Comment-profiler: Detecting trends and parasitic behaviors in online comments

Abstract

Can we detect anomalies and abuse among users of commenting platforms? Commenting has become a significant activity and specialized platforms provide commenting capability to many popular websites, such as Huffington Post. These platforms have become a new type of online social interaction, but have received very little attention. We conduct an extensive study on 19M comments from Disqus, one of the largest commenting platforms. Our work consists of two thrusts: (a) we identify features and patterns of commenting behavior, and (b) we detect peculiar and parasitic users. First, we study and evaluate features of user behavior that capture different aspects: user-user interaction (“social”), user-article interaction (“engagement”), and temporal properties. We also develop a method which we call, DownTimeFinder, to determine users’ downtime (think night-time) in their daily behavior, which helps identify three major groups of users based on their utilization (3, 9, 15 h of up-time). Second, we identify surprising and abnormal behaviors using our features. Interestingly, we find: (a) two tightly collaborative groups of size at least 29 users that seem to be promoting the same ideas, (b) 38 users with behavior that points to spamming and trolling activities, and (c) 19 different instances where Disqus is used as a chat room. The goal of our work is to highlight commenting platforms as an ignored, but information-rich, online activity.