Mining Multiplatform Opinions During Public Health Crisis

A Comparative Study

Bibliographic Data

ID22106945
AuthorsKun Sun (0000-0001-9766-269X, Jinan University), Tian-Fang Zhao (0000-0002-3520-2951, Jinan University), Xiao-Kun Wu (0000-0003-3843-0164, South China University of Technology), Liang Yang (0000-0002-6576-332X, Hebei University of Technology), Di Jin (0009-0001-4646-5220, Tianjin University), Wei–Neng Chen (0000-0003-0843-5802, South China University of Technology)
Year2024
Volume11
Issue2
Pages2121-2134
Publication date2024-04-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueIEEE Transactions on Computational Social Systems (JOURNAL)
Journal identifiersISSN: 2329-924X • E-ISSN: 2373-7476
PublisherInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/tcss.2023.3301951
OpenAlexW4386025648
LanguageEN
Citations received2
References cited35

Emerging infectious diseases pose a growing threat to human society and have sparked extensive public discussions on social media. Although numerous efforts have been made in health data mining on social media, there is a lack of focus on quantitative comparisons across multiple platforms, despite their crucial role in the holistic social communication system. This study addresses this gap by developing a generalized regression model that considers the distinct attributes of social media platforms, including short-text, long-text, and Eastern or Western orientation. Using Monkeypox as an application case, this study examines differences among platforms based on four factors: user characteristics, text topics, text emotion, and text quality. The modeling and regression results reveal significant heterogeneity in public opinion expressions across different platforms, particularly between long-text and short-text platforms. Users on short-text platforms are more exposed to diverse information and tend to be susceptible to emotionally provocative content. On the other hand, users on long-text platforms prefer in-depth discussions and show greater receptivity to content infused with positive emotions. This study reveals the information bias brought by platform differences and contributes to data-driven modeling in social communication systems

Data science · Internet privacy · Political science · Public health · Public opinion · Social media · Topic model · World Wide Web · Computer Science · Hate Speech and Cyberbullying Detection · Medicine · Misinformation and Its Impacts · Social Media and Politics · Artificial Intelligence

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