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