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Intelligence analysts face a difficult problem: distinguishing extremistrhetoric from potential extremist violence. Many are content to express abuseagainst some target group, but only a few indicate a willingness to engage inviolence. We address this problem by building a predictive model for intent,bootstrapping from a seed set of intent words, and language templatesexpressing intent. We design both an n-gram and attention-based deep learnerfor intent and use them as colearners to improve both the basis for predictionand the predictions themselves. They converge to stable predictions in a fewrounds. We merge predictions of intent with predictions of abusive language todetect posts that indicate a desire for violent action. We validate thepredictions by comparing them to crowd-sourced labelling. The methodology canbe applied to other linguistic properties for which a plausible starting pointcan be defined.

A Bootstrapped Model to Detect Abuse and Intent in White Supremacist Corpora