Online Library

• In this report, we show how recent terror attacks in the UK have been successfully exploited by anti-Muslim activists over social media, to increase their reach and grow their audiences.
• Monitoring key anti-Muslim social media accounts and their networks, we show how even small events are amplified through an international network of activists.
• We also provide concrete evidence of a leading anti-Muslim activist whose message is hugely amplified by the use of a 100+ strong ‘bot army’.
• The global reach, low price and lack of regulation on social media platforms presents new possibilities for independent, single issue and extremist viewpoints to gain significant audiences.
• We delve into the murky and secretive world of the dark web to explore just what tools are available for manipulating social media and show how easy it is to make use of these tactics even for non-tech savvy users.
• Through testing, we conclude that even cheaply inflating one’s number of followers has an effect on the ability to reach a larger audience.
• We situate these developments in the context of increasing hostility towards Muslims and immigration in the Europe and the US.
• “Trigger events” such as terror attacks, and other events that reflect badly on Muslims and Islam, cause both an increase in anti-Muslim hate on the street and, as we will show, also online.

In the context of the 2016 U.S. Presidential Election, President Donald Trump’s use of Twitter to connect with followers and supporters created unprecedented access to Trump’s online political campaign. In using the campaign slogan, “Make America Great Again” (or its acronym “MAGA”), Trump communicatively organized and controlled media systems by offering his followers an opportunity to connect with his campaign through the discursive hashtag. In effect, the strategic use of these networks over time communicatively constituted an effective and winning political organization; however, Trump’s political organization was not without connections to far-right and hate groups that coalesced in and around the hashtag. Semantic network analyses uncovered how the textual nature of #MAGA organized connections between hashtags, and, in doing so, exposed connections to overtly White supremacist groups within the United States and the United Kingdom throughout late November 2016. Cluster analyses further uncovered semantic connections to White supremacist and White nationalist groups throughout the hashtag networks connected to the central slogan of Trump’s presidential campaign. Theoretically, these findings contribute to the ways in which hashtag networks show how Trump’s support developed and united around particular organizing processes and White nationalist language, and provide insights into how these networks discursively create and connect White supremacists’ organizations to Trump’s campaign.

This study looks at how terror attacks are rendered discursively meaningful on social media through the concurrent use and reiteration of terror hashtags, which were created following previous incidents of terror. The article focuses on 12 terror attacks in Europe in 2015-2017 and their relating hashtags on Twitter, to see how various combinations of these were reused and co-articulated in tweets posted in relation to subsequent attacks. Through social network analysis of co-occurring hashtags in about 3 million tweets, in combination with close readings of a smaller sample, this study aims to analyze both the networks of hashtags in relation to terror attacks as well as the discursive process of hashtag co-articulation. The study shows that the patterns by which attack hashtags are reused and co-articulated depend on both temporal and contextual differences.

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.