Using Machine Learning to Infer Real-world Political Attitudes and Behaviors from Social Media Data
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2023-02-24
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Indiana University Workshop in Methods
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Abstract
Social media has become a primary means of communication and personal expression for many, and digital trace data from social media platforms can contain rich and extensive archives of individuals’ attitudes, beliefs, and actions. But even though these data are increasingly plentiful and available to social science researchers, the process of extracting meaningful measures of individual-level attributes from large collections of social media data is nontrivial. In this talk, Computational Social Scientist, Sam Bestvater, will draw from his research on political engagement in online spaces and its impacts on real-world behaviors to discuss how machine learning algorithms can be used to analyze large amounts of social media data and extract insights into political attitudes and activities. Along the way, the talk will introduce several recent innovations in natural language processing and computer vision, and will discuss some potential challenges and limitations of using these tools and data sources for political research, as well as ethical considerations that should be taken into account.
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Dr. Samuel Bestvater is a Computational Social Scientist at Pew Research Center in Washington DC, where he researches topics at the intersection of technology, social media, political behavior, and computational methodology. A political scientist by training, Dr. Bestvater holds an MA in Political Science from Indiana University and a PhD in Political Science and Social Data Analytics from the Pennsylvania State University. Dr. Bestvater’s research has appeared in Political Analysis, Conflict Management and Peace Science, and in the Proceedings of the International AAAI Conference on Web and Social Media as well as in published reports from Pew Research Center.
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