Analyzing COVID-19 disinformation on Twitter using the hashtags #scamdemic and #plandemic: Retrospective study

Heather D. Lanier, Marlon I. Diaz, Sameh N Saleh, Christoph Lehmann, Richard J. Medford

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction The use of social media during the COVID-19 pandemic has led to an "infodemic" of mis- and disinformation with potentially grave consequences. To explore means of counteracting disinformation, we analyzed tweets containing the hashtags #Scamdemic and #Plandemic. Methods Using a Twitter scraping tool called twint, we collected 419,269 English-language tweets that contained “#Scamdemic” or “#Plandemic” posted in 2020. Using the Twitter application-programming interface, we extracted the same tweets (by tweet ID) with additional user metadata. We explored descriptive statistics of tweets including their content and user profiles, analyzed sentiments and emotions, performed topic modeling, and determined tweet availability in both datasets. Results After removal of retweets, replies, non-English tweets, or duplicate tweets, 40,081 users tweeted 227,067 times using our selected hashtags. The mean weekly sentiment was overall negative for both hashtags. One in five users who used these hashtags were suspended by Twitter by January 2021. Suspended accounts had an average of 610 followers and an average of 6.7 tweets per user, while active users had an average of 472 followers and an average of 5.4 tweets per user. The most frequent tweet topic was “Complaints against mandates introduced during the pandemic” (79,670 tweets), which included complaints against masks, social distancing, and closures. Discussion While social media has democratized speech, it also permits users to disseminate potentially unverified or misleading information that endangers people’s lives and public health interventions. Characterizing tweets and users that use hashtags associated with COVID-19 pandemic denial allowed us to understand the extent of misinformation. With the preponderance of inaccessible original tweets, we concluded that posters were in denial of the COVID-19 pandemic and sought to disperse related mis- or disinformation resulting in suspension. Conclusion Leveraging 227,067 tweets with the hashtags #scamdemic and #plandemic in 2020, we were able to elucidate important trends in public disinformation about the COVID-19 vaccine.

Original languageEnglish (US)
Article numbere0268409
JournalPloS one
Volume17
Issue number6 June
DOIs
StatePublished - Jun 2022

ASJC Scopus subject areas

  • General

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