Emotion Bubbles: Emotional Composition of Online Discourse Before and After the COVID-19 Outbreak

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dc.contributor.authorZhunis, Assemko
dc.contributor.authorLima, Gabrielko
dc.contributor.authorSong, Hyeonhoko
dc.contributor.authorHan, Jiyoungko
dc.contributor.authorCha, Meeyoungko
dc.date.accessioned2022-08-26T07:00:15Z-
dc.date.available2022-08-26T07:00:15Z-
dc.date.created2022-06-07-
dc.date.created2022-06-07-
dc.date.created2022-06-07-
dc.date.created2022-06-07-
dc.date.created2022-06-07-
dc.date.created2022-06-07-
dc.date.created2022-06-07-
dc.date.issued2022-04-25-
dc.identifier.citation31st ACM World Wide Web Conference, WWW 2022, pp.2603 - 2613-
dc.identifier.urihttp://hdl.handle.net/10203/298139-
dc.description.abstractThe 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.languageEnglish-
dc.publisherAssociation for Computing Machinery, Inc-
dc.titleEmotion Bubbles: Emotional Composition of Online Discourse Before and After the COVID-19 Outbreak-
dc.typeConference-
dc.identifier.wosid000852713002066-
dc.identifier.scopusid2-s2.0-85129792644-
dc.type.rimsCONF-
dc.citation.beginningpage2603-
dc.citation.endingpage2613-
dc.citation.publicationname31st ACM World Wide Web Conference, WWW 2022-
dc.identifier.conferencecountryFR-
dc.identifier.conferencelocationVirtual-
dc.identifier.doi10.1145/3485447.3512132-
dc.embargo.liftdate9999-12-31-
dc.embargo.terms9999-12-31-
dc.contributor.localauthorHan, Jiyoung-
dc.contributor.localauthorCha, Meeyoung-
dc.contributor.nonIdAuthorZhunis, Assem-
dc.contributor.nonIdAuthorLima, Gabriel-
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