Cited 20 time in
Examining thematic and emotional differences across Twitter, Reddit, and YouTube: The case of COVID-19 vaccine side effects
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kwon, Soyeon | - |
| dc.contributor.author | Park, Albert | - |
| dc.date.accessioned | 2024-08-08T10:01:32Z | - |
| dc.date.available | 2024-08-08T10:01:32Z | - |
| dc.date.issued | 2023-07 | - |
| dc.identifier.issn | 0747-5632 | - |
| dc.identifier.issn | 1873-7692 | - |
| dc.identifier.uri | https://scholarworks.dongguk.edu/handle/sw.dongguk/21252 | - |
| dc.description.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. | - |
| dc.format.extent | 15 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | Elsevier Ltd | - |
| dc.title | Examining thematic and emotional differences across Twitter, Reddit, and YouTube: The case of COVID-19 vaccine side effects | - |
| dc.type | Article | - |
| dc.publisher.location | 네델란드 | - |
| dc.identifier.doi | 10.1016/j.chb.2023.107734 | - |
| dc.identifier.scopusid | 2-s2.0-85150448014 | - |
| dc.identifier.wosid | 000959428000001 | - |
| dc.identifier.bibliographicCitation | Computers in Human Behavior, v.144, pp 1 - 15 | - |
| dc.citation.title | Computers in Human Behavior | - |
| dc.citation.volume | 144 | - |
| dc.citation.startPage | 1 | - |
| dc.citation.endPage | 15 | - |
| dc.type.docType | Article | - |
| dc.description.isOpenAccess | Y | - |
| dc.description.journalRegisteredClass | ssci | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Psychology | - |
| dc.relation.journalWebOfScienceCategory | Psychology, Multidisciplinary | - |
| dc.relation.journalWebOfScienceCategory | Psychology, Experimental | - |
| dc.subject.keywordPlus | SOCIAL MEDIA | - |
| dc.subject.keywordPlus | FEAR APPEALS | - |
| dc.subject.keywordPlus | ONLINE | - |
| dc.subject.keywordPlus | COMMUNICATION | - |
| dc.subject.keywordPlus | AFFORDANCES | - |
| dc.subject.keywordPlus | SENTIMENT | - |
| dc.subject.keywordPlus | FEATURES | - |
| dc.subject.keywordPlus | ANGER | - |
| dc.subject.keywordPlus | NEED | - |
| dc.subject.keywordAuthor | Social media | - |
| dc.subject.keywordAuthor | Consumer health information | - |
| dc.subject.keywordAuthor | Schema theory | - |
| dc.subject.keywordAuthor | Unsupervised machine learning | - |
| dc.subject.keywordAuthor | Social network analysis | - |
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