About

Jennifer Pan is the Sir Robert Ho Tung Professor of Chinese Studies and a Professor of Communication at Stanford University. Her research focuses on political communication, digital media, and authoritarian politics. She uses experimental and computational methods with large-scale datasets on political activity to answer questions about the role of digital media in both authoritarian and democratic contexts. Her work explores how political censorship, propaganda, and information manipulation operate in the digital age, and how these processes influence preferences and behaviors. Her scholarly contributions include a range of publications examining topics such as social assistance in China, political censorship in large language models, digital propaganda, and the impact of social media on political attitudes and movements. She has authored a book titled 'Welfare for Autocrats: How Social Assistance in China Cares for its Rulers' and has contributed to numerous articles in leading journals. Her research provides insights into the strategies of Chinese state media, the effects of online censorship, and the dynamics of information flow in authoritarian regimes, making her a prominent figure in the study of digital politics and authoritarianism.

Research topics

• Political Science
• Computer Science
• Sociology
• Law
• Business
• Advertising
• Social psychology
• Internet privacy
• Psychology
• Media studies
• Social Science
• Public relations
• Medicine
• Algorithm
• Mathematics
• Communication
• Chemistry
• Demography
• World Wide Web

Selected publications

• How do social media feed algorithms affect attitudes and behavior in an election campaign?

  Science · 2023 · 286 citations

  • Computer Science
  • Political Science
  • Algorithm

  We investigated the effects of Facebook's and Instagram's feed algorithms during the 2020 US election. We assigned a sample of consenting users to reverse-chronologically-ordered feeds instead of the default algorithms. Moving users out of algorithmic feeds substantially decreased the time they spent on the platforms and their activity. The chronological feed also affected exposure to content: The amount of political and untrustworthy content they saw increased on both platforms, the amount of content classified as uncivil or containing slur words they saw decreased on Facebook, and the amount of content from moderate friends and sources with ideologically mixed audiences they saw increased on Facebook. Despite these substantial changes in users' on-platform experience, the chronological feed did not significantly alter levels of issue polarization, affective polarization, political knowledge, or other key attitudes during the 3-month study period.

  PublisherDOI

• Like-minded sources on Facebook are prevalent but not polarizing

  Nature · 2023 · 296 citations

  • Political Science
  • Computer Science
  • Sociology

  . Here we present data from 2020 for the entire population of active adult Facebook users in the USA showing that content from 'like-minded' sources constitutes the majority of what people see on the platform, although political information and news represent only a small fraction of these exposures. To evaluate a potential response to concerns about the effects of echo chambers, we conducted a multi-wave field experiment on Facebook among 23,377 users for whom we reduced exposure to content from like-minded sources during the 2020 US presidential election by about one-third. We found that the intervention increased their exposure to content from cross-cutting sources and decreased exposure to uncivil language, but had no measurable effects on eight preregistered attitudinal measures such as affective polarization, ideological extremity, candidate evaluations and belief in false claims. These precisely estimated results suggest that although exposure to content from like-minded sources on social media is common, reducing its prevalence during the 2020 US presidential election did not correspondingly reduce polarization in beliefs or attitudes.

  PublisherOA PDFDOI

• Asymmetric ideological segregation in exposure to political news on Facebook

  Science · 2023 · 291 citations

  • Political Science
  • Sociology
  • Political Science

  Does Facebook enable ideological segregation in political news consumption? We analyzed exposure to news during the US 2020 election using aggregated data for 208 million US Facebook users. We compared the inventory of all political news that users could have seen in their feeds with the information that they saw (after algorithmic curation) and the information with which they engaged. We show that (i) ideological segregation is high and increases as we shift from potential exposure to actual exposure to engagement; (ii) there is an asymmetry between conservative and liberal audiences, with a substantial corner of the news ecosystem consumed exclusively by conservatives; and (iii) most misinformation, as identified by Meta's Third-Party Fact-Checking Program, exists within this homogeneously conservative corner, which has no equivalent on the liberal side. Sources favored by conservative audiences were more prevalent on Facebook's news ecosystem than those favored by liberals.

  PublisherDOI

• Public Sentiment on Chinese Social Media during the Emergence of COVID19

  Journal of Quantitative Description Digital Media · 2021 · 51 citations

  • Political Science
  • Political Science
  • Public relations

  When COVID-19 first emerged in China, there was speculation that the outbreak would trigger public anger and weaken the Chinese regime. By analyzing millions of social media posts from Sina Weibo made between December 2019 and February 2020, we describe the contours of public, online discussions pertaining to COVID-19 in China. We find that discussions of COVID-19 became widespread on January 20, 2020, consisting primarily of personal reflections, opinions, updates, and appeals. We find that the largest bursts of discussion, which contain simultaneous spikes of criticism and support targeting the Chinese government, coincide with the January 23 lockdown of Wuhan and the February 7 death of Dr. Li Wenliang. Criticisms are directed at the government for perceived lack of action, incompetence, and wrongdoing—in particular, censoring information relevant to public welfare. Support is directed at the government for aggressive action and positive outcomes. As the crisis unfolds, the same events are interpreted differently by different people, with those who criticize focusing on the government’s shortcomings and those who praise focusing on the government’s actions.

  DOI

• Capturing Clicks: How the Chinese Government Uses Clickbait to Compete for Visibility

  Political Communication · 2020 · 120 citations

  Senior authorCorresponding
  • Sociology
  • Political Science
  • Media studies

  The proliferation of social media and digital technologies has made it necessary for governments to expand their focus beyond propaganda content in order to disseminate propaganda effectively. We identify a strategy of using clickbait to increase the visibility of political propaganda. We show that such a strategy is used across China by combining ethnography with a computational analysis of a novel dataset of the titles of 197,303 propaganda posts made by 213 Chinese city-level governments on WeChat. We find that Chinese propagandists face intense pressures to demonstrate their effectiveness on social media because their work is heavily quantified–measured, analyzed, and ranked–with metrics such as views and likes. Propagandists use both clickbait and non-propaganda content (e.g., lifestyle tips) to capture clicks, but rely more heavily on clickbait because it does not decrease space available for political propaganda. Government propagandists use clickbait at a rate commensurate with commercial and celebrity social media accounts. The use of clickbait is associated with more views and likes, as well as greater reach of government propaganda outlets and messages. These results reveal how the advertising-based business model and affordances of social media influence political propaganda and how government strategies to control information are moving beyond censorship, propaganda, and disinformation.

  DOI

Recent grants

• RIDIR: Collaborative Research: Integrated Communication Database and Computational Tools

  NSF · $209k · 2018–2022

Frequent coauthors

• Han Zhang

  University of Hong Kong

  69 shared

• Yingdan Lu

  Northwestern University

  27 shared

• Margaret E. Roberts

  22 shared

• Gary King

  Harvard University Press

  21 shared

• Yiqing Xu

  Stanford University

  13 shared

• Yulia Tsvetkov

  12 shared

• Doron Kliger

  University of Haifa

  11 shared

• Anjalie Field

  11 shared

Education

• Ph.D., Political Science

  Stanford University

  2007

• M.A., Political Science

  Stanford University

  2002

• B.A., Political Science

  University of California, Berkeley

  1999

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• Zhenan Baoh-224
• Karl Deisserothh-218
• Michael Snyderh-215
• Roger Blandfordh-205