An exploratory study of COVID-19 misinformation on Twitter

Abstract

During the COVID-19 pandemic, social media has become a home ground for misinformation. To tackle this infodemic, scientific oversight, as well as a better understanding by practitioners in crisis management, is needed. We have conducted an exploratory study into the propagation, authors and content of misinformation on Twitter around the topic of COVID-19 in order to gain early insights. We have collected all tweets mentioned in the verdicts of fact-checked claims related to COVID-19 by over 92 professional fact-checking organisations between January and mid-July 2020 and share this corpus with the community. This resulted in 1500 tweets relating to 1274 false and 226 partially false claims, respectively. Exploratory analysis of author accounts revealed that the verified twitter handle(including Organisation/celebrity) are also involved in either creating(new tweets) or spreading(retweet) the misinformation. Additionally, we found that false claims propagate faster than partially false claims. Compare to a background corpus of COVID-19 tweets, tweets with misinformation are more often concerned with discrediting other information on social media. Authors use less tentative language and appear to be more driven by concerns of potential harm to others. Our results enable us to suggest gaps in the current scientific coverage of the topic as well as propose actions for authorities and social media users to counter misinformation.

Keywords: COVID-19; Coronavirus; Diffusion of information; Fake news; Misinformation; Social media; Twitter.

Fig. 1

An Illustration of data collection method- Extraction of social media link (Tweet Link) on the fact-checked article and fetching the relevant tweets from Twitter (screenshots from [54], [55]).

Fig. 2

An example of misinformation of false category.

Fig. 3

An example of Misinformation of partially false category.

Fig. 4

The language distribution of tweets with misinformation prior to translation of tweets.

Fig. 5

Timeline of misinformation tweets created during January 2020 to mid-July 2020.

Fig. 6

Frequency distribution of retweet(time window of 3 h) for false (blue) and partially false (orange) claims for each month- 6(a) January, 2020, 6(b) February, 2020, 6(c) March, 2020, 6(d) April, 2020, 6(e) May, 2020, 6(f) June, 2020, 6(g) July, 2020. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 7

Top 10 hashtags used in the tweets with misinformation. (Translation provided in blue where necessary).

Fig. 8

Top 10 emojis used in the tweets.

Fig. 9

LIWC results comparing COVID-19 background corpus to COVID-19 misinformation.