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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.

In the context of far-right terrorism, memes on 4chan play a fundamental role. These memes not only serve as a form of propaganda but also as a tool to radicalize and mobilize users. On 4chan, a site known for its lack of moderation and anonymity, memes are used to spread extremist ideologies in a viral and accessible manner. Memes can destabilize and trivialize serious issues, normalizing hate and violence among users. Furthermore, their humorous and accessible format facilitates the spread of radical ideas, making them more appealing and less confrontational. By becoming an integral part of 4chan’s online culture, these memes not only reinforce extremist ideology but also foster a community where violence and hate become acceptable and common topics. This dynamic highlights the importance of addressing the role of memes in the spread of extremism and the need for strategies to counteract their impact.

This book explores the social forces among and between online aggressors that affect the expression and perpetration of online hate. Its chapters illustrate how patterns of interactive social behavior reinforce, magnify, or modify this expression. It also considers the characteristics of social media that facilitate social interactions that promote hate and facilitate relationships among haters. Bringing together a range of international experts and covering an array of themes, including woman abuse, antisemitism, pornography, radicalization, and extreme political youth movements, this book examines the specific social factors and processes that facilitate these forms of hate and proposes new approaches for explaining them. Cutting-edge, interdisciplinary, and authoritative, this book will be of interest to sociologists, criminologists, and scholars of media, communication, and computational social science alike, as well as those engaged with hate crime, hate speech, social media, and online social networks.

While the extreme right wing (ERW) has not gained a foothold in local or national Irish politics, the country has witnessed a growth in online activism and harassment, and physical protest and violence. This paper explores a case study based on 4876 unique posts from one Irish-based Telegram group active during six months of the COVID-19 pandemic. The main findings are that: (a) this group was heavily influenced by foreign content and influencers, in particular, American ERW content and conspiracies associated with QAnon; (b) conspiracy theories were not adopted wholesale but adapted for an Irish audience; (c) Irish actors were not passive receivers of US content, and multi-directional exchanges of ideas were witnessed. While generalising beyond the case study analysed in this paper would require further data, this may suggest that Irish influencers are part of a transnational online ecosystem in which ideas and theories are shared and adapted to local contexts. These findings offer insights into the ERW in Ireland and, more generally, how the ERW communicates and spread conspiracy theories across national borders.