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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.

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.

Over the course of the 2016 Presidential campaign, an execrable trend has emerged: reporters who voiced even slightly negative opinions about presidential candidate Donald Trump have been targeted relentlessly on social media by the candidate’s self-styled supporters; reporters who are Jewish (or are perceived to be Jewish) have borne the brunt of these attacks. There is evidence that Mr. Trump himself may have contributed to an environment in which reporters were targeted. Indeed, he repeatedly denounced reporters as “absolute scum,” and said of “most journalists” in December 2015, “I would never kill them, but I do hate them. And some of them are such lying, disgusting people. It’s true.” Accordingly, while we cannot (and do not) say that the candidate caused the targeting of reporters, we can say that he may have created an atmosphere in which
such targeting arose.

The social media attacks on journalists were brutal.

This report presents an analysis of the
production and propagation of online
antagonistic content related to Jews
posted on Twitter between October 2015
and October 2016 in the UK.

In this paper, we conduct a comprehensive study of online antagonistic content related to Jewish identity posted on Twitter between October 2015 and October 2016 by UK-based users. We trained a scalable supervised machine learning classifier to identify antisemitic content to reveal patterns of online antisemitism perpetration at the source. We built statistical models to analyse the inhibiting and enabling factors of the size (number of retweets) and survival (duration of retweets) of information flows in addition to the production of online antagonistic content. Despite observing high temporal variability, we found that only a small proportion (0.7%) of the content was antagonistic. We also found that antagonistic content was less likely to disseminate in size or survive fora longer period. Information flows from antisemitic agents on Twitter gained less traction, while information flows emanating from capable and willing counter-speech actors -i.e. Jewish organisations- had a significantly higher size and survival rates. This study is the first to demonstrate that Sampson’s classic sociological concept of collective efficacy can be observed on social media (SM). Our findings suggest that when organisations aiming to counter harmful narratives become active on SM platforms, their messages propagate further and achieve greater longevity than antagonistic messages. On SM, counter-speech posted by credible, capable and willing actors can be an effective measure to prevent harmful narratives. Based on our findings, we underline the value of the work by community organisations in reducing the propagation of cyberhate and increasing trust in SM platforms.

In this article, we conduct a comprehensive study of online antagonistic content related to Jewish identity posted on Twitter between October 2015 and October 2016 by UK-based users. We trained a scalable supervised machine learning classifier to identify antisemitic content to reveal patterns of online antisemitism perpetration at the source. We built statistical models to analyze the inhibiting and enabling factors of the size (number of retweets) and survival (duration of retweets) of information flows in addition to the production of online antagonistic content. Despite observing high temporal variability, we found that only a small proportion (0.7%) of the content was antagonistic. We also found that antagonistic content was less likely to disseminate in size or survive for a longer period. Information flows from antisemitic agents on Twitter gained less traction, while information flows emanating from capable and willing counter-speech actors—that is, Jewish organizations—had a significantly higher size and survival rates. This study is the first to demonstrate that Sampson’s classic sociological concept of collective efficacy can be observed on social media (SM). Our findings suggest that when organizations aiming to counter harmful narratives become active on SM platforms, their messages propagate further and achieve greater longevity than antagonistic messages. On SM, counter-speech posted by credible, capable and willing actors can be an effective measure to prevent harmful narratives. Based on our findings, we underline the value of the work by community organizations in reducing the propagation of cyberhate and increasing trust in SM platforms.