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Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics

Author

Listed:
  • Riccardo Gallotti

    (CoMuNe Lab, Fondazione Bruno Kessler)

  • Francesco Valle

    (CoMuNe Lab, Fondazione Bruno Kessler)

  • Nicola Castaldo

    (CoMuNe Lab, Fondazione Bruno Kessler)

  • Pierluigi Sacco

    (IULM University
    Fondazione Bruno Kessler)

  • Manlio De Domenico

    (CoMuNe Lab, Fondazione Bruno Kessler)

Abstract

During COVID-19, governments and the public are fighting not only a pandemic but also a co-evolving infodemic—the rapid and far-reaching spread of information of questionable quality. We analysed more than 100 million Twitter messages posted worldwide during the early stages of epidemic spread across countries (from 22 January to 10 March 2020) and classified the reliability of the news being circulated. We developed an Infodemic Risk Index to capture the magnitude of exposure to unreliable news across countries. We found that measurable waves of potentially unreliable information preceded the rise of COVID-19 infections, exposing entire countries to falsehoods that pose a serious threat to public health. As infections started to rise, reliable information quickly became more dominant, and Twitter content shifted towards more credible informational sources. Infodemic early-warning signals provide important cues for misinformation mitigation by means of adequate communication strategies.

Suggested Citation

  • Riccardo Gallotti & Francesco Valle & Nicola Castaldo & Pierluigi Sacco & Manlio De Domenico, 2020. "Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics," Nature Human Behaviour, Nature, vol. 4(12), pages 1285-1293, December.
    • Handle: RePEc:nat:nathum:v:4:y:2020:i:12:d:10.1038_s41562-020-00994-6
    DOI: 10.1038/s41562-020-00994-6
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    References listed on IDEAS

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    1. Peter D. Lunn & Cameron A. Belton & Ciarán Lavin & Féidhlim P. McGowan & Shane Timmons & Deirdre A. Robertson, 2020. "Using behavioral science to help fight the Coronavirus," Journal of Behavioral Public Administration, Center for Experimental and Behavioral Public Administration, vol. 3(1).
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    3. Chengcheng Shao & Giovanni Luca Ciampaglia & Onur Varol & Kai-Cheng Yang & Alessandro Flammini & Filippo Menczer, 2018. "The spread of low-credibility content by social bots," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
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    Cited by:

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    2. Quan-Hoang Vuong & Tam-Tri Le & Viet-Phuong La & Huyen Thanh Thanh Nguyen & Manh-Toan Ho & Quy Khuc & Minh-Hoang Nguyen, 2022. "Covid-19 vaccines production and societal immunization under the serendipity-mindsponge-3D knowledge management theory and conceptual framework," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-12, December.
    3. Leto Peel & Tiago P. Peixoto & Manlio De Domenico, 2022. "Statistical inference links data and theory in network science," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    4. Li, WenYao & Xue, Xiaoyu & Pan, Liming & Lin, Tao & Wang, Wei, 2022. "Competing spreading dynamics in simplicial complex," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    5. Wen Shi & Diyi Liu & Jing Yang & Jing Zhang & Sanmei Wen & Jing Su, 2020. "Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter," IJERPH, MDPI, vol. 17(22), pages 1-18, November.
    6. You, Xuemei & Zhang, Man & Ma, Yinghong & Tan, Jipeng & Liu, Zhiyuan, 2023. "Impact of higher-order interactions and individual emotional heterogeneity on information-disease coupled dynamics in multiplex networks," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).

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