Abstract
The current COVID-19 pandemic and its uncertainty have given rise to various myths and rumours. These myths spread incredibly fast through social media, which has caused massive panic in society. In this paper, we comprehensively examined the prevailing myths related to COVID-19 in regard to the diffusion of myths, people's engagement with myths and people's subjective emotions to myths. First, we classified the myths into five categories: spread of infection, preventive measures, detection measures, treatment and miscellaneous. We collected the tweets about each category of myths from 1 January to 7 July in the year 2020. We found that the vast majority of the myth tweets were about the spread of the infection. Next, we fitted myths spreading with the SIR epidemic model and calculated the basic reproduction number n}{}R_0 for each category of myths. We observed that the myths about the spread of infection and preventive measures propagated faster than other categories of myths, and more miscellaneous myths raised and quickly spread from later June 2020. We further analyzed people's emotions evoked by each category of myths and found that fear was the strongest emotion in all categories of myths and around 64% of the collected tweets expressed the emotion of fear. The study in this paper provides insights for authorities and governments to understand the myths during the eruption of the pandemic, and hence enable targeted and feasible measures to demystify the most concerned myths in due time.
Highlights
Myths have been widely prevalent with various common infections from time to time, including Tuberculosis [1], Leprosy [2], and Flu [3]
In early April 2020, a conspiracy theory claimed 5G can spread the coronavirus [4]. This myth spread across the UK, and caused physical damages to mobile phone masts in Birmingham, England, even if the World Health Organisation (WHO) has clarified: “viruses cannot travel on radio waves/mobile networks”
We model the spread of myth tweets with the SIR epidemic model, which characterizes the basic reproduction number (R0), where R0 > 1 indicates the possibility of an infodemic [14]
Summary
Myths have been widely prevalent with various common infections from time to time, including Tuberculosis [1], Leprosy [2], and Flu [3]. To measure people’s interaction and engagement with COVID-19 infodemics, Cinelli et al [8] model the spread of information with epidemic propagation models and compare the information diffusion speed on different social media platforms They collected around 8 million posts from five popular social media platforms: Gab, Reddit, Instagram, YouTube and Twitter, between 1 January 2020 and 14 February 2020. To analyze emotions evoked by news headlines of COVID-19 outbreak, Aslam et al [17] collected 141 thousand headlines carrying keyword coronavirus from top 25 English news sources from 15 January 2020 to 3 June 2020 They adopt the National Research Council Canada (NRC) Word-Emotion Lexicon [18] to calculate the presence of eight basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy and disgust) and their corresponding valence in each news headline. Zhou et al [23], [24] analysed sentiment and depression dynamics for people in much fine-grained local government areas across the New South Wales state in Australia
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