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When Infodemic Meets Epidemic: a Systematic Literature Review

arXiv 2210.04612 ↗First posted 3 Oct 2022cs.SI cs.AIcited by 0

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Abstract (arXiv)

Epidemics and outbreaks present arduous challenges requiring both individual and communal efforts. Social media offer significant amounts of data that can be leveraged for bio-surveillance. They also provide a platform to quickly and efficiently reach a sizeable percentage of the population, hence their potential impact on various aspects of epidemic mitigation. The general objective of this systematic literature review is to provide a methodical overview of the integration of social media in different epidemic-related contexts. Three research questions were conceptualized for this review, resulting in over 10000 publications collected in the first PRISMA stage, 129 of which were selected for inclusion. A thematic method-oriented synthesis was undertaken and identified 5 main themes related to social media enabled epidemic surveillance, misinformation management, and mental health. Findings uncover a need for more robust applications of the lessons learned from epidemic post-mortem documentation. A vast gap exists between retrospective analysis of epidemic management and result integration in prospective studies. Harnessing the full potential of social media in epidemic related tasks requires streamlining the results of epidemic forecasting, public opinion understanding and misinformation propagation, all while keeping abreast of potential mental health implications. Pro-active prevention has thus become vital for epidemic curtailment and containment.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 169 references that are in the map. First posted on arXiv 3 Oct 2022.

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7 of 169 references.

References · 7

  1. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  2. 2018FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Mediacited by 285
  3. 2020CoAID: COVID-19 Healthcare Misinformation Datasetcited by 84
  4. 2020An Exploratory Study of COVID-19 Misinformation on Twittercited by 33
  5. 2021Analyzing COVID-19 Tweets with Transformer-based Language Modelscited by 5
  6. 2016Tracking Dengue Epidemics using Twitter Content Classification and Topic Modellingcited by 2
  7. 2020Understanding the Socio-Economic Disruption in the United States during COVID-19's Early Dayscited by 1

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