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The emergence of user forums in electronic news media has given rise to the proliferation of opinion manipulation trolls. Finding such trolls automatically is a hard task, as there is no easy way to recognize or even to define what they are; this also makes it hard to get training and testing data. We solve this issue pragmatically: we assume that a user who is called a troll by several people is likely to be one. We experiment with different variations of this definition, and in each case we show that we can train a classifier to distinguish a likely troll from a non-troll with very high accuracy, 82‐95%, thanks to our rich feature set.
Mihaylov et al. (Thu,) studied this question.
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