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The power of big data analytics over fake news: A scientometric review of Twitter as a predictive system in healthcare

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  • Cano-Marin, Enrique
  • Mora-Cantallops, Marçal
  • Sanchez-Alonso, Salvador

Abstract

Interest in healthcare has grown significantly worldwide, especially since the Covid-19 outbreak. Digitalisation has allowed users to interact on social networks through platforms like Twitter, collecting user interactions over time, resulting in the proliferation of fake news. This research aims to analyse, evaluate and classify the predictive potential of Twitter analytics in healthcare, identifying the latent knowledge insights and distinguishing them from related rumours and fake news. Thus, a systematic literature review (SLR) is carried out to identify and analyse the existing academic research and applications in Twitter in predicting healthcare. The most important predictive applications are detecting mental health issues and public health emergencies. Covid-19 has been the main topic of most of the studies linked to fake news and misinformation, and this research provides a practical contribution to the use of unstructured data from Twitter and raises awareness of the importance of this content applied to healthcare. Therefore, it is pertinent to focus on the advances offered by these data as a predictive tool in healthcare since it is essential, to this end, to evaluate the veracity of the information shared on Twitter.

Suggested Citation

  • Cano-Marin, Enrique & Mora-Cantallops, Marçal & Sanchez-Alonso, Salvador, 2023. "The power of big data analytics over fake news: A scientometric review of Twitter as a predictive system in healthcare," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    • Handle: RePEc:eee:tefoso:v:190:y:2023:i:c:s0040162523000719
    DOI: 10.1016/j.techfore.2023.122386
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    1. Kim, Jong Min & Park, Keeyeon Ki-cheon & Mariani, Marcello & Wamba, Samuel Fosso, 2024. "Investigating reviewers' intentions to post fake vs. authentic reviews based on behavioral linguistic features," Technological Forecasting and Social Change, Elsevier, vol. 198(C).

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