# Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter

## TL;DR

This paper develops models based on textual, psychological, and behavioral signals to automatically identify radical content on Twitter, demonstrating high accuracy and highlighting psychological features as particularly distinctive.

## Contribution

It introduces a comprehensive approach combining textual, psychological, and behavioral signals to detect online radical content, validated on Twitter data.

## Key findings

- Psychological properties are highly distinguishing features.
- Vector embedding features outperform TF-IDF in detection accuracy.
- Models achieve high accuracy in identifying radical tweets.

## Abstract

The Internet and, in particular, Online Social Networks have changed the way that terrorist and extremist groups can influence and radicalise individuals. Recent reports show that the mode of operation of these groups starts by exposing a wide audience to extremist material online, before migrating them to less open online platforms for further radicalization. Thus, identifying radical content online is crucial to limit the reach and spread of the extremist narrative. In this paper, our aim is to identify measures to automatically detect radical content in social media. We identify several signals, including textual, psychological and behavioural, that together allow for the classification of radical messages. Our contribution is three-fold: (1) we analyze propaganda material published by extremist groups and create a contextual text-based model of radical content, (2) we build a model of psychological properties inferred from these material, and (3) we evaluate these models on Twitter to determine the extent to which it is possible to automatically identify online radical tweets. Our results show that radical users do exhibit distinguishable textual, psychological, and behavioural properties. We find that the psychological properties are among the most distinguishing features. Additionally, our results show that textual models using vector embedding features significantly improves the detection over TF-IDF features. We validate our approach on two experiments achieving high accuracy. Our findings can be utilized as signals for detecting online radicalization activities.