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FakeNewsLab: Experimental Study on Biases and Pitfalls Preventing Us from Distinguishing True from False News

Author

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  • Giancarlo Ruffo

    (Department of Computer Science, Università degli Studi di Torino, 10149 Torino, Italy
    Current affiliation: Department of Science and Technological Innovation (DISIT), Università del Piemonte Orientale, 15121 Alessandria, Italy.
    These authors contributed equally to this work.)

  • Alfonso Semeraro

    (Department of Computer Science, Università degli Studi di Torino, 10149 Torino, Italy
    These authors contributed equally to this work.)

Abstract

Misinformation posting and spreading in social media is ignited by personal decisions on the truthfulness of news that may cause wide and deep cascades at a large scale in a fraction of minutes. When individuals are exposed to information, they usually take a few seconds to decide if the content (or the source) is reliable and whether to share it. Although the opportunity to verify the rumour is often just one click away, many users fail to make a correct evaluation. We studied this phenomenon with a web-based questionnaire that was compiled by 7298 different volunteers, where the participants were asked to mark 20 news items as true or false. Interestingly, false news is correctly identified more frequently than true news, but showing the full article instead of just the title, surprisingly, does not increase general accuracy. Additionally, displaying the original source of the news may contribute to misleading the user in some cases, while the genuine wisdom of the crowd can positively assist individuals’ ability to classify news correctly. Finally, participants whose browsing activity suggests a parallel fact-checking activity show better performance and declare themselves as young adults. This work highlights a series of pitfalls that can influence human annotators when building false news datasets, which in turn can fuel the research on the automated fake news detection; furthermore, these findings challenge the common rationale of AI that suggest users read the full article before re-sharing.

Suggested Citation

  • Giancarlo Ruffo & Alfonso Semeraro, 2022. "FakeNewsLab: Experimental Study on Biases and Pitfalls Preventing Us from Distinguishing True from False News," Future Internet, MDPI, vol. 14(10), pages 1-24, September.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:10:p:283-:d:929501
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    References listed on IDEAS

    as
    1. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," NBER Working Papers 23089, National Bureau of Economic Research, Inc.
    2. Hunt Allcott & Matthew Gentzkow & Chuan Yu, 2019. "Trends in the Diffusion of Misinformation on Social Media," NBER Working Papers 25500, National Bureau of Economic Research, Inc.
    3. Hunt Allcott & Matthew Gentzkow, 2017. "Social Media and Fake News in the 2016 Election," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 211-236, Spring.
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