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This working paper explores trends in extremist Facebook data from July 2023 to June 2024. We examined engagement, sentiment, and topics within Facebook groups categorized as anti-Israel/Semitic, anti-Palestine/Muslim, and anti-both, mapping these trends against five major events related to the recent Israel-Hamas conflict. Our findings support the hypothesis that shifts in trends correspond with these key events, showing varying patterns across different group categories. We observed decreased activity proportion in anti-both groups and increased activity proportion in the two one-sided hate groups at the conflict’s onset. This pattern reversed after the Israeli troop withdrawal from Khan Yunis, Gaza. During the conflict, negative content proportion surged, and neutral content proportion fell in all the three group categories. Anti-Palestine/Muslim groups’ discourses shifted from religious to social media activism and political/protest around the time the war began, while anti-Israel/Semitic groups moved from political/protest to religious topics a couple of weeks before the war.

VOX-Pol Blog post.

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.