Examining thematic and emotional differences across Twitter, Reddit, and YouTube: The case of COVID-19 vaccine side effectsopen access
- Authors
- Kwon, Soyeon; Park, Albert
- Issue Date
- Jul-2023
- Publisher
- Elsevier Ltd
- Keywords
- Social media; Consumer health information; Schema theory; Unsupervised machine learning; Social network analysis
- Citation
- Computers in Human Behavior, v.144, pp 1 - 15
- Pages
- 15
- Indexed
- SSCI
SCOPUS
- Journal Title
- Computers in Human Behavior
- Volume
- 144
- Start Page
- 1
- End Page
- 15
- URI
- https://scholarworks.dongguk.edu/handle/sw.dongguk/21252
- DOI
- 10.1016/j.chb.2023.107734
- ISSN
- 0747-5632
1873-7692
- Abstract
- Social media discourse has become a key data source for understanding the public's perception of, and senti-ments during a public health crisis. However, given the different niches which platforms occupy in terms of information exchange, reliance on a single platform would provide an incomplete picture of public opinions. Based on the schema theory, this study suggests a 'social media platform schema' to indicate users' different expectations based on previous usages of platform and argues that a platform's distinct characteristics foster distinct platform schema and, in turn, distinct nature of information. We analyzed COVID-19 vaccine side effect -related discussions from Twitter, Reddit, and YouTube, each of which represents a different type of the platform, and found thematic and emotional differences across platforms. Thematic analysis using k-means clustering algorithm identified seven clusters in each platform. To computationally group and contrast thematic clusters across platforms, we employed modularity analysis using the Louvain algorithm to determine a semantic network structure based on themes. We also observed differences in emotional contexts across platforms. Theoretical and public health implications are then discussed.
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Collections - Dongguk Business School > Department of Management Information System > 1. Journal Articles

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