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VOX-Pol Blog Post.

Right-wing extremist groups harness popular social media platforms to accrue and mobilize followers. In recent years, researchers have examined the various themes and narratives espoused by extremist groups in the United States and Europe, and how these themes and narratives are employed to mobilize their followings on social media. Little, however, is comparatively known about how such efforts unfold within and between right-wing extremist groups in Australia and Canada. In this study, we conducted a cross-national comparative analysis of over eight years of online content found on 59 Australian and Canadian right-wing group pages on Facebook. Here we assessed the level of active and passive user engagement with posts and identified certain themes and narratives that generated the most user engagement. Overall, a number of ideological and behavioral commonalities and differences emerged in regard to patterns of active and passive user engagement, and the character of three prevailing themes: methods of violence, and references to national and racial identities. The results highlight the influence of both the national and transnational context in negotiating which themes and narratives resonate with Australian and Canadian right-wing online communities, and the multi-dimensional nature of right-wing user engagement and social mobilization on social media.

In recent years, extremists have increasingly turned to online spaces to distribute propaganda and as a recruitment tool. While there is a clear need for governments and social media companies to respond to these efforts, such responses also bring with them a set of ethical challenges. This paper provides an ethical analysis of key policy responses to online extremist propaganda. It identifies the ethical challenges faced by policy responses and details the ethical foundations on which such policies can potentially be justified in a modern liberal democracy. We also offer an ethical framework in which policy responses to online extremism in liberal democracies can be grounded, setting clear parameters upon which future policies can be built in a fast-changing online environment.

Departing from Jessie Daniels’s 2013 review of scholarship on race and racism online, this article maps and discusses recent developments in the study of racism and hate speech in the subfield of social media research. Systematically examining 104 articles, we address three research questions: Which geographical contexts, platforms, and methods do researchers engage with in studies of racism and hate speech on social media? To what extent does scholarship draw on critical race perspectives to interrogate how systemic racism is (re)produced on social media? What are the primary methodological and ethical challenges of the field? The article finds a lack of geographical and platform diversity, an absence of researchers’ reflexive dialogue with their object of study, and little engagement with critical race perspectives to unpack racism on social media. There is a need for more thorough interrogations of how user practices and platform politics co-shape contemporary racisms.

Extremism has been a problematic term to define and suggests different and opposing meanings. This study explores how Twitter users conceptualize extremism in Arabic and express their opinions/arguments to construct the term. A corpus of tweets was collected from Twitter API using the word ‘تطرف أو متطرف’ in Arabic for extremist/extremism. A topic modeling algorithm was then applied to the dataset to uncover latent associated concepts underlying extremism, followed by a critical discourse analysis using Van Dijk’s Sociocognitive approach. The discursive and linguistic strategies used by Twitter users to support and justify their views of extremism were examined. The findings demonstrate an ideological influence that controls the concept of extremism, keeping it open to manipulation to serve shared interests and goals. Arab users of Twitter use extremism to promote their groups’ positive schema against others-negative schema.

The dramatic growth of the Web has motivated researchers to extract knowledge from enormous repositories and to exploit the knowledge in myriad applications. In this study, we focus on natural language processing (NLP) and, more concretely, the emerging field of affective computing to explore the automation of understanding human emotions from texts. This paper continues previous efforts to utilize and adapt affective techniques into different areas to gain new insights. This paper proposes two novel feature extraction methods that use the previous sentic computing resources AffectiveSpace and SenticNet. These methods are efficient approaches for extracting affect-aware representations from text. In addition, this paper presents a machine learning framework using an ensemble of different features to improve the overall classification performance. Following the description of this approach, we also study the effects of known feature extraction methods such as TF-IDF and SIMilarity-based sentiment projectiON (SIMON). We perform a thorough evaluation of the proposed features across five different datasets that cover radicalization and hate speech detection tasks. To compare the different approaches fairly, we conducted a statistical test that ranks the studied methods. The obtained results indicate that combining affect-aware features with the studied textual representations effectively improves performance. We also propose a criterion considering both classification performance and computational complexity to select among the different methods.