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What drives unverified information sharing and cyberchondria during the COVID-19 pandemic?

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  • Samuli Laato
  • A. K. M. Najmul Islam
  • Muhammad Nazrul Islam
  • Eoin Whelan

Abstract

The World Health Organisation has emphasised that misinformation – spreading rapidly through social media – poses a serious threat to the COVID-19 response. Drawing from theories of health perception and cognitive load, we develop and test a research model hypothesising why people share unverified COVID-19 information through social media. Our findings suggest a person’s trust in online information and perceived information overload are strong predictors of unverified information sharing. Furthermore, these factors, along with a person’s perceived COVID-19 severity and vulnerability influence cyberchondria. Females were significantly more likely to suffer from cyberchondria, with males more likely to share news without verifying its reliability. Our findings suggest that to mitigate the spread of COVID-19 misinformation and cyberchondria, measures should be taken to enhance a healthy scepticism of health news while simultaneously guarding against information overload.

Suggested Citation

  • Samuli Laato & A. K. M. Najmul Islam & Muhammad Nazrul Islam & Eoin Whelan, 2020. "What drives unverified information sharing and cyberchondria during the COVID-19 pandemic?," European Journal of Information Systems, Taylor & Francis Journals, vol. 29(3), pages 288-305, May.
    • Handle: RePEc:taf:tjisxx:v:29:y:2020:i:3:p:288-305
    DOI: 10.1080/0960085X.2020.1770632
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    Cited by:

    1. Ammara Malik & Faiza Bashir & Khalid Mahmood, 2023. "Antecedents and Consequences of Misinformation Sharing Behavior among Adults on Social Media during COVID-19," SAGE Open, , vol. 13(1), pages 21582440221, January.
    2. Mohammad Alamgir Hossain & Md. Maruf Hossan Chowdhury & Ilias O. Pappas & Bhimaraya Metri & Laurie Hughes & Yogesh K. Dwivedi, 2023. "Fake news on Facebook and their impact on supply chain disruption during COVID-19," Annals of Operations Research, Springer, vol. 327(2), pages 683-711, August.
    3. Xia, Huosong & Wang, Yuan & Zhang, Justin Zuopeng & Zheng, Leven J. & Kamal, Muhammad Mustafa & Arya, Varsha, 2023. "COVID-19 fake news detection: A hybrid CNN-BiLSTM-AM model," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    4. Adrian Kwek & Luke Peh & Josef Tan & Jin Xing Lee, 2023. "Distractions, analytical thinking and falling for fake news: A survey of psychological factors," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.

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