Decoding Hate: Using Experimental Text Analysis to Classify Terrorist Content

This paper uses automated text analysis – the process by which unstructured text is extracted, organised and processed into a meaningful format – to develop tools capable of analysing Islamic State (IS) propaganda at scale. Although we have used a static archive of IS material, the underlying principle is that these techniques can be deployed against content produced by any number of violent extremist movements in real‑time. This study therefore aims to complement work that looks at technology‑driven strategies employed by social media, video‑hosting and file‑sharing platforms to tackle violent extremist content disseminators. In general, these platforms aim to remove material produced by terrorist and hate organisations unless such material is disseminated in very specific contexts (such as, for instance, by journalists or academics). In recent years, the collective efforts of such platforms have become highly effective, with almost all terrorist content being removed before it has even been reported.


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