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 invol...

Social media platforms are used by an increasing number of extremist political actors for mobilization, recruiting or radicalization purposes. We propose a machine learning approach to support manual monitoring aiming at identifying right-wing extremist content in German Twitter profiles. We frame the task as profile classification, based on textual cues, traits of emotionality in language use,...

What can street gangs tell us about radicalization and extremist groups? At first glance, these two groups seem to push the boundaries of comparison. In this article, we examine the important similarities and differences across criminal, deviant, and extremist groups. Drawing from research on street gangs, this article explores issues such as levels of explanation,organizational structure, grou...