Bot-Hunter was used to detect bots in both the informed and misinformed communities. A two-sample $$z$$-test was then conducted to evaluate the difference in the proportion of bots between the two groups.

University of California, Berkeley

Google

With the data the researchers extracted, they analyzed the communities, bots, linguistics, network, tone, and vaccination stance of the tweets and users. 

Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset data analysis

Memon, S. A., & Carley, K. M. (2020). Characterizing covid-19 misinformation communities using a novel twitter dataset. arXiv preprint arXiv:2008.00791. https://arxiv.org/abs/2008.00791

Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset

Two communities were derived using the annotated tweets. A valence value of +1 was given to the categories of annotated tweets that fall into "True Treatment", "True Prevention", "Correction/Calling Out", "Sarcasm/Satire", and
"True Public Health Response", and a valence value of -1 was given to the categories of annotated tweets that fall into "Conspiracy," "Fake Cure", "Fake Treatment",
"False Fact or Prevention", and "False Public Health Response." Users' valence values are calculated and placed into three groups: >0 "informed", <0 misinformed, and 0 "ambiguous." 

Of the 3629 users, 1697 (47%) were placed into the informed group, 1043 (29%) were placed into the misinformed group, and 889 (24%) were ambiguous. 

Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Dataset communities

To compute network density of the two communities, they took the ratio of actual connections and potential connections using retweets, replies, and mentions networks of the tweets in their data.

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