What can internet users' behaviours reveal about the mental health impacts of the COVID-19 pandemic? A systematic review

V. Gianfredi*, S. Provenzano, O.E. Santangelo

*Corresponding author for this work

Research output: Contribution to journal(Systematic) Review article peer-review


Objectives: At the end of 2019, an acute infectious pneumonia (coronavirus disease 2019 [COVID-19]) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began in Wuhan, China, and subsequently spread around the world starting a pandemic. Globally, to date, there have been >118 million confirmed cases, including >2 million deaths. In this context, it has been shown that the psychological impact of the pandemic is important and that it can be associated with an increase in internet searches related to fear, anxiety, depression, as well as protective behaviours, health knowledge and even maladaptive behaviours.Study design: This is a systematic review.Methods: This review aims to collect, analyse and synthesise available evidence on novel data streams for surveillance purposes and/or their potential for capturing the public reaction to epidemic outbreaks, particularly focusing on mental health effects and emotions.Results: At the end of the screening process, 19 articles were included in this systematic review. Our results show that the COVID-19 pandemic had a great impact on internet searches for mental health of entire populations, which manifests itself in a significant increase of depressed, anxious and stressed internet users' emotions.Conclusions: Novel data streams can support public health experts and policymakers in establishing priorities and setting up long-term strategies to mitigate symptoms and tackle mental health disorders. (C) 2021 The Royal Society for Public Health. Published by Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)44-52
Number of pages9
JournalPublic Health
Publication statusPublished - 1 Sept 2021


  • Novel data stream
  • Mental health
  • COVID-19
  • Twitter
  • Weibo
  • Google trends
  • Baidu
  • YouTube
  • Systematic review

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