Analyzing COVID-Related Social Discourse on Twitter using Emotion, Sentiment, Political Bias, Stance, Veracity and Conspiracy Theories
ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023
; : 688-693, 2023.
Artículo
en Inglés
| Scopus | ID: covidwho-20241249
ABSTRACT
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. © 2023 ACM.
BERT; COVID-datasets; Misinformation; Natural Language Processing; Transformers; Classification (of information); Fake detection; Natural language processing systems; Social networking (online); COVID-dataset; Language processing; Multiple data sets; Natural languages; Social media platforms; Textual features; Transformer; COVID-19
Texto completo:
Disponible
Colección:
Bases de datos de organismos internacionales
Base de datos:
Scopus
Tipo de estudio:
Investigación cualitativa
Idioma:
Inglés
Revista:
ACM Web Conference 2023 - Companion of the World Wide Web Conference, WWW 2023
Año:
2023
Tipo del documento:
Artículo
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