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Self-certification: A novel method for increasing sharing discernment on social media

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  • Piers Douglas Lionel Howe
  • Andrew Perfors
  • Keith J Ransom
  • Bradley Walker
  • Nicolas Fay
  • Yoshi Kashima
  • Morgan Saletta
  • Sihan Dong

Abstract

The proliferation of misinformation on social media platforms has given rise to growing demands for effective intervention strategies that increase sharing discernment (i.e. increase the difference in the probability of sharing true posts relative to the probability of sharing false posts). One suggested method is to encourage users to deliberate on the veracity of the information prior to sharing. However, this strategy is undermined by individuals’ propensity to share posts they acknowledge as false. In our study, across three experiments, in a simulated social media environment, participants were shown social media posts and asked whether they wished to share them and, sometimes, whether they believed the posts to be truthful. We observe that requiring users to verify their belief in a news post’s truthfulness before sharing it markedly curtails the dissemination of false information. Thus, requiring self-certification increased sharing discernment. Importantly, requiring self-certification didn’t hinder users from sharing content they genuinely believed to be true because participants were allowed to share any posts that they indicated were true. We propose self-certification as a method that substantially curbs the spread of misleading content on social media without infringing upon the principle of free speech.

Suggested Citation

  • Piers Douglas Lionel Howe & Andrew Perfors & Keith J Ransom & Bradley Walker & Nicolas Fay & Yoshi Kashima & Morgan Saletta & Sihan Dong, 2024. "Self-certification: A novel method for increasing sharing discernment on social media," PLOS ONE, Public Library of Science, vol. 19(6), pages 1-17, June.
  • Handle: RePEc:plo:pone00:0303025
    DOI: 10.1371/journal.pone.0303025
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    References listed on IDEAS

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    1. 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).
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