectiveness of efforts to counteract misperceptions encountered on social media. Coverage of these findings has appeared in the New York Times, the New Yorker, Slate, the Chronicle of Higher Education, and other publications.

    His research has been supported by grants from the Volkswagen Foundation, Russell Sage Foundation, and American Press Institute and published in peer-reviewed journals such as the American Journal of Political Science, Political Analysis, and Science Advances.

Jonathan Nagler

Professor of Politics, New York University (Codirector of Lab)

  • Bio ▾

    Jonathan Nagler is a professor of politics and affiliated faculty at the Center of Data Science at NYU. He is a codirector of the NYU Social Media and Political Participation Laboratory. Nagler is a past president of the Society for Political Methodology, as well as an inaugural fellow of the Society for Political Methodology. Professor Nagler's research focuses on voting and elections, and the role of social media, as well as traditional media, in politics. He has been at the forefront of computational social science for many years, and pioneered innovative methods for analysis of discrete choice problems. Nagler has produced recent papers on the nature of online ideological media consumption of individuals, the amount of hate speech on Twitter, the impact of exposure to online information on knowledge of politics and political attitudes, and the impact of media coverage of the economy on economic perceptions. Several of these papers have combined survey data with social media consumption in novel ways. Nagler has been a Fernand Braudel Senior Fellow at the European University Institute, a visiting scholar at the Russell Sage Foundation, and has taught at Harvard and Caltech. He is a coauthor of Who Votes Now? (Princeton University Press, 2014).

Megan Brown

Research Engineer, New York University

  • Bio ▾

    Megan Brown is a research engineer and data scientist at SMaPP. She is especially interested in studying cross-platform media manipulation, understanding bias in machine learning and AI, and the effect of computational information recommendation systems on political information and behavior.

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