  We present a machine learning framework that leverages a mixture of metadata,
network, and temporal features to detect extremist users, and predict content
adopters and interaction reciprocity in social media. We exploit a unique
dataset containing millions of tweets generated by more than 25 thousand users
who have been manually identified, reported, and suspended by Twitter due to
their involvement with extremist campaigns. We also leverage millions of tweets
generated by a random sample of 25 thousand regular users who were exposed to,
or consumed, extremist content. We carry out three forecasting tasks, (i) to
detect extremist users, (ii) to estimate whether regular users will adopt
extremist content, and finally (iii) to predict whether users will reciprocate
contacts initiated by extremists. All forecasting tasks are set up in two
scenarios: a post hoc (time independent) prediction task on aggregated data,
and a simulated real-time prediction task. The performance of our framework is
extremely promising, yielding in the different forecasting scenarios up to 93%
AUC for extremist user detection, up to 80% AUC for content adoption
prediction, and finally up to 72% AUC for interaction reciprocity forecasting.
We conclude by providing a thorough feature analysis that helps determine which
are the emerging signals that provide predictive power in different scenarios.
