Predicting Misinformation and Engagement in COVID-19 Twitter Discourse in the First Months of the Outbreak

• URL: http://arxiv.org/abs/2012.02164v2
• Date: Wed, 23 Dec 2020 15:13:31 GMT
• Title: Predicting Misinformation and Engagement in COVID-19 Twitter Discourse in the First Months of the Outbreak
• Authors: Mirela Silva, Fabr\'icio Ceschin, Prakash Shrestha, Christopher Brant, Juliana Fernandes, Catia S. Silva, Andr\'e Gr\'egio, Daniela Oliveira, and Luiz Giovanini
• Abstract summary: We examine nearly 505K COVID-19-related tweets from the initial months of the pandemic to understand misinformation as a function of bot-behavior and engagement. We found that real users tweet both facts and misinformation, while bots tweet proportionally more misinformation.
• Score: 1.2059055685264957
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Disinformation entails the purposeful dissemination of falsehoods towards a greater dubious agenda and the chaotic fracturing of a society. The general public has grown aware of the misuse of social media towards these nefarious ends, where even global public health crises have not been immune to misinformation (deceptive content spread without intended malice). In this paper, we examine nearly 505K COVID-19-related tweets from the initial months of the pandemic to understand misinformation as a function of bot-behavior and engagement. Using a correlation-based feature selection method, we selected the 11 most relevant feature subsets among over 170 features to distinguish misinformation from facts, and to predict highly engaging misinformation tweets about COVID-19. We achieved an average F-score of at least 72\% with ten popular multi-class classifiers, reinforcing the relevance of the selected features. We found that (i) real users tweet both facts and misinformation, while bots tweet proportionally more misinformation; (ii) misinformation tweets were less engaging than facts; (iii) the textual content of a tweet was the most important to distinguish fact from misinformation while (iv) user account metadata and human-like activity were most important to predict high engagement in factual and misinformation tweets; and (v) sentiment features were not relevant.

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