Detecting Influential Users and Communities in Censored Tweets Using Data-Flow Graphs

Current literature on social media censorship examined various aspects of censorship. However, the relationship among censored social media users has received much less attention. We address this gap in the literature by constructing a complete dynamic data-flow graph that models the communication between users, identifies influential users, and utilizes a wide variety of metadata embedded in tweets to follow data paths of censored tweets. Using a dataset that includes over 25 million tweets from Turkey, our analysis is based on 712,218 unique, censored tweets, associated with 13,056 distinct users. By applying a modularity metric to find user communities, our graph identified 5 large communities with influential users who are supporters of the Kurdish separatist movement, and famous accounts that had active social media involvement during the graft scandal of December 2013. In addition, using a machine-learning topic clustering algorithms to extract popular censored topics and keywords within these communities, we found that the largest community frequently mentioned topics regarding the leaked court documents of the corruption case, while the next four largest communities frequently referenced topics criticizing the military strikes against the separatist groups.