Kouzy, R., Abi Jaoude, J., Kraitem, A., El Alam, M. B., Karam, B., Adib, E., ... & Baddour, K. (2020). Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on Twitter. Cureus, 12(3). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7152572/ 

University of Michigan - Ann Arbor

Covid-19 misinformation on Twitter is a major problem that needs to be addressed because there is a lot of false or unverifiable information present. After studying whether the quality and type of Tweet is associated with the likeliness of containing misinformation, it is concluded that the number of likes and re-Tweets does not affect how much misinformation Tweets contained in general 

Coronavirus goes viral: quantifying the COVID-19 misinformation epidemic on Twitter

- Obtained 673 Tweets from the Twitter Archiver add-on search on February 27, 2020 that contained 14 different trending hashtags (446 or 66.6% of the Tweets were from informal accounts and 129 or 19.2% came from verified Twitter accounts)
- Targeted Tweets that had at least one of the 11 common hashtags and 3 keywords that were related to Covid-19 and excluded Tweets with fewer than 4 re-Tweets (hashtags not case sensitive)
- Randomly selected 50 Tweets out of more than 100 Tweets that fit the search criteria
- Categorized the 50 Tweets based on content tone (serious, humorous, opinions) and content type (medical/public health, financial, sociopolitical)
- Compared Tweets with information obtained from WHO, CDC, peer-reviewed scientific journals, and prominent new outlets to identify any potential misinformation 
- If information presented in a Tweet cannot be verified using these sources, it is considered unverifiable rather than misinformative 

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