Douglas Guilbeault studies computational social science with Damon Centola in the Network Dynamics Group. He uses mathematical models and online experiments to study how communication in social networks shapes the way people categorize the world.
His recent study in the Proceedings of the National Academy of the Sciences shows how communication in bipartisan networks can reduce polarization via collective intelligence, but that this polarization endures if people are primed to process information in terms of political social categories (e.g. party membership). This paper was given the best paper award at the 2018 International Conference on Computational Social Science.
Guilbeault's dissertation explores how category systems emerge in the first place, using online experiments involving the dynamic construction of novel categories in large-scale social networks. This research has been awarded the "Computational Modeling Prize in Applied Cognition" at the 41st Annual Conference of the Cognitive Science Society (Cogsci 2019).
Guilbeault also engages in policy-related research surrounding social media. In a recent article in Policy & Internet, he provides an overview of the policy issues created by emerging misinformation technologies such as bots. This paper was awarded the top student paper award at the 2018 conference of the International Communication Association, in the Technology and Communication Division.
Guilbeault has examined the political implications of digital media elsewhere in the International Journal of Communication and the Journal of International Affairs. He has also written several journalistic pieces on these topics for outlets including The Atlantic and Wired. This research is supported by collaboration with the Annenberg Public Policy Center and the Computational Propaganda Project at the Oxford Internet Institute.
Guilbeault's research is funded by a Joseph-Armand Bombardier doctoral scholarship from the Social Sciences and Humanities Research Council of Canada, by a dissertation fellowship from the Institute for Research on Innovation and Science (UMich), and by Facebook's Content Moderation Research Initiative.