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. 2020 Jan 7;117(1):243-250.
doi: 10.1073/pnas.1906420116. Epub 2019 Nov 25.

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

Christopher A Bail  1   2   3 , Brian Guay  4   5 , Emily Maloney  4   2 , Aidan Combs  4   2 , D Sunshine Hillygus  4   3   5 , Friedolin Merhout  4   6 , Deen Freelon  7 , Alexander Volfovsky  4   8
Affiliations

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

Christopher A Bail et al. Proc Natl Acad Sci U S A. .

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.

Keywords: computational social science; misinformation; political polarization; social media.

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Conflict of interest statement

The authors declare no competing interest.

Figures

Fig. 1.
Fig. 1.
Binomial regression model predicting interaction with Twitter accounts associated with the Russian IRA. Purple circles describe standardized point estimates, and blue lines describe 90% and 95% CIs. Survey respondents with strong ideological homophily in their Twitter network, high interest in politics, and who use Twitter more than once a day were most likely to interact with IRA accounts.
Fig. 2.
Fig. 2.
BCF models describing the effect of interacting with Russian IRA accounts on change in political attitudes and behaviors of Republican and Democratic Twitter users who responded to 2 surveys fielded between October and November 2017. Purple circles describe the average treatment effects on the treated, and blue lines describe 95% credible intervals. Interaction with IRA accounts has no significant effect on all 6 outcomes.
Fig. 3.
Fig. 3.
Individual effects of interacting with Russian IRA accounts on political attitudes and behavior by level of news interest using BCFs.
Fig. 4.
Fig. 4.
Individual effects of interacting with Russian IRA accounts on political attitudes and behavior by frequency of Twitter usage using BCFs.
Fig. 5.
Fig. 5.
Individual effects of interacting with Russian IRA accounts on political attitudes and behavior by partisan identification using BCFs.
Fig. 6.
Fig. 6.
Assessing dosage effects on self-reported ideology over an extended time period. Circles describe standardized point estimates, and lines describe 95% credible intervals for different amounts of direct and indirect engagement with IRA accounts. Here, again, interactions with troll accounts over an extended period show no significant association with ideological polarization.
See this image and copyright information in PMC

Comment in

  • Studying human attention on the Internet.
    Lazer D. Lazer D. Proc Natl Acad Sci U S A. 2020 Jan 7;117(1):21-22. doi: 10.1073/pnas.1919348117. Epub 2019 Dec 17. Proc Natl Acad Sci U S A. 2020. PMID: 31848240 Free PMC article. No abstract available.

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