Online Library

Exposure to radicalizing information has been associated with support for violent extremism. It is, however, unclear whether specific information use behavior, namely, a distinct pattern of incidental exposure (IE) to and active selection (AS) of radicalizing content, indicates stronger violent extremist attitudes and radical action intentions. Drawing on a representative general population sample (N = 1509) and applying latent class analysis, we addressed this gap in the literature. Results highlighted six types of information use behavior. The largest group of participants reported a near to zero probability of both IE to and AS of radicalizing material. Two groups of participants were characterized by high or moderate probabilities of incidental exposure as well as a low probability of active selection of radicalizing content. The remaining groups displayed either low, moderate, or high probabilities of both IE and AS. Importantly, we showed between-group differences regarding violent extremist attitudes and radical behavioral intentions. Individuals reporting near zero or high probabilities for both IE to and AS of radicalizing information expressed the lowest and strongest violent extremist attitudes and willingness to use violence respectively. Groups defined by even moderate probabilities of AS endorsed violent extremism more strongly than those for which the probability for incidental exposure was moderate or high but AS of radicalizing content was unlikely.

VOX-Pol Blog post.

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.

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.