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Mobilization is central to the emergence, survival and success of armed groups challenging the state, and has lately expanded to new arenas with the rise of social media. Using a new dataset of rebel group Twitter use, we examined the topics contained in rebel group social media communications to understand how different messaging strategies impact civilian engagement with rebel messages. Rather than benefiting solely from direct calls to action, we found that rebel groups also increased civilian engagement through indirect messages of self-promotion. While direct appeals received more engagement than indirect appeals, their effects were tempered by audience fatigue when relied on too heavily. We additionally found that including images further enhanced the impact of a mobilizing message. These findings expanded our understanding of rebel communications and mobilization, with important implications for combating the use of social media as a recruitment tool for violent extremism.

Scholarship has highlighted the rise of political influencer networks on YouTube, raising concerns about the platform’s propensity to spread and even incentivize politically extreme content. While many studies have focused on YouTube’s algorithmic infrastructure, limited research exists on the actual content in these networks. Building on Lewis’s (2018) classification of an “alternative influencer” network, we apply structural topic modeling across all text-based autocaptions from her study’s sample to identify common topics featured on these channels. This allows us to gauge which topics appear together and to trace politicization over time. Through network analysis, we determine channel similarities and evaluate whether deplatformed channels influenced topic shifts. We find that political topics increasingly dominate the focus of all analyzed channels. The convergence of culture and politics occurs mostly about identity-driven issues. Furthermore, more extreme channels do not form distinct clusters but blend into the larger content-based network. Our findings illustrate how political topics may function as connective ties across an initially more diverse network of YouTube influencer channels.

Approaches for strategically countering or providing alternatives to hate speech and extremism online have evolved substantively in the last ten years. Technological advancement and a generation of young activists who have been socialized as digital natives have facilitated a maelstrom of both hate-based extremist content and attempts to counter this material in different guises and through diverse channels. The rate and pace of change within the tech sector, and social media growth in particular, have meant that although counterspeech is now more prevalent than ever before, it requires greater guidance and more robust public–private partnerships to effectively prevent and counter extremism online. The chapter embraces a cross-platform and international overview of some of the best practices within efforts to prevent and counter violent extremism online and discusses the future of counterspeech with recommendations for expanded innovation and partnership models.

On October 27th, 2022, Elon Musk purchased Twitter, becoming its new CEO and firing many top executives in the process. Musk listed fewer restrictions on content moderation and removal of spam bots among his goals for the platform. Given findings of prior research on moderation and hate speech in online communities, the promise of less strict content moderation poses the concern that hate will rise on Twitter. We examine the levels of hate speech and prevalence of bots before and after Musk’s acquisition of the platform. We find that hate speech rose dramatically upon Musk purchasing Twitter and the prevalence of most types of bots increased, while the prevalence of astroturf bots decreased.

As efforts to identify and remove online terrorist and violent extremist content have intensified, concern has also grown about so‐called lawful but awful content. Various options have been touted for reducing the visibility of this borderline content, including removing it from search and recommendation algorithms, downranking it and redirecting those who search for it. This article contributes to this discussion by considering the moderation of such content, in terms of three sets of values. First, definitional clarity. This is necessary to provide users with fair warning of what content is liable to moderation and to place limits on the discretion of content moderators. Yet, at present, definitions of borderline content are vague and imprecise. Second, necessity and proportionality. While downranking and removal from search and recommender algorithms should be distinguished from deplatforming, tech companies’ efforts to deamplify borderline content give rise to many of the same concerns as content removal and account shutdowns. Third, transparency. While a number of platforms now publish their content moderation policies and transparency data reports, these largely focus on violative, not borderline content. Moreover, there remain questions around access to data for independent researchers and transparency at the level of the individual user.