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

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Meeyoung ChaAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 2603 - 2613

https://doi.org/10.1145/3485447.3512132

Published: 25 April 2022 Publication History

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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.

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Index Terms

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

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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National Research Foundation of Korea
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WWW '22: The ACM Web Conference 2022

April 25 - 29, 2022

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Cited By

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https://doi.org/10.1371/journal.pone.0296855
Wu FLakhanpal SLi QLee KKim DChae HKwon KChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Not All Asians are the Same: A Disaggregated Approach to Identifying Anti-Asian Racism in Social MediaProceedings of the ACM Web Conference 202410.1145/3589334.3645630(2615-2626)Online publication date: 13-May-2024
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Chin HLima GShin MZhunis ACha CChoi JCha M(2023)User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment AnalysisJournal of Medical Internet Research10.2196/4092225(e40922)Online publication date: 27-Jan-2023
https://doi.org/10.2196/40922
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