  Commenting platforms, such as Disqus, have emerged as a major online
communication platform with millions of users and posts. Their popularity has
also attracted parasitic and malicious behav- iors, such as trolling and
spamming. There has been relatively little research on modeling and
safeguarding these platforms. As our key contribution, we develop a systematic
approach to detect malicious users on commenting platforms focusing on having:
(a) interpretable, and (b) fine-grained classification of malice. Our work has
two key novelties: (a) we propose two classifications methods, with one
following a two stage approach, which first maps observ- able features to
behaviors and then maps these behaviors to user roles, and (b) we use a
comprehensive set of 73 features that span four dimensions of information. We
use 7 million comments during a 9 month period, and we show that our
classification methods can distinguish between benign, and malicious roles
(spammers, trollers, and fanatics) with a 0.904 AUC. Our work is a solid step
to- wards ensuring that commenting platforms are a safe and pleasant medium for
the exchange of ideas.
