Online Library

In the wake of the international refugee crisis, racist attitudes are becoming more publicly evident across the European Union. Propelled by the attacks in Köln on New Year’s Eve 2015 and harsher public sentiments on immigration, vigilante gangs have emerged in various European cities. These gangs mobilize through social media networks and claim to protect citizens from alleged violent and sexual attacks by refugees. This article analyzes how racist actors use social media to mobilize and organize street politics targeting refugees/immigrants. The aim is to explore the relation between social media and anti-refugee mobilization in a time of perceived insecurity and forced migration. The study uses the vigilante network Soldiers of Odin as a specific case, looking at (1) how they communicate through social media, (2) how they are represented in the large “alternative” space of right-wing online sites, and (3) how they are represented in traditional mainstream news. Using a critical adaption of Cammaerts’ theory of “mediation opportunity structure,” the article explicates the (inverted) rationale of racist online networks. Using quantitative and qualitative content analysis, both social media content and traditional news media are examined. The results show that although racist actors succeed in utilizing many of the opportunities embedded in social media communication and protest logic, they are also subject to constraints, such as a lack of public support and negative framing in news media. The article calls for more research on the (critical) relationship between uncivil engagement and social media networks.

How does the far right operate today? This volume presents a unique critical survey of the online and offline tactics, symbols and platforms that are strategically remixed to stake national and transnational cultural claims by contemporary far-right groups in Europe and North America. Featuring short, accessible analyses by an international range of expert scholars, policy advisors and activists, the book offers a plurality of answers to practical and theoretical questions about how and why the Internet has been crucial to emboldening extreme nationalisms in these regions and what counter-cultural approaches civil societies should develop in response.

With the spread of social networks and their unfortunate use for hate speech, automatic detection of the latter has become a pressing problem. In this paper, we reproduce seven state-of-the-art hate speech detection models from prior work, and show that they perform well only when tested on the same type of data they were trained on. Based on these results, we argue that for successful hate speech detection, model architecture is less important than the type of data and labeling criteria. We further show that all proposed detection techniques are brittle against adversaries who can (automatically) insert typos, change word boundaries or add innocuous words to the original hate speech. A combination of these methods is also effective against Google Perspective – a cuttingedge solution from industry. Our experiments demonstrate that adversarial training does not completely mitigate the attacks, and using character-level features makes the models systematically more
attack-resistant than using word-level features.

To prevent and counter the spread of illegal hate speech online, in May 2016, the Commission agreed with Facebook, Microsoft, Twitter and YouTube a “Code of conduct on countering illegal hate speech online”. The implementation of the Code of Conduct is evaluated through a regular monitoring exercise set up in collaboration with a network of organisations located in the different EU countries. Using a commonly agreed methodology, these organisations test how the IT companies are implementing the commitments in the Code.

This is an analysis of what the Alt-Right stands for and who its followers and leaders are. Including exclusive interviews with members of the movement and evidence linking extremists with terror attacks and hate crimes, it is clear that despite its high-profile support, the movement’s lack of a coherent base and its contradictory tendencies is already leading to its downfall.

In recent years, the increasing propagation of hate speech on social media and the urgent need for effective counter-measures have drawn significant investment from governments, companies, and empirical research. Despite a large number of emerging scientific studies to address the problem, a major limitation of existing work is the lack of comparative evaluations, which makes it difficult to assess the contribution of individual works. This paper introduces a new method based on a deep neural network combining convolutional and gated recurrent networks. We conduct an extensive evaluation of the method against several baselines and state of the art on the largest collection of publicly available Twitter datasets to date, and show that compared to previously reported results on these datasets, our proposed method is able to capture both word sequence and order information in short texts, and it sets new benchmark by outperforming on 6 out of 7 datasets by between 1 and 13% in F1. We also extend the existing dataset collection on this task by creating a new dataset covering different topics.