A FAIR Principles-Driven Quality Assessment of Social Media Datasets for Natural Language Processing-Based Pandemic Surveillance

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Author(s): Yang Liu (North Carolina Central University, USA), May Almousa (Princess Nourah Bint Abdulrahman University, Saudi Arabia)and Mohd Anwar (North Carolina A&T State University, USA)
Copyright: 2026
Volume: 37
Issue: 1
Pages: 23
Source title: Journal of Database Management (JDM)
Editor(s)-in-Chief: Keng Siau (Singapore Management University, Singapore)
DOI: 10.4018/JDM.399759

Keywords: Data Mining and Databases / Databases / Engineering Science Reference / Library & Information Science

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Abstract

Social media has become integral to daily interactions and a key data source for researchers. Using COVID-19 as a case study, this work compares 24 social media datasets to address three research questions: 1) Is the dataset in compliance with the FAIR principles of being Findable, Accessible, Interoperable, and Reusable? 2) To what extent have people utilized social media to voice and exchange their apprehensions during the COVID-19 pandemic? 3) To what extent can social media datasets be utilized for natural language processing (NLP)-based COVID-19 pandemic surveillance? Leveraging the evaluation questions derived from the FAIR principles, the authors assess 24 social media datasets related to the COVID-19 pandemic. Additionally, they comprehensively analyze each dataset, including their composition, and the specific instances and features they encompass. They have initiated an attempt hoping that more researchers will join to create a data community where information can be repurposed and reused.

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A FAIR Principles-Driven Quality Assessment of Social Media Datasets for Natural Language Processing-Based Pandemic Surveillance

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