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Negativity bias in the spread of voter fraud conspiracy theory tweets during the 2020 US election

Author

Listed:
  • Mason Youngblood

    (Max Planck Institute for Geoanthropology
    Stony Brook University)

  • Joseph M. Stubbersfield

    (University of Winchester)

  • Olivier Morin

    (Max Planck Institute for Geoanthropology
    PSL University, CNRS)

  • Ryan Glassman

    (IBM Watson)

  • Alberto Acerbi

    (University of Trento)

Abstract

During the 2020 US presidential election, conspiracy theories about large-scale voter fraud were widely circulated on social media platforms. Given their scale, persistence, and impact, it is critically important to understand the mechanisms that caused these theories to spread. The aim of this preregistered study was to investigate whether retweet frequencies among proponents of voter fraud conspiracy theories on Twitter during the 2020 US election are consistent with frequency bias and/or content bias. To do this, we conducted generative inference using an agent-based model of cultural transmission on Twitter and the VoterFraud2020 dataset. The results show that the observed retweet distribution is consistent with a strong content bias causing users to preferentially retweet tweets with negative emotional valence. Frequency information appears to be largely irrelevant to future retweet count. Follower count strongly predicts retweet count in a simpler linear model but does not appear to drive the overall retweet distribution after temporal dynamics are accounted for. Future studies could apply our methodology in a comparative framework to assess whether content bias for emotional valence in conspiracy theory messages differs from other forms of information on social media.

Suggested Citation

  • Mason Youngblood & Joseph M. Stubbersfield & Olivier Morin & Ryan Glassman & Alberto Acerbi, 2023. "Negativity bias in the spread of voter fraud conspiracy theory tweets during the 2020 US election," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-11, December.
  • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02106-x
    DOI: 10.1057/s41599-023-02106-x
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    References listed on IDEAS

    as
    1. Alberto Acerbi, 2019. "Cognitive attraction and online misinformation," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-7, December.
    2. Jason W. Burton & Nicole Cruz & Ulrike Hahn, 2021. "Reconsidering evidence of moral contagion in online social networks," Nature Human Behaviour, Nature, vol. 5(12), pages 1629-1635, December.
    3. Osmundsen, Mathias & Bor, Alexander & Vahlstrup, Peter Bjerregaard & Bechmann, Anja & Petersen, Michael Bang, 2021. "Partisan Polarization Is the Primary Psychological Motivation behind Political Fake News Sharing on Twitter," American Political Science Review, Cambridge University Press, vol. 115(3), pages 999-1015, August.
    4. Robert F. Lachlan & Oliver Ratmann & Stephen Nowicki, 2018. "Cultural conformity generates extremely stable traditions in bird song," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    5. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    6. Mason Youngblood, 2020. "Extremist ideology as a complex contagion: the spread of far-right radicalization in the United States between 2005 and 2017," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-10, December.
    7. Simon Carrignon & R. Alexander Bentley & Damian Ruck, 2019. "Modelling rapid online cultural transmission: evaluating neutral models on Twitter data with approximate Bayesian computation," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-9, December.
    8. Radnitz, Scott & Underwood, Patrick, 2017. "Is Belief in Conspiracy Theories Pathological? A Survey Experiment on the Cognitive Roots of Extreme Suspicion," British Journal of Political Science, Cambridge University Press, vol. 47(1), pages 113-129, January.
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