Detecting Propagators of Disinformation on Twitter Using Quantitative Discursive Analysis
This addresses the need for government entities to counter foreign influence on democratic elections by detecting disinformation propagators, but it is incremental as it applies existing methods to a specific domain.
The study tackled the problem of identifying Russian disinformation bots on Twitter during the 2016 U.S. Presidential Election, achieving a statistically significant classification with an MCC of 0.9070, though it was highly sensitive to true positives but not true negatives.
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.