Evidence map›Paper›PMID 31767743›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2020

Assessing the Russian Internet Research Agency's impact on the political attitudes and behaviors of American Twitter users in late 2017.

Christopher A Bail, Brian Guay, Emily Maloney, Aidan Combs, D Sunshine Hillygus, Friedolin Merhout, Deen Freelon, Alexander Volfovsky

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.

0numbers the graph read from it
0cells of the map it votes in
35citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

35 citing papers in PubMed.

  1. State-sponsored agenda setting: Measuring the impact of narrative laundering.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  2. Article
  3. Article
  4. Targeted digital voter suppression efforts likely decrease voter turnout.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  5. Article
  6. Article
  7. Social Drivers and Algorithmic Mechanisms on Digital Media.Perspectives on psychological science : a journal of the Association for Psychological Science · 2024
    Review
  8. Article
  9. Temporal dynamics of coordinated online behavior: Stability, archetypes, and influence.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Review
  20. Online engagement with 2020 election misinformation and turnout in the 2021 Georgia runoff election.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Christopher A BailPolarization Lab, Duke University, Durham, NC 27708; christopher.bail@duke.edu.
Brian GuayPolarization Lab, Duke University, Durham, NC 27708.
Emily MaloneyPolarization Lab, Duke University, Durham, NC 27708.
Aidan CombsPolarization Lab, Duke University, Durham, NC 27708.
D Sunshine HillygusPolarization Lab, Duke University, Durham, NC 27708.
Friedolin MerhoutPolarization Lab, Duke University, Durham, NC 27708.
Deen FreelonSchool of Media and Journalism, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599.
Alexander VolfovskyPolarization Lab, Duke University, Durham, NC 27708.ORCID 0000-0003-4462-1020

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is widespread concern that Russia and other countries have launched social-media campaigns designed to increase political divisions in the United States. Though a growing number of studies analyze the strategy of such campaigns, it is not yet known how these efforts shaped the political attitudes and behaviors of Americans. We study this question using longitudinal data that describe the attitudes and online behaviors of 1,239 Republican and Democratic Twitter users from late 2017 merged with nonpublic data about the Russian Internet Research Agency (IRA) from Twitter. Using Bayesian regression tree models, we find no evidence that interaction with IRA accounts substantially impacted 6 distinctive measures of political attitudes and behaviors over a 1-mo period. We also find that interaction with IRA accounts were most common among respondents with strong ideological homophily within their Twitter network, high interest in politics, and high frequency of Twitter usage. Together, these findings suggest that Russian trolls might have failed to sow discord because they mostly interacted with those who were already highly polarized. We conclude by discussing several important limitations of our study-especially our inability to determine whether IRA accounts influenced the 2016 presidential election-as well as its implications for future research on social media influence campaigns, political polarization, and computational social science.

Indexed as

AttitudeBehaviorInternetOrganizationsPoliticsSocial MediaCommunicationHumansRussiaSocial SciencesUnited Statescomputational social sciencemisinformationpolitical polarizationsocial media

Identifiers

PMID31767743
PMCPMC6955293

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.