About

Andrew M. Guess is an associate professor of politics and public affairs at Princeton University. His research employs quantitative and computational methods to investigate the relationship between digital media and politics. His work addresses key questions such as the extent to which digital and social media facilitate encounters with differing political perspectives, the prevalence and effects of online misinformation, and the relationship between social media and political outcomes including polarization and trust. Additionally, he explores how social platforms can improve knowledge and promote news diet quality, as well as how researchers can use survey and digital trace data to measure concepts related to digital media and politics. He is also a founding co-editor of the Journal of Quantitative Description: Digital Media, alongside Kevin Munger and Eszter Hargittai, contributing to the development of the journal's philosophy and goals.

Research topics

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

Selected publications

• Code for "The Effect of Deactivating Facebook and Instagram on Users’ Emotional State"

  ICPSR Data Holdings · 2026-03-31

  datasetOpen access

  We estimate the effect of social media deactivation on users’ emotional state in two large randomized experiments before the 2020 U.S. election. People who deactivated Facebook for the six weeks before the election reported a 0.060 standard deviation improvement in an index of happiness, depression, and anxiety, relative to controls who deactivated for just the first of those six weeks. People who deactivated Instagram for those six weeks reported a 0.041 standard deviation improvement relative to controls. Exploratory analysis suggests the Facebook effect is driven by people over 35, while the Instagram effect is driven by women under 25.<br>

  PublisherDOI

• Code for "The Effect of Deactivating Facebook and Instagram on Users’ Emotional State"

  ICPSR Data Holdings · 2026-03-31

  datasetOpen access

  We estimate the effect of social media deactivation on users’ emotional state in two large randomized experiments before the 2020 U.S. election. People who deactivated Facebook for the six weeks before the election reported a 0.060 standard deviation improvement in an index of happiness, depression, and anxiety, relative to controls who deactivated for just the first of those six weeks. People who deactivated Instagram for those six weeks reported a 0.041 standard deviation improvement relative to controls. Exploratory analysis suggests the Facebook effect is driven by people over 35, while the Instagram effect is driven by women under 25.<br>

  PublisherDOI

• Peer Review 2027: Scenarios for Academic Publishing in the Age of AI

  2026-01-27 · 1 citations

  article

  The practices of peer review and the broader landscape of academic publishing are under strain. Submission rates are rising, placing greater demands on editorial and reviewer capacity. The use of LLMs in the production of academic papers threatens to accelerate these trends beyond the breaking point. This article is the product of a meeting of editors of academic journals across political science, sociology, and communication science to discuss the issue of LLMs in academic publishing. We argue that peer review is an essential and irreplaceably human component of social science: if elements of the research process traditionally done by humans are substituted by AI, humans should increase their involvement with the evaluation of research. We present a scenario-casting exercise illustrating four possible equilibria for the incorporation of LLMs into the publication ecosystem, and we discuss the various levers that academic journals have at their disposal to navigate the changing landscape. We emphasize that adaptive policies with built-in evaluation mechanisms, feedback loops, and a capacity for revision are required, along with new streams of metascientific data to remain up to date as AI and its adoption continue to evolve.

  PublisherDOI

• How deceptive online networks reached millions in the US 2020 elections

  Nature Human Behaviour · 2026-04-06

  article
  PublisherDOI

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

  UNC Libraries · 2025-03-19 · 10 citations

  articleOpen access

  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

• The Effects of Political Advertising on Facebook and Instagram before the 2020 US Election

  National Bureau of Economic Research · 2025-05-01 · 1 citations

  reportOpen access

  a collaboration between a team of researchers at Meta and an independent set of external academic researchers.

  PublisherDOI

• Citizen preferences for online hate speech regulation

  PNAS Nexus · 2025-02-01 · 6 citations

  articleOpen access

  Abstract The shift of public discourse to online platforms has intensified the debate over content moderation by platforms and the regulation of online speech. Designing rules that are met with wide acceptance requires learning about public preferences. We present a visual vignette study using a sample (n=2,622) of German and US citizens that were exposed to 20,976 synthetic social media vignettes mimicking actual cases of hateful speech. We find people’s evaluations to be primarily shaped by message type and severity, and less by contextual factors. While focused measures like deleting hateful content are popular, more extreme sanctions like job loss find little support even in cases of extreme hate. Further evidence suggests in-group favoritism among political partisans. Experimental evidence shows that exposure to hateful speech reduces tolerance of unpopular opinions.

  PublisherDOI

• The Effects of Political Advertising on Facebook and Instagram Before the 2020 US Election

  SSRN Electronic Journal · 2025-01-01

  articleOpen access
  PublisherDOI

• Reshares on social media amplify political news but do not detectably affect beliefs or opinions

  UNC Libraries · 2025-03-19

  articleOpen accessSenior author

  We studied the effects of exposure to reshared content on Facebook during the 2020 US election by assigning a random set of consenting, US-based users to feeds that did not contain any reshares over a 3-month period. We find that removing reshared content substantially decreases the amount of political news, including content from untrustworthy sources, to which users are exposed; decreases overall clicks and reactions; and reduces partisan news clicks. Further, we observe that removing reshared content produces clear decreases in news knowledge within the sample, although there is some uncertainty about how this would generalize to all users. Contrary to expectations, the treatment does not significantly affect political polarization or any measure of individual-level political attitudes.

  PublisherDOI

• The Effects of Unsubstantiated Claims of Voter Fraud on Confidence in Elections – CORRIGENDUM

  Journal of Experimental Political Science · 2025-09-04

  erratumOpen access

  from tampering is [1, 5] when it should instead have read [1, 4].

  PublisherOA PDFDOI

Frequent coauthors

• Brendan Nyhan

  136 shared

• Jason Reifler

  University of Exeter

  75 shared

• Benjamin Lyons

  University of Utah

  67 shared

• Jacob Montgomery

  Washington University in St. Louis

  65 shared

• Michael Lerner

  56 shared

• Neelanjan Sircar

  Centre for Policy Research

  56 shared

• Pablo Barberá

  New York University

  44 shared

• Simon Munzert

  Hertie School

  35 shared

Education

• Ph.D., Political Science

  Columbia University

  2015