TY - JOUR
T1 - Examining thematic and emotional differences across Twitter, Reddit, and YouTube
T2 - The case of COVID-19 vaccine side effects
AU - Kwon, Soyeon
AU - Park, Albert
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/7
Y1 - 2023/7
N2 - Social media discourse has become a key data source for understanding the public's perception of, and sentiments 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.
AB - Social media discourse has become a key data source for understanding the public's perception of, and sentiments 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.
KW - Consumer health information
KW - Schema theory
KW - Social media
KW - Social network analysis
KW - Unsupervised machine learning
UR - http://www.scopus.com/inward/record.url?scp=85150448014&partnerID=8YFLogxK
U2 - 10.1016/j.chb.2023.107734
DO - 10.1016/j.chb.2023.107734
M3 - Article
AN - SCOPUS:85150448014
SN - 0747-5632
VL - 144
JO - Computers in Human Behavior
JF - Computers in Human Behavior
M1 - 107734
ER -