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Following Sahana Udupa and Matti Pohjonen’s (2019) invitation to move the debate beyond a normative understanding of hate speech, this article seeks to build a foundation for conceptual and empirical inquiry of speech commonly considered deviant and disturbing. It develops in three stages. It first maps the public lives of terms that refer to online vitriol and how they have been used by different communities of researchers, politicians, advocacy groups, and national organizations. Second, it shows how different types of “haters” have been interpreted as parts of “swarms” or “armies,” depending on whether their violent potential emerges around critical incidents or whether they respond to longer-term strategies through which communities and their leaders tie their speech acts to explicit narratives. The article concludes by locating “extreme speech” within this broader conceptual tapestry, arguing that the paternalistic the gaze that characterizes a lot of research on online hate speech is tied to what Chantal Mouffe has referred to as the “moralization of politics,” a phenomenon that cannot be matched by responses that are themselves moral.

A balkanized Internet beset by hostile cyber-related activities raises a host of questions and problems for the U.S. government, American corporations, and American citizens. The Council on Foreign Relations launched this Task Force to define the scope of this rapidly developing issue and to help shape the norms, rules, and laws that should govern the Internet. This is the report published by the Task Force.

VOX-Pol Blog post.

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.

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.

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.