DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhunis, Assem | ko |
dc.contributor.author | Lima, Gabriel | ko |
dc.contributor.author | Song, Hyeonho | ko |
dc.contributor.author | Han, Jiyoung | ko |
dc.contributor.author | Cha, Meeyoung | ko |
dc.date.accessioned | 2022-08-26T07:00:15Z | - |
dc.date.available | 2022-08-26T07:00:15Z | - |
dc.date.created | 2022-06-07 | - |
dc.date.created | 2022-06-07 | - |
dc.date.created | 2022-06-07 | - |
dc.date.created | 2022-06-07 | - |
dc.date.created | 2022-06-07 | - |
dc.date.created | 2022-06-07 | - |
dc.date.created | 2022-06-07 | - |
dc.date.issued | 2022-04-25 | - |
dc.identifier.citation | 31st ACM World Wide Web Conference, WWW 2022, pp.2603 - 2613 | - |
dc.identifier.uri | http://hdl.handle.net/10203/298139 | - |
dc.description.abstract | The COVID-19 pandemic has been the single most important global agenda in the past two years. In addition to its health and economic impacts, it has affected people's psychological states, including a rise in depression and domestic violence. We traced how the overall emotional states of individual Twitter users changed before and after the pandemic. Our data, including more than 9 million tweets posted by 9,493 users, suggest that the threat posed by the virus did not upset the emotional equilibrium of social media. In early 2020, COVID-related tweets skyrocketed in number and were filled with negative emotions; however, this emotional outburst was short-lived. We found that users who had expressed positive emotions in the pre-COVID period remained positive after the initial outbreak, while the opposite was true for those who regularly expressed negative emotions. Individuals achieved such emotional consistency by selectively focusing on emotion-reinforcing topics. The implications are discussed in light of an emotionally motivated confirmation bias, which we conceptualize as emotion bubbles that demonstrate the public's resilience to a global health risk. | - |
dc.language | English | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.title | Emotion Bubbles: Emotional Composition of Online Discourse Before and After the COVID-19 Outbreak | - |
dc.type | Conference | - |
dc.identifier.wosid | 000852713002066 | - |
dc.identifier.scopusid | 2-s2.0-85129792644 | - |
dc.type.rims | CONF | - |
dc.citation.beginningpage | 2603 | - |
dc.citation.endingpage | 2613 | - |
dc.citation.publicationname | 31st ACM World Wide Web Conference, WWW 2022 | - |
dc.identifier.conferencecountry | FR | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1145/3485447.3512132 | - |
dc.embargo.liftdate | 9999-12-31 | - |
dc.embargo.terms | 9999-12-31 | - |
dc.contributor.localauthor | Han, Jiyoung | - |
dc.contributor.localauthor | Cha, Meeyoung | - |
dc.contributor.nonIdAuthor | Zhunis, Assem | - |
dc.contributor.nonIdAuthor | Lima, Gabriel | - |
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