Policymakers and the public are increasingly interested in the effects of social media algorithms on society. In this talk some of the challenges this topic poses to researchers will be outlined. Two different approaches to studying these systems’ effects on individuals will be introduced. One is a large-scale collaboration with a social platform designed to coincide with the 2020 U.S. presidential campaign, and the other is an experimental research design that can be adapted to assess the impact of proprietary recommendation systems. The talk will conclude with a discussion of lessons learned from these studies and the possibilities for future projects building on this work.
Andy Guess 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 VolkswagenStiftung, the Russell Sage Foundation, and the National Science Foundation and published in peer-reviewed journals such as Nature Human Behaviour, Political Analysis, and Proceedings of the National Academy of Sciences.
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This talk will not be recorded.