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Analyzing Public Opinion and Misinformation in a COVID-19 Telegram Group Chat

We analyze a Singapore-based COVID-19 Telegram group with more than 10000 participants. First, we study the group’s opinion over time, focusing on five dimensions: participation, sentiment, negative emotions, topics, and message types. We find that participation peaked when the Ministry of Health ra...

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Detalles Bibliográficos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: IEEE 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9280806/
https://www.ncbi.nlm.nih.gov/pubmed/35938074
http://dx.doi.org/10.1109/MIC.2020.3040516
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description We analyze a Singapore-based COVID-19 Telegram group with more than 10000 participants. First, we study the group’s opinion over time, focusing on five dimensions: participation, sentiment, negative emotions, topics, and message types. We find that participation peaked when the Ministry of Health raised the disease alert level, but this engagement was not sustained. Second, we investigate the prevalence of, and reactions to, authority-identified misinformation in the group. We find that authority-identified misinformation is rare, and that participants affirm, deny, and question misinformation. Third, we explore searching for user skepticism as one strategy for identifying misinformation, finding misinformation not previously identified by authorities.
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spelling pubmed-92808062022-08-01 Analyzing Public Opinion and Misinformation in a COVID-19 Telegram Group Chat IEEE Internet Comput Theme Article: CYBER SOCIAL HEALTH We analyze a Singapore-based COVID-19 Telegram group with more than 10000 participants. First, we study the group’s opinion over time, focusing on five dimensions: participation, sentiment, negative emotions, topics, and message types. We find that participation peaked when the Ministry of Health raised the disease alert level, but this engagement was not sustained. Second, we investigate the prevalence of, and reactions to, authority-identified misinformation in the group. We find that authority-identified misinformation is rare, and that participants affirm, deny, and question misinformation. Third, we explore searching for user skepticism as one strategy for identifying misinformation, finding misinformation not previously identified by authorities. IEEE 2020-12-11 /pmc/articles/PMC9280806/ /pubmed/35938074 http://dx.doi.org/10.1109/MIC.2020.3040516 Text en This article is free to access and download, along with rights for full text and data mining, re-use and analysis.
spellingShingle Theme Article: CYBER SOCIAL HEALTH
Analyzing Public Opinion and Misinformation in a COVID-19 Telegram Group Chat
title Analyzing Public Opinion and Misinformation in a COVID-19 Telegram Group Chat
title_full Analyzing Public Opinion and Misinformation in a COVID-19 Telegram Group Chat
title_fullStr Analyzing Public Opinion and Misinformation in a COVID-19 Telegram Group Chat
title_full_unstemmed Analyzing Public Opinion and Misinformation in a COVID-19 Telegram Group Chat
title_short Analyzing Public Opinion and Misinformation in a COVID-19 Telegram Group Chat
title_sort analyzing public opinion and misinformation in a covid-19 telegram group chat
topic Theme Article: CYBER SOCIAL HEALTH
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9280806/
https://www.ncbi.nlm.nih.gov/pubmed/35938074
http://dx.doi.org/10.1109/MIC.2020.3040516
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