American Political Science Review, 2024
Aligning theory and measurement in political information sharing on social media.
Recommended citation: Green, Jon, Stefan McCabe, Sarah Shugars, Hanyu Chwe, Luke Horgan, Shuyang Cao, and David Lazer. "Curation Bubbles." Accepted, American Political Science Review.
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Public Opinion Quarterly, 2024
Uses a sample of political pundits on Twitter to identify ideological structure in political discourse.
Recommended citation: Green, Jon. "The Rhetorical 'What Goes with What': Political Pundits and the Discursive Superstructure of Ideology in U.S. Politics." Accepted, Public Opinion Quarterly.
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Journal of Quantitative Description: Digital Media, 2023
Finds that Twitter users who follow their own member of Congress are older, live in wealthier areas of their district, and are more politically and demographically similar to the member than users who do not.
Recommended citation: McCabe, Stefan, Jon Green, Pranav Goel, and David Lazer. 2023. "Inequalities in Online Representation: Who follows their own member of Congress on Twitter?." Journal of Quantitative Description: Digital Media, 3.
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Political Behavior, 2023
Finds that prospective candidates for state and local office who articulated their interest in localized and goal-congruent terms were likelier to actually run for office than those who articulated their interest in more general or nationalized terms.
Recommended citation: Green, Jon, Meredith Conroy, and Ciera Hammond. 2023. "Something to Run For: Stated Motives as Indicators of Candidate Emergence." Political Behavior.
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Nature, 2023
Finds limited evidence that users’ search results systematically differ by user partisanship, but stronger evidence that users’ engagement with search results does.
Recommended citation: Robertson, Ronald E., Jon Green, Damian Ruck, Katherine Ognyanova, Christo Wilson, and David Lazer. 2023. "Users choose to engage with more partisan news than they are exposed to on Google Search." Nature 618: 342-348.
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Cambridge University Press, Elements Series in Experimental Political Science, 2023
A practical introduction to machine learning for readers who are familiar with experiments.
Recommended citation: Green, Jon, and Mark H. White II. 2023. Machine Learning for Experiments in the Social Sciences. Cambridge University Press, Elements Series in Experimental Political Science.
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Proceedings of the National Academy of Sciences, 2022
Finds relationships between public engagement with 2020 election conspiracy theories on Twitter and turnout in the 2021 Georgia runoff election.
Recommended citation: Green, Jon, William Hobbs, Stefan McCabe, and David Lazer. 2022. "Online Engagement with 2020 Election Misinformation and Turnout in the 2021 Georgia Runoff Election." Proceedings of the National Academy of Sciences 119(34): e2115900119.
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