  With the increasing importance of online communities, discussion forums, and
customer reviews, Internet "trolls" have proliferated thereby making it
difficult for information seekers to find relevant and correct information. In
this paper, we consider the problem of detecting and identifying Internet
trolls, almost all of which are human agents. Identifying a human agent among a
human population presents significant challenges compared to detecting
automated spam or computerized robots. To learn a troll's behavior, we use
contextual anomaly detection to profile each chat user. Using clustering and
distance-based methods, we use contextual data such as the group's current
goal, the current time, and the username to classify each point as an anomaly.
A user whose features significantly differ from the norm will be classified as
a troll. We collected 38 million data points from the viral Internet fad,
Twitch Plays Pokemon. Using clustering and distance-based methods, we develop
heuristics for identifying trolls. Using MapReduce techniques for preprocessing
and user profiling, we are able to classify trolls based on 10 features
extracted from a user's lifetime history.
