This paper investigates the link between social media and hate crime. We show that anti-refugee sentiment on Facebook predicts crimes against refugees in otherwise similar municipalities with higher social media usage. To establish causality, we exploit exogenous variation in the timing of major Facebook and internet outages. Consistent with a role for “echo chambers”, we find that right-wing social media posts contain narrower and more loaded content than news reports. Our results suggest that social media can act as a propagation mechanism for violent crimes by enabling the spread of extreme viewpoints.

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In this article, I introduce the ldagibbs command, which implements latent Dirichlet allocation in Stata. Latent Dirichlet allocation is the most popular machine-learning topic model. Topic models automatically cluster text documents into a user-chosen number of topics. Latent Dirichlet allocation represents each document as a probability distribution over topics and represents each topic as a probability distribution over words. Therefore, latent Dirichlet allocation provides a way to analyze the content of large unclassified text data and an alternative to predefined document classifications.