How transnational are the audiences of far right parties and movements on Twitter? While an increasing number of contributions addresses the topic of transnationalism in far right politics, few systematic investigations exist on the actors and discourses favored in transnational exchanges on social media. Building on the literature on the far right, social movements, transnationalism and the Internet, the paper addresses this gap by studying the initiators and the issues that are favored in online exchanges between audiences of far right organizations, e.g. political parties and movements across France, Germany, Italy and the United Kingdom. We use a new dataset on the activities of far right Twitter users that is analyzed through a mixed methods approach. Using social network analysis, we detect transnational links between far right organizations across countries based on retweets from audiences of far right Twitter users. Retweets are qualitatively coded for content and compared to the content retweeted within national communities. Finally, using a logistic regression, we quantify the level to which specific issues and organizations enjoy high levels of attention across borders. Subsequently, we use discourse analysis to qualitatively reconstruct the interpretative frames accompanying these patterns. We find that although social media are often ascribed much power in favoring transnational exchanges between far right organizations, there is little evidence of this. Only a few issues (anti-immigration and nativist interpretations of the economy) garner transnational far right audiences on Twitter. In addition, we find that more than movements, political parties play a prominent role in the construction of a transnational far right discourse.
Social media are now a routine part of political campaigns all over the world. However, studies of the impact of campaigning on social platform have thus far been limited to cross-section datasets from one election period which are vulnerable to unobserved variable bias. Hence empirical evidence on the effectiveness of political social media activity is thin. We address this deficit by analysing a novel panel dataset of political Twitter activity in the 2015 and 2017 elections in the United Kingdom. We find that Twitter based campaigning does seem to help win votes, a finding which is consistent across a variety of different model specifications including a first difference regression. The impact of Twitter use is small in absolute terms, though comparable with that of campaign spending. Our data also support the idea that effects are mediated through other communication channels, hence challenging the relevance of engaging in an interactive fashion.
Extremist exploitation of social media platforms is an important regulatory question for civil society, government, and the private sector. Extremists exploit social media for a range of reasons—from spreading hateful narratives and propaganda to financing, recruitment, and sharing operational information. Policy responses to this question fit under two headings, strategic communication and content moderation. At the center of both of these policy responses is a calculation about how best to limit audience exposure to extremist narratives and maintain the marginality of extremist views, while being conscious of rights to free expression and the appropriateness of restrictions on speech. This special issue on “Countering Extremists on Social Media: Challenges for Strategic Communication and Content Moderation” focuses on one form of strategic communication, countering violent extremism. In this editorial we discuss the background and effectiveness of this approach, and introduce five articles which develop multiple strands of research into responses and solutions to extremist exploitation of social media. We conclude by suggesting an agenda for future research on how multistakeholder initiatives to challenge extremist exploitation of social media are conceived, designed, and implemented, and the challenges these initiatives need to surmount.
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