A socialbots analysis-driven graph-based approach for identifying coordinated campaigns in twitter

Twitter is a popular microblogging platform, which facilitates users to express views and thoughts on day-to-day events using short texts limited to a maximum of 280 characters. However, it is generally targeted by socialbots for political astroturfing, advertising, spamming, and other illicit activities due to its open and real-time information sharing and dissemination nature. In this paper, we present a socialbots analysis-driven graph-based approach for identifying coordinated campaigns among Twitter users. To this end, we first present statistical insights derived from the analysis of logged data of 98 socialbots, which were injected in Twitter and associated with top-six Twitter using countries. In the analysis, we study and present the impact of socialbots’ profile features, such as age and gender on infiltration. We also present a multi-attributed graph-based approach to model the profile attributes and interaction behavior of users as a similarity graph for identifying different groups of synchronized users involved in coordinated campaigns. The proposed approach is experimentally evaluated using four different evaluation parameters over a real dataset containing socialbots’ trapped user profiles. The evaluation of identified campaigns in the form of clusters reveals the traces of spammers, botnets, and other malicious users.