“Half the Truth is often a great Lie.”

 

– Benjamin Franklin

PennMAP

Penn Media Accountability Project

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.

Touchstone Publications

Quantifying partisan news diets in Web and TV audiences

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

Measuring the news and its impact on democracy

Examining radical content consumption on YouTube

Data Dashboard: YouTube Politics

Does YouTube’s video recommendation algorithm drive users towards more extreme political content?

YouTube is arguably the largest and most engaging online media consumption platform in the world. Recently, its scale has fueled concerns that YouTube users are being radicalized via a combination of biased recommendations and ostensibly apolitical “anti-woke” channels, both of which have been claimed to direct attention to radical political content.

We test this hypothesis using a representative panel of over 300,000 Americans and their individual-level browsing behavior, on and off of the YouTube platform. Learn more with our interactive data dashboard.

Mission

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.

Commitment to Use-Inspired Research

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.

In the News

Warped Front Pages

Warped Front Pages

Seven years ago, in the wake of the 2016 presidential election, media analysts rushed to explain Donald Trump’s victory. Misinformation was to blame, the theory went, fueled by Russian agents and carried on social networks. But as researchers, we wondered if fascination and fear over “fake news” had led people to underestimate the influence of traditional journalism outlets. After all, mainstream news organizations remain an important part of the media ecosystem—they’re widely read and watched; they help set the agenda, including on social networks.

People

Duncan Watts

Stevens University Professor
Penn Integrates Knowledge Professor

Homa Hosseinmardi

Associate Research Scientist

Amir Ghasemian

Affiliate Research Scientist
Yale University

David Rothschild

Affiliate Research Scientist
Microsoft Research

Coen Needell

Pre-Doctoral Researcher

Timothy Dorr

Ph.D. in Communications

Upasana Dutta

Ph.D. in Computer and Information Science

Samar Haider

Ph.D. in Computer and Information Science

Baird Howland

Ph.D. in Communications

Sam Wolken

Ph.D. in Communications and Political Science

People

Duncan Watts

Stevens University Professor
Penn Integrates Knowledge Professor

Homa Hosseinmardi

Associate Research Scientist

Amir Ghasemian

Affiliate Research Scientist
Yale University

David Rothschild

Affiliate Research Scientist
Microsoft Research

Coen Needell

Pre-Doctoral Researcher

Timothy Dorr

Ph.D. in Communications

Upasana Dutta

Ph.D. in Computer and Information Science

Samar Haider

Ph.D. in Computer and Information Science

Baird Howland

Ph.D. in Communications

Sam Wolken

Ph.D. in Communications and Political Science

Publications

Ribeiro, Manoel Horta; Hosseinmardi, Homa; West, Robert; Watts, Duncan J.

Deplatforming did not decrease Parler users’ activity on fringe social media Journal Article

In: PNAS Nexus, vol. 2, iss. 3, 2023.

Abstract | Links | BibTeX

Muise, Daniel; Hosseinmardi, Homa; Howland, Baird; Mobius, Markus; Rothschild, David; Watts, Duncan J.

Quantifying partisan news diets in Web and TV audiences Journal Article

In: Science Advances, vol. 8, iss. 28, 2022.

Abstract | Links | BibTeX

Balietti, Stefano; Getoor, Lise; Goldstein, Daniel G.; Watts, Duncan J.

Reducing opinion polarization: Effects of exposure to similar people with differing political views Journal Article

In: Proceedings of the National Academy of Sciences, vol. 118, no. 52, 2021.

Abstract | Links | BibTeX

Lifchits, George; Anderson, Ashton; Goldstein, Daniel G.; Hofman, Jake M.; Watts, Duncan J.

Success stories cause false beliefs about success Journal Article

In: Judgment and Decision Making, vol. 16, no. 6, pp. 1439-1463, 2021, ISSN: 1930-2975.

Abstract | Links | BibTeX

Konitzer, Tobias; Allen, Jennifer; Eckman, Stephanie; Howland, Baird; Mobius, Markus; Rothschild, David; Watts, Duncan J.

Comparing Estimates of News Consumption from Survey and Passively Collected Behavioral Data Journal Article

In: Public Opinion Quarterly, 2021.

Abstract | Links | BibTeX

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, vol. 118, no. 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, vol. 118, no. 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, vol. 6, no. 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 Ask.fm 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, vol. 32, no. 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, vol. 359, no. 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, vol. 48, no. 3, pp. 588-607, 2017.

Abstract | Links | BibTeX