Online Information Environment

In the digital age, true and false information spreads rapidly on social media. CSMaP experts study how we consume and share news online and the impact the online information environment has on our democracy.

Academic Research

  • Working Paper

    Artificial Intelligence, Politics, and Political Science

    Working Paper, 2026

    View Article View abstract

    This forthcoming edited volume (Cambridge University Press) examines the transformative impact of artificial intelligence on democratic institutions, political behavior, governance, and the discipline of political science itself. The volume represents the report of the American Political Science Association’s Presidential Task Force on AI, Politics, and Political Science, co-chaired by Joshua Tucker and Nathaniel Persily. 

    Across twelve chapters produced by close to 60 scholars, the report evaluates how generative AI and machine learning systems are reshaping public opinion formation, political communication, labor markets, electoral processes, state capacity, and regulatory frameworks. The authors analyze both the opportunities and risks posed by AI technologies, including concerns surrounding information integrity, ideological personalization, surveillance, democratic accountability, and concentrated technological power. Themes that cut across multiple chapters include: the unprecedented power of a small number of AI corporations; the opacity and non-replicability of model outputs; bias in AI systems; and the absence of agreed-upon benchmarks for evaluation.The volume also addresses methodological and ethical implications for political science research, emphasizing transparency, reproducibility, and the responsible integration of AI tools into scholarly inquiry. Ultimately, the volume argues that AI will not only alter political institutions and citizen-state relations, but also may fundamentally reshape how political knowledge is produced and interpreted. It calls for sustained interdisciplinary collaboration and evidence-based governance to ensure that AI development supports democratic resilience rather than undermining it.

  • Journal Article

    How Deceptive Online Networks Reached Millions in the US 2020 elections

    • Ruth E. Appel, 
    • Young Mie Kim, 
    • Jennifer Pan, 
    • Yiqing Xu, 
    • Ben Nimmo, 
    • Daniel Robert Thomas, 
    • Hunt Allcott, 
    • Pablo Barberá
    • Taylor Brown, 
    • Adriana Crespo-Tenorio, 
    • Drew Dimmery, 
    • Deen Freelon, 
    • Matthew Gentzkow, 
    • Sandra González-Bailón
    • Andrew M. Guess
    • Shanto Iyengar, 
    • David Lazer, 
    • Neil Malhotra, 
    • Devra Moehler, 
    • Brendan Nyhan, 
    • Jaime Settle, 
    • Emily Thorson, 
    • Rebekah Tromble, 
    • Caros Velasco Rivera, 
    • Arjun Wilkins, 
    • Magdalena Wojcieszak
    • Beixian Xiong, 
    • Chad Kiewiet de Jonge, 
    • Annie Franco, 
    • Winter Mason, 
    • Natalie Jomini Stroud, 
    • Joshua A. Tucker

    Nature Human Behaviour, 2026

    View Article View abstract

    Deceptive online networks are coordinated efforts that use identity deception to pursue strategic political or financial goals. During the US 2020 elections, these networks reached at least 37 million Facebook and 3 million Instagram users, representing 15% and 2% of the platforms’ active US adult users, respectively. Only 3 networks out of 49—1 network with explicitly political aims and 2 that appeared to use politics as a lure for profit—were responsible for over 70% of users reached. Notably, accounts unaffiliated with the networks played an important role in facilitating this reach by resharing content the three networks produced. Deceptive networks, regardless of whether their goals were political or financial, reached users who were older, more conservative, more frequently exposed to content from untrustworthy sources, and spent more time on Facebook.

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Reports & Analysis

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