Penn Media Accountability Project

“Half the Truth is often a great Lie.”

– Benjamin Franklin

PennMAP is an interdisciplinary, nonpartisan research project of the Computational Social Science Lab at the University of Pennsylvania dedicated to enhancing media transparency and accountability at the scale of the entire information ecosystem.

Misinformation in media is believed to have harmful effects on public opinion, political polarization, and ultimately democratic decision making. And yet, much remains unknown regarding the prevalence of misinformation and its effects on society. 

To address this problem, PennMAP is building technology to detect patterns of bias and misinformation in media from across the political spectrum and spanning television, radio, social media, and the broader web. We will also track consumption of information via television, desktop computers, and mobile devices, as well as its effects on individual and collective beliefs and understanding.

In collaboration with our data partners, we are also building a scalable data infrastructure to ingest, process, and analyze tens of terabytes of television, radio, and web content, as well as representative panels of roughly 100,000 media consumers over several years. While our initial focus is on the U.S., our hope is to scale this infrastructure to eventually cover other countries and languages other than English.

We will share our insights through publications and interactive data visualizations, and will work with various stakeholders—including journalists, policy makers, and industry partners—to implement solutions. Aside from powering our own research, our infrastructure will support other research teams, thereby accelerating the pace of knowledge accumulation and enhancing its reliability.

ABOVE: Evaluating the fake news problem at the scale of the information ecosystem

Selections from the 2020 paper “Evaluating the fake news problem at the scale of the information ecosystem,” coauthored by Lab Director Duncan Watts, Baird Howland, and David Rothschild. With data spanning from January 2016 to December 2018, the piece examines the news ecosystem on a large scale to determine the prevalence of fake news.

Read the full paper here.


Duncan Watts


Stevens University Professor & twenty-third Penn Integrates Knowledge Professor

Homa Hosseinmardi

Homa Hosseinmard

Research Scientist

James Houghton

Research Scientist

Amir Ghasemian

Affiliate Research Scientist

David Rothschild

Affiliate Research Scientist

Samar Haider

Ph.D. Researcher

Baird Howland

Ph.D. Researcher

Bryan Li

Ph.D. Researcher

Zhangyi (David) Fan

David (Zhangyi) Fan Headshot

Graduate Student Researcher

Keith Golden

Keith Golden Headshot

Graduate Student Researcher

Kailun Li

Kailun Li Headshot

Graduate Student Researcher

Xiaonan Liu

Xiaonan Liu Headshot

Graduate Student Researcher

Vivienne Chen

Vivienne Chen Headshot

Undergraduate Student Researcher

Yue (Flora) Chen

Yue (Flora) Chen Headshot

Undergraduate Student Researcher

Misty Liao

Misty Liao Headshot

Undergraduate Student Researcher

Josh Ludan

Josh Ludan Headshot

Undergraduate Student Researcher


Hosseinmardi, Homa; Ghasemian, Amir; Clauset, Aaron; Mobius, Markus; Rothschild, David M.; Watts, Duncan J.

Examining the consumption of radical content on YouTube Journal Article

In: Proceedings of the National Academy of Sciences, 118 (32), 2021.

Abstract | Links | BibTeX

Allen, Jennifer; Mobius, Markus; Rothschild, David M.; Watts, Duncan J.

Research note: Examining potential bias in large-scale censored data Journal Article

In: Harvard Kennedy School (HKS) Misinformation Review, 2021.

Abstract | Links | BibTeX

Watts, Duncan J; Rothschild, David M; Mobius, Markus

Measuring the news and its impact on democracy Journal Article

In: Proceedings of the National Academy of Sciences, 118 (15), 2021.

Abstract | Links | BibTeX

Allen, Jennifer; Howland, Baird; Mobius, Markus; Rothschild, David; Watts, Duncan J

Evaluating the fake news problem at the scale of the information ecosystem Journal Article

In: Science Advances, 6 (14), pp. eaay3539, 2020.

Abstract | Links | BibTeX

Kao, Hsien-Te; Yan, Shen; Huang, Di; Bartley, Nathan; Hosseinmardi, Homa; Ferrara, Emilio

Understanding Cyberbullying on Instagram and via Social Role Detection Journal Article

In: Companion Proceedings of The 2019 World Wide Web Conference, pp. 183-188, 2019.

Abstract | Links | BibTeX

Ghasemian, Amir; Hosseinmardi, Homa; Clauset, Aaron

Evaluating overfit and underfit in models of network community structure Journal Article

In: IEEE Transactions on Knowledge and Data Engineering, 32 (9), pp. 1722-1735, 2019.

Abstract | Links | BibTeX

Lazer, David M. J.; Baum, Matthew A.; Benkler, Yochai; Berinsky, Adam J.; Greenhill, Kelly M.; Menczer, Filippo; Metzger, Miriam J.; Nyhan, Brendan; Pennycook, Gordon; Rothschild, David; Schudson, Michael; Sloman, Steven A.; Sunstein, Cass R.; Thorson, Emily A.; Watts, Duncan J.; Zittrain, Jonathan L.

The science of fake news Journal Article

In: Science, 359 (6380), pp. 1094-1096, 2018.

Links | BibTeX

Houghton, James P.; Siegel, Michael; Madnick, Stuart; Tounaka, Nobuaki; Nakamura, Kazutaka; Sugiyama, Takaaki; Nakagawa, Daisuke; Shirnen, Buyanjargal

Beyond Keywords: Tracking the evolution of conversational clusters in social media Journal Article

In: Sociological Methods & Research, 48 (3), pp. 588-607, 2017.

Abstract | Links | BibTeX