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Leveraging Social Media Data for COVID-19 Studies

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Computer Science > Social and Information Networks

arXiv:2606.10459 (cs)
COVID-19 e-print

Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field.

[Submitted on 9 Jun 2026]

Title:Leveraging Social Media Data for COVID-19 Studies

View a PDF of the paper titled Leveraging Social Media Data for COVID-19 Studies, by Nur Hafieza Ismail and 2 other authors
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Abstract:Nowadays, social media networks have become widely preferred sources of information. Especially during the time of the Coronavirus disease 2019 COVID 19 pandemic, social media has been one of the most used platforms to get the latest news and information related to COVID 19. Social media are popular because they offer free access to their registered users and allow them to do posting, disseminate information, and respond to others postings. With almost 4.6 billion social media users worldwide, it is not surprising the significant amount of information shared through these platforms could affect how people perceive and cope with the pandemic that we are facing right now. With decent use, social media can be a beneficial digital tool to spread reliable news and public awareness for patients, clinicians, and society. Specifically, this chapter describes linguistic, visual, and emotional indicators expressed in user disclosures. Thus, in this chapter, the related studies of social media platforms usage during the COVID 19 pandemic are explored and discussed in detail. This chapter also categorizes social media data used, introduces different deployed machine learning, feature engineering, natural language processing, and survey methods, and outlines directions for future research.
Comments: 8 pages, 1 figure
Subjects: Social and Information Networks (cs.SI); Computation and Language (cs.CL)
Cite as: arXiv:2606.10459 [cs.SI]
  (or arXiv:2606.10459v1 [cs.SI] for this version)
  https://doi.org/10.48550/arXiv.2606.10459
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Nur Shazwani Kamarudin [view email]
[v1] Tue, 9 Jun 2026 06:14:36 UTC (261 KB)
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