Radical Text Detection based on Stylometry

Abstract

The Internet has become an effective tool for terrorist and radical groups to spread their propaganda. One of the current problems is to detect these radical messages in order to block them or promote counter-narratives. In this work, we propose the use of stylometric methods for characterizing radical messages. We have used a machine learning approach to classify radical texts based on a corpus of news from radical sources such as the so-called ISIS online magazines Dabiq and Rumiyah, as well as news from general newspapers. The results show that stylometric features are effective for radical text classification.