Online Library

Countering online hate speech is a critical yet challenging task, but one which can be aided by the use of Natural Language Processing (NLP) techniques. Previous research has primarily focused on the development of NLP methods to automatically and effectively detect online hate speech while disregarding further action needed to calm and discourage individuals from using hate speech in the future. In addition, most existing hate speech datasets treat each post as an isolated instance, ignoring the conversational context. In this paper, we propose a novel task of generative hate speech intervention, where the goal is to automatically generate responses to intervene during online conversations that contain hate speech. As a part of this work, we introduce two fully-labeled large-scale hate speech intervention datasets collected from Gab and Reddit. These datasets provide conversation segments, hate speech labels, as well as intervention responses written by Mechanical Turk Workers. In this paper, we also analyze the datasets to understand the common intervention strategies and explore the performance of common automatic response generation methods on these new datasets to provide a benchmark for future research.

A fundamental idea from physics is that macroscopic transitions can occur as a result of an escalation in the correlated activity of a many-body system’s constituent particles. Here we apply this idea in an interdisciplinary setting, whereby the particles are individuals, their correlated activity involves online search activity surrounding the topics of social unrest, and the macroscopic phenomenon being measured are real-world protests. Our empirical study covers countries in Latin America during 2011–2014 using datasets assembled from multiple sources by subject matter experts. We find specifically that the volume and momentum of searches on Google Trends surrounding mass protest language, can detect – and may even pre-empt – the macroscopic on-street activity. Not only can this simple open-source solution prove an invaluable aid for monitoring civil order, our study serves to strengthen the increasing literature in the physics community aimed at understanding the collective dynamics of interacting populations of living objects across the life sciences.

Which news sources do supporters of populist islamophobic groups and their opponents rely on, and how are these sources related to each other? We explore these questions by studying the websites referenced in discussions surrounding Pegida, a right-wing populist movement based in Germany that is opposed to what its supporters regard as islamization, cultural marginalization and political correctness. We draw on a manual content analysis of the news sources and the stances of Twitter users, to then calculate the overlap of sources across audiences. Finally, we perform a cluster analysis of the resulting user groups, based on shared sources. Preferences by language, nationality, region and politics emerge, showing the distinction between different groups among the users. Our tentative findings have implications both for the study of mass media audiences through the lens of social media, and for research on the public sphere and its possible fragmentation in online discourse. This contribution, which is the result of an interdisciplinary collaboration between communication scholars in Germany and journalists in Austria, is part of a larger ongoing effort to understand forms of online extremism through the analysis of social media data

The research discusses ISIS’s Media Strategy towards western women by examining
the Tumblr blog UMM LAYTH. Written by a young woman from Scotland who
traveled to the Islamic State, the blog speaks about daily life under ISIS. The paper
gives a background on the author, the content and the blog’s style.

This chapter explores the intersection of white and male supremacy, both of which misrepresent women as genetically and intellectually inferior and reduce them to reproductive and/or sexual functions. The white power movement historically has been characterized by sexism and misogyny, as evidenced by the movement’s attempts to retain European heritage and maintain whiteness by policing the behavior and controlling the bodies of white women. However, the influence of white supremacist discourses on physically violent manifestations of the male supremacist movement remains largely understudied. Using supervised machine learning, we compare a corpus of violent male supremacist manifestos and other multimodal content with highly influential white nationalist texts and the manifestos of violent white supremacists to identify the shared beliefs, tropes and justifications for violence deployed within.

This article discusses the emergence of jihadist social media strategies, explains how the Syrian jihadist group Jabhat al-Nusra (JN) has used Twitter to disseminate content, and analyzes content shared by JN. Using an interdisciplinary approach to the analysis of jihadist propaganda, this article demonstrates how jihadist groups are using Twitter to disseminate links to video content shot on the battlefield in Syria and posted for mass consumption on YouTube.