Solutions to Detect and Analyze Online Radicalization

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This paper discusses automated methods for detecting online radicalization and radical communities on various platforms, from websites, to forums, to social media platforms such as Facebook, YouTube, Twitter, and others. The authors survey 40 papers published between 2003 and 2011 and suggest a classification scheme for the literature by analyzing techniques, identifying trends, and discussing limitations and research gaps. The authors argue that the challenge is making use of the vast quantity of data online, and effectively analyzing it.

This article will be of use to practitioners and researchers looking at the classification and detection of radical and extremist content online, but also those with a wider interest in identifying trends, online content detection, and content classification more generally. In terms of detection, the authors group methods into “link-based bootstrapping” techniques, text classification techniques, and others. In terms of automated analysis of radical content, the authors discuss methods from the literature that are both network based and content based.

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Denzil Correa and Ashish Sureka