Coordinated Reply Attacks in Influence Operations: Characterization and Detection
This addresses the detection of influence operations targeting influential individuals like journalists and politicians, representing an incremental advance in social media analysis.
The study tackled the problem of coordinated reply attacks in online influence operations on Twitter by characterizing them and proposing two supervised machine-learning models for detection, achieving AUC scores of 0.88 for tweet classification and 0.97 for account classification.
Coordinated reply attacks are a tactic observed in online influence operations and other coordinated campaigns to support or harass targeted individuals, or influence them or their followers. Despite its potential to influence the public, past studies have yet to analyze or provide a methodology to detect this tactic. In this study, we characterize coordinated reply attacks in the context of influence operations on Twitter. Our analysis reveals that the primary targets of these attacks are influential people such as journalists, news media, state officials, and politicians. We propose two supervised machine-learning models, one to classify tweets to determine whether they are targeted by a reply attack, and one to classify accounts that reply to a targeted tweet to determine whether they are part of a coordinated attack. The classifiers achieve AUC scores of 0.88 and 0.97, respectively. These results indicate that accounts involved in reply attacks can be detected, and the targeted accounts themselves can serve as sensors for influence operation detection.