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In recent years, the United States (US) has witnessed a rise in political violence. Prior research has found that an individual’s social network is associated with their likelihood of engaging in various forms of violence, but research on social networks and political violence in the US context is limited. This study examined associations between social network size and endorsement of political violence in a recent nationally representative survey and explored how the relationship varied by use of social media as a major news source, perceptions of the government as an enemy, and membership in a marginalized or privileged racial or ethnic group.

This chapter explores the manner in which extremist groups could use artificial intelligence (AI) in the future to promote their narratives and recruit people to their causes. It looks at four broad use cases, namely generative AI, chat bots, gaming, and predictive analytics, and analyzes how extremists could adapt their propaganda and outreach efforts to incorporate new and innovative AI-powered techniques which would revolutionize their efficacy. The chapter then analyzes the current regulatory environment with regard to online content to assess the extent to which we are prepared for the AI revolution as it pertains to political messaging and the dissemination of information online. The chapter also touches on what all of this means for our civil liberties and what they might look like in a digital era that is also experiencing decentralization and a decline in Big Tech’s monopoly. It concludes with some thoughts on what we can expect to see in the coming years and suggestions for further research.

Social media messages often provide insights into offline behaviors. Although hate speech proliferates rapidly across social media platforms, it is rarely recognized as a cybercrime, even when it may be linked to offline hate crimes that typically involve physical violence. This paper aims to anticipate violent acts by analyzing online hate speech (hatred, toxicity, and sentiment) and comparing it to offline hate crime. The dataset for this preregistered study included social media posts from X (previously called Twitter) and Facebook and internal police records of hate crimes reported in Spain between 2016 and 2018. After conducting preliminary data analysis to check the moderate temporal correlation, we used time series analysis to develop computational models (VAR, GLMNet, and XGBTree) to predict four time periods of these rare events on a daily and weekly basis. Forty-eight models were run to forecast two types of offline hate crimes, those against migrants and those against the LGBT community. The best model for migrant crime achieved an R² of 64%, while that for LGBT crime reached 53%. According to the best ML models, the weekly aggregations outperformed the daily aggregations, the national models outperformed those geolocated in Madrid, and those about migration were more effective than those about LGBT people. Moreover, toxic language outperformed hatred and sentiment analysis, Facebook posts were better predictors than tweets, and in most cases, speech temporally preceded crime. Although we do not make any claims about causation, we conclude that online inflammatory language could be a leading indicator for detecting potential hate crimes acts and that these models can have practical applications for preventing these crimes.

Why do people engage in violent extremism? Why do people deem killing others as a reasonable way – the only reasonable way – to get their political voice heard? How do driving a cargo truck into crowds on a promenade or shooting at random believers in a mosque come to be seen as legitimate alternatives? The short answer is that we have no answer. However, over the last 20 years, radicalization has been the cornerstone concept used by academics, media and policymakers to describe how ordinary individuals become violent extremists. Radicalization outlines the process of embracing an extremist ideology and justifying or perpetrating violent actions in the name of such ideology. Originally, the purpose of this concept was to investigate the roots behind the series of jihadist terrorist attacks that shook the heart of Western democracies at the beginning of the 21st century. For instance, the train bombings of Madrid (2004) and London (2005) with a combined death toll of 249 civilians were not carried out by Islamists educated under the banner of Al-Qaeda sponsored madrassas but by second-generation immigrants born and raised within the liberal social fabric of the European Union. As a result, these attacks were labelled cases of homegrown terrorism and their causes were linked to the perpetrators “acting out” a violent Islamist ideology. Explanations harkening back to situational factors like group structure and settings were difficult to digest at that time, probably because it would have amounted to admitting that Western societies could prepare the structural soil conducive to jihadist terrorism. More comforting was framing the perpetrators as vulnerable individuals who fell prey to the luring speeches of unscrupulous recruiters (e.g., preachers, combatants) and led the former to reach the “tipping point” and turn the words into actions (Crone 2016).

In this chapter, we explore the communication modes and tactics used by extremists across the ideological spectrum to spread their narratives on social media. We argue that a growing body of evidence suggests that extremist movements across the political spectrum make use of similar communication modes and tactics on social media to reach their objectives. Drawing on specific case studies and examples from extreme-right, conspiracy myth and Islamist extremist campaigns, this chapter highlights how groups and networks adapt to changing circumstances and external pressure by making different strategic choices about whether to use overt or covert, centralized or crowdsourced, influencer-based or network-based and aggressive or optics-focused communication modes. On a tactical level, our case studies provide insights into approaches chosen by extremist movements to radicalize sympathizers, influence mainstream audiences and intimidate political opponents. Throughout these case studies, it becomes clear that extremists are engaged in collective learning in relation to their communication modes and the use of new technologies, with approaches and instructional materials being shared and inspiring extremists across borders.