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Literature on COVID-19 Misinformation on Twitter
Research on COVID-19 misinformation on Twitter examines its propagation, sources, and mitigation. Studies indicate that while low-credibility information is present and often amplified by social bots and anti-vaccine communities, factual and science-based information is generally more prevalent and more frequently retweeted. Researchers also evaluate methods to combat false claims, finding that natural language processing (NLP) models can struggle to identify misinformation and that platform warning labels are largely ineffective at altering users' pre-existing beliefs.
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CSCW (Computer-supported cooperative work)
Computing Sciences
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Detecting COVID-19 Misinformation on Social Media
Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on Twitter
The COVID‑19 social media infodemic
Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset
Types, Sources, and Claims of COVID-19 Misinformation
A first look at COVID-19 information and misinformation sharing on Twitter
COVID-19 infodemic: More retweets for science-based information on coronavirus than for false information
Top concerns of tweeters during the COVID-19 pandemic: infoveillance study
Misinformation Warning Labels: Twitter's Soft Moderation Effects on COVID-19 Vaccine Belief Echoes
An exploratory study of COVID-19 misinformation on Twitter
Mining Trends of COVID-19 Vaccine Beliefs on Twitter with Lexical Embeddings
Study on the Prevalence of Low-Credibility Information on Twitter During the COVID-19 Outbreak