  Efforts by foreign actors to influence public opinion have gained
considerable attention because of their potential to impact democratic
elections. Thus, the ability to identify and counter sources of disinformation
is increasingly becoming a top priority for government entities in order to
protect the integrity of democratic processes. This study presents a method of
identifying Russian disinformation bots on Twitter using centering resonance
analysis and Clauset-Newman-Moore community detection. The data reflect a
significant degree of discursive dissimilarity between known Russian
disinformation bots and a control set of Twitter users during the timeframe of
the 2016 U.S. Presidential Election. The data also demonstrate statistically
significant classification capabilities (MCC = 0.9070) based on community
clustering. The prediction algorithm is very effective at identifying true
positives (bots), but is not able to resolve true negatives (non-bots) because
of the lack of discursive similarity between control users. This leads to a
highly sensitive means of identifying propagators of disinformation with a high
degree of discursive similarity on Twitter, with implications for limiting the
spread of disinformation that could impact democratic processes.
