Media Patronage: How Authoritarian Regimes Exploit Media Competition to Shape Global Information
Matt DeButts
April 27, 2026 · 12:00 PM - 1:00 PM · Nafziger Room 5055, Vilas Hall
UW::CODE
The CODE Lab
Computational Observation of Digital Exposure
Overview
The CODE Lab (Computational Observation of Digital Exposure) at the University of Wisconsin–Madison studies what information people encounter in digital environments and how that exposure shapes attitudes and behavior. Using large-scale behavioral data, the lab builds observational infrastructure and computational tools for studying online information environments increasingly shaped by AI.
A major line of work examines online prediction markets as a new form of political information. Platforms like Polymarket and Kalshi now sit alongside polls and forecasts as public signals about elections, and the lab maintains a real-time archive of prediction market trading, commenting, and reacting to study who trades, what moves prices, and how market odds circulate through news coverage and shape what people believe about a race. Recent work describes the small set of sophisticated traders behind most political trading volume and asks whether traders buy the candidate they expect to win or the candidate they want to win.
Alongside the prediction markets archive, the lab runs a longitudinal panel of Americans' web browsing paired with survey responses. The lab also supports graduate training in computational methods for communication research.
People
Ross Dahlke
Director, CODE Lab
Assistant Professor,
School of Journalism and Mass Communication,
University of Wisconsin–Madison
Graduate Students
Yingqi (Vicky) Huang
Research MA Student,
School of Journalism and Mass Communication,
University of Wisconsin–Madison
Hyerin Kwon
PhD Candidate,
School of Journalism and Mass Communication,
University of Wisconsin–Madison
Yanshu (Sybil) Wang
PhD Student,
School of Journalism and Mass Communication,
University of Wisconsin–Madison
Haotian (Barry) Zhao
PhD Student,
School of Journalism and Mass Communication,
University of Wisconsin–Madison
Publications and Reports
* indicates equal authorship
Prediction Markets
Dahlke, R., Mine, N., Zhao, H., Huang, Y., & Shah, D. (2026). Electoral Predictions on Polymarket: A Quantitative Description of Trading, Commenting, and Reacting. Journal of Quantitative Description: Digital Media, 6. https://doi.org/10.51685/jqd.2026.011
Coverage: The Washington Post · Milwaukee Journal Sentinel
Huang, Y., & Dahlke, R. (2026). Who Reports Using Polymarket? A Demographic, Attitudinal, and Political Analysis. CODE Lab, University of Wisconsin–Madison. https://rossdahlke.com/codelab/polymarket-users-2026.pdf
Open Web
Dahlke, R., Moore, R. C., Adib-Azpeitia, D., Ugander, J., & Hancock, J. T. (2026). Multi-Platform Referrers of Misinformation: A Comparative Analysis of Misinformation Visits Referred by Facebook, Twitter, Instagram, Reddit, YouTube, Snapchat, and TikTok. Political Communication. https://doi.org/10.1080/10584609.2026.2679492
Dahlke*, R., Moore*, R. C., & Hancock, J. T. (2026). Exposure to (AI-Generated) Untrustworthy Websites in the 2024 US Election. OSF Preprints. https://doi.org/10.31234/osf.io/qtdmg_v1
Dahlke*, R., Moore*, R. C., Bengani, P., & Hancock, J. (2026). The Consumption of Pink Slime Journalism: Who, What, When, Where, and Why? Digital Journalism, 1-23. https://doi.org/10.1080/21670811.2026.2669525
Coverage: The Boston Globe · Northwestern Local News Initiative
Dahlke*, R., Tu*, F., Wang*, Y.-C., Lu, Y., Engeda, B. W., & Hancock, J. T. (2025). Contextualizing Misinformation: A User-Centric Approach to Linguistic and Topical Patterns in News Consumption. Proceedings of the ACM on Human-Computer Interaction, 9(CSCW1), 1-40. https://doi.org/10.1145/3757571
Dahlke, R., Kumar, D., Durumeric, Z., & Hancock, J. T. (2025). Quantifying the Systematic Bias in the Accessibility and Inaccessibility of Web Scraping Content from URL-Logged Web-Browsing Digital Trace Data. Social Science Computer Review, 43(5), 1071-1086. https://doi.org/10.1177/08944393231218214
Dahlke, R., & Hancock, J. (2025). Untrustworthy Website Exposure and Election Beliefs: Selective Exposure and Ideological Asymmetry. Journal of Online Trust and Safety, 3(1). https://doi.org/10.54501/jots.v3i1.250
Moore*, R. C., Dahlke*, R., Forberg, P. L., & Hancock, J. T. (2024). The Private Life of QAnon: A Mixed Methods Investigation of Americans' Exposure to QAnon Content on the Web. Proceedings of the ACM on Human-Computer Interaction, 8(CSCW2), 1-34. https://doi.org/10.1145/3687057
Moore*, R. C., Dahlke*, R., & Hancock, J. T. (2023). Exposure to Untrustworthy Websites in the 2020 US Election. Nature Human Behaviour, 7, 1096-1105. https://doi.org/10.1038/s41562-023-01564-2
Coverage: The New York Times · World Economic Forum · Minnesota Star Tribune · The Daily Beast
Personal Messaging
Dahlke, R., & Hancock, J. (2025). The Public Sphere in Private Spaces: Quantifying Political Computer-Mediated Communication in Personal Messaging. OSF Preprints. https://osf.io/6cpv8/
Social Media
Kwon, H., Jiang, X., & Dahlke, R. (Forthcoming). Do Politicians' Strategic Facial Displays Drive Resonant Moments on Social Media? Computational Analysis of the 2024 U.S. Presidential Debate Using Multimodal Large Language Models. International Journal of Communication.
Talks
Past
Short Educational Videos Improve Older Adults’ Digital Literacy and Information Diets at Scale
Ryan Moore, PhD
March 20, 2026 · 1:00 PM - 2:00 PM · Nafziger Room 5055, Vilas Hall
Join
If you are a current UW–Madison graduate student interested in the CODE Lab, please email ross.dahlke@wisc.edu for more details.
Prospective MA and PhD students can apply through UW–Madison SJMC graduate admissions. The SJMC admits students to the program, not to individual faculty labs. If you are interested in working with me, please mention me in your application.