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Communication Dans Un Congrès Année : 2023

Analyzing COVID-Related Social Discourse on Twitter using Emotion, Sentiment, Political Bias, Stance, Veracity and Conspiracy Theories

Youri Peskine
Raphaël Troncy
Paolo Papotti

Résumé

Online misinformation has become a major concern in recent years, and it has been further emphasized during the COVID-19 pandemic. Social media platforms, such as Twitter, can be serious vectors of misinformation online. In order to better understand the spread of these fake-news, lies, deceptions, and rumours, we analyze the correlations between the following textual features in tweets: emotion, sentiment, political bias, stance, veracity and conspiracy theories. We train several transformer-based classifiers from multiple datasets to detect these textual features and identify potential correlations using conditional distributions of the labels. Our results show that the online discourse regarding some topics, such as COVID-19 regulations or conspiracy theories, is highly controversial and reflects the actual U.S. political landscape.
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Dates et versions

hal-04087004 , version 1 (02-05-2023)

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Youri Peskine, Raphaël Troncy, Paolo Papotti. Analyzing COVID-Related Social Discourse on Twitter using Emotion, Sentiment, Political Bias, Stance, Veracity and Conspiracy Theories. WWW 2023, Companion Proceedings of the ACM Web Conference 2023, 30 April-4 May 2023, Austin, USA, ACM, Apr 2023, Austin TX USA, United States. pp.688-693, ⟨10.1145/3543873.3587622⟩. ⟨hal-04087004⟩

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