Megan Ann Duncan, Ph.D.

Assistant professor

Megan Duncan (Ph.D. University of Wisconsin-Madison, 2018) is an assistant professor in the School of Communication at Virginia Tech. She is part of the Journalism & Mass Communication Division there, and has taught courses to communication, journalism and sports media & analytics students, as well as graduate students. Her research focuses on how partisans judge the credibility of and engage with the news. Using survey-embedded experiments, surveys, and other quantitative methods, she’s interested in knowing more about audiences, their perceptions of the news, how they form opinions, and how to use this knowledge to make democracy stronger.

Recent research

Duncan, M., Perryman, M., & Shaughnessy, B. (2023). Same scandal, different standards: The effect of partisanship on expectations of news reports about whistleblowers. Online first from Mass Communication & Society. 26(2), 201-226. doi:

Duncan, M. (2022). Selective rating: Partisan bias in crowdsourced news rating systems. Journal of Information Technology and Politics­. 13(9), 360-375.

Duncan, M. (2022). What’s in a label? Negative credibility labels in partisan news. Journalism and Mass Communication Quarterly. 99(2), 390-413. doi:

Research in the news

New York Times.  – “How could I ever love Michigan State?” Oct. 30, 2021.

Washington Post – “Is election night broken? TV news stuck to old routines amid voting upheaval – and confusion followed.” Nov. 15, 2020.–and-confusion-followed/2020/11/14/9024f248-207b-11eb-90dd-abd0f7086a91_story.html

Globe and Mail – “Presidential projections are indispensable, but not infallible.” Nov. 11, 2020.