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Systematic meta-analysis of research on AI tools to deal with misinformation on social media during natural and anthropogenic hazards and disasters

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  • Rosa Vicari

    (International Institute for Applied Systems Analysis (IIASA))

  • Nadejda Komendatova

    (International Institute for Applied Systems Analysis (IIASA))

Abstract

The spread of misinformation on social media has led to the development of artificial intelligence (AI) tools to deal with this phenomenon. These tools are particularly needed when misinformation relates to natural or anthropogenic disasters such as the COVID-19 pandemic. The major research question of our work was as follows: what kind of gatekeepers (i.e. news moderators) do we wish social media algorithms and users to be when misinformation on hazards and disasters is being dealt with? To address this question, we carried out a meta-analysis of studies published in Scopus and Web of Science. We extracted 668 papers that contained keyterms related to the topic of “AI tools to deal with misinformation on social media during hazards and disasters.” The methodology included several steps. First, we selected 13 review papers to identify relevant variables and refine the scope of our meta-analysis. Then we screened the rest of the papers and identified 266 publications as being significant for our research goals. For each eligible paper, we analyzed its objective, sponsor’s location, year of publication, research area, type of hazard, and related topics. As methods of analysis, we applied: descriptive statistics, network representation of keyword co-occurrences, and flow representation of research rationale. Our results show that few studies come from the social sciences (5.8%) and humanities (3.5%), and that most of those papers are dedicated to the COVID-19 risk (92%). Most of the studies deal with the question of detecting misinformation (68%). Few countries are major funders of the development of the topic. These results allow some inferences. Social sciences and humanities seem underrepresented for a topic that is strongly connected to human reasoning. A reflection on the optimum balance between algorithm recommendations and user choices seems to be missing. Research results on the pandemic could be exploited to enhance research advances on other risks.

Suggested Citation

  • Rosa Vicari & Nadejda Komendatova, 2023. "Systematic meta-analysis of research on AI tools to deal with misinformation on social media during natural and anthropogenic hazards and disasters," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.
    • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-01838-0
    DOI: 10.1057/s41599-023-01838-0
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    References listed on IDEAS

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    1. Salman Bin Naeem & Maged N. Kamel Boulos, 2021. "COVID-19 Misinformation Online and Health Literacy: A Brief Overview," IJERPH, MDPI, vol. 18(15), pages 1-12, July.
    2. Napoli, Philip M., 2015. "Social media and the public interest: Governance of news platforms in the realm of individual and algorithmic gatekeepers," Telecommunications Policy, Elsevier, vol. 39(9), pages 751-760.
    3. Diana Tsoy & Tanin Tirasawasdichai & Konstantin Ivanovich Kurpayanidi, 2021. "Role of Social Media in Shaping Public Risk Perception during COVID-19 Pandemic: A Theoretical Review," International Journal of Management Science and Business Administration, Inovatus Services Ltd., vol. 7(2), pages 35-41, January.
    4. Kyle Hunt & Bairong Wang & Jun Zhuang, 2020. "Misinformation debunking and cross-platform information sharing through Twitter during Hurricanes Harvey and Irma: a case study on shelters and ID checks," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(1), pages 861-883, August.
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