Social Media Data as a Research Tool for Understanding COVID-19 Information Spread and Public Perception
A newly submitted arXiv preprint surveys how social media data has been used to study the COVID-19 pandemic, covering linguistic, visual, and emotional indicators in user posts. The chapter reviews machine learning, natural language processing, and feature engineering methods applied to platforms used by an estimated 4.6 billion people worldwide. It aims to consolidate existing research and outline directions for future work in pandemic-related social media analysis.
A preprint chapter submitted to arXiv in June 2026 provides a structured overview of research leveraging social media data during the COVID-19 pandemic. The authors examine how platforms became primary information sources during the crisis and explore the linguistic, visual, and emotional signals present in user-generated content. The chapter categorizes the types of social media data used across studies and surveys the range of computational methods deployed, including machine learning, natural language processing, and survey-based approaches. It also discusses how the large volume of information shared on these platforms can shape public perception and coping behaviors. The work concludes by identifying gaps and proposing directions for future research in this area.
What's missing
As a preprint chapter, this work has not yet undergone formal peer review. The chapter does not appear to specify the time range of studies reviewed, or the criteria used to select or exclude prior research, which are standard limitations for a systematic or scoping review. The 4.6 billion user figure lacks a cited source or reference date.
What different sources said
- arXiv cs.CLCenter
Leveraging Social Media Data for COVID-19 Studies
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