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Right-wing extremists and Islamist extremists try to recruit new followers by addressing their national (for instance, German) or religious (Muslim) social identity via online propaganda videos. Two studies examined whether capitalizing on a shared group-membership affects the emotional and cognitive response towards extremist propaganda. In both studies, Germans/non-migrants, Muslim migrants and control participants (N = 235) were confronted with right-wing extremist and Islamist extremist videos. Emotional and cognitive effects of students (Study 1) and apprentices (Study 2) were assessed. Results showed a general negative evaluation of extremist videos. More relevant, in-group propaganda led to more emotional costs in both studies. Yet, the responses varied depending on educational level: students reported more negative emotions and cognitions after in-group directed videos, while apprentices reported more positive emotions and cognitions after in-group directed propaganda. Results are discussed considering negative social identities.

A letter to Mr. Zuckerberg from the UN Rapporteur on the definition of terrorism.

VOX-Pol Blog post.

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.

Violence usually spread online, as it has spread in the past. With the increasing use of social media, the violence attributed to online hate speech has increased worldwide resulting rise in number of attacks on immigrants and other minorities. Analysis of such short text posts (e.g. tweets etc.) is valuable for identification of abusive language and hate speech. In this paper, we present Deep Context-Aware Embedding for the detection of Hate speech and abusive language on twitter. To improve the classification performance, we have enhanced the quality of the tweets by considering polsemy, syntax, semantic, OOV words as well as sentiment knowledge and concatenated to form input vector. We have used BiLSTM with attention modeling to identify tweet with hate speech. Experimental results showed significant improvement in the classification of tweets.

Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of the natural language constructs makes this task very challenging. We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity. Our experiments on a benchmark dataset of 16K annotated tweets show that such deep learning methods outperform state-of-the-art char/word n-gram methods by ~18 F1 points.