The dataset, titled the Twitch Plays Pokemon Dataset, contains 37.8 million IRC chat messages. It contains IRC chat log data for messages made between February 2, 2014 and April 23, 2014 (68 days). Each line denotes a single IRC chat message. Sample of the dataset: <date>2014-02-14</date><time>08:17:32</time><user>medicblue</user><msg>a</msg> <date>2014-02-14</date><time>08:17:32</time><user>murderousburger</user><msg>rare candy, RARE CANDY</msg> <date>2014-02-14</date><time>08:17:32</time><user>milk2978</user><msg>B</msg> <date>2014-02-14</date><time>08:17:32</time><user>mrtiktalik</user><msg>b</msg> <date>2014-02-14</date><time>08:17:32</time><user>dualhammers</user><msg>b</msg> <date>2014-02-14</date><time>08:17:32</time><user>shares5</user><msg>YES</msg> <date>2014-02-14</date><time>08:17:32</time><user>orangerust</user><msg>start</msg> <date>2014-02-14</date><time>08:17:32</time><user>snowiee</user><msg>a</msg> <date>2014-02-14</date><time>08:17:33</time><user>duroate</user><msg>down</msg> <date>2014-02-14</date><time>08:17:33</time><user>crypticcraig</user><msg>up</msg> <date>2014-02-14</date><time>08:17:33</time><user>doug2725</user><msg>LOL HELIX FOSSIL WENT BACK THAT FAR</msg> Abstract 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. You can view the full technical paper here: https://arxiv.org/abs/1902.06208 Source Code Code related to this dataset can be found at: https://github.com/ahaque/twitch-troll-detection