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Fanning the Flames of Hate: Social Media and Hate Crime∗

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Fanning the Flames of Hate: Social Media and Hate Crime∗ Fanning the Flames of Hate: Social Media and Hate Crime∗ Karsten M¨uller† and Carlo Schwarz‡ February 19, 2018 Abstract This paper investigates the link between social media and hate crime using hand- collected data from Facebook. We study the case of Germany, where the recently emerged right-wing party Alternative f¨urDeutschland (AfD) has developed a major social media presence. Using a Bartik-type empirical strategy, we show that right-wing anti-refugee sentiment on Facebook predicts violent crimes against refugees in otherwise similar municipalities with higher social media usage. To establish causality, we further exploit exogenous variation in major internet and Facebook outages, which fully undo the correlation between social media and hate crime. We further find that the effect decreases with distracting news events; increases with user network interactions; and does not hold for posts unrelated to refugees. Our results suggest that social media can act as a propagation mechanism between online hate speech and real-life incidents. JEL classification: D74, J15, Z10, D72, O35, N32, N34. Keywords: social media, hate crime, minorities, Germany, AfD ∗We are grateful to Sascha Becker, Leonardo Bursztyn, Mirko Draca, Evan Fradkin, Thiemo Fetzer, Fabian Waldinger, and seminar participants at the University of Chicago and the Leverhulme Causality Conference at the University of Warwick for their helpful suggestions. We would also like to thank the Amadeu Antonio Stiftung for sharing their data on refugee attacks with us. M¨ullerwas supported by a Doctoral Training Centre scholarship granted by the Economic and Social Research Council [grant number 1500313]. Schwarz was supported by a Doctoral Scholarships from the Leverhulme Trust as part of the Bridges program. †Warwick Business School, University of Warwick, [email protected] ‡Department of Economics, University of Warwick, Centre for Competitive Advantage in the Global Economy (CAGE), [email protected] 1 1 Introduction Social media has come under increasing scrutiny during the most recent Presidential election in the United States and all over the globe. Relatively recent phenomena such as fake news, social media echo chambers or bot farms have been subject of widespread media coverage and public discourse (e.g New York Times, 2016, 2017a). The role of hate speech, especially online, has been at the center of a particularly intense and polarized debate. Despite the public interest and calls for policy action, there is little empirical evidence on the association between social media and real-life outcomes. In this paper, we study the link between social media and hate crime drawing on hand-collected data from Facebook, the largest social media network. In particular, we investigate the relationship between anti-refugee sentiment on Facebook and hate crimes against refugees in Germany. The German setting is motivated by the relatively large recent influx of refugees into the country and the unfortunate frequency of violent crimes committed against them (see, for example, recent video coverage by New York Times, 2017b). We create a measure for the salience of anti-refugee hate speech on social media based on the Facebook page of the \Alternative f¨urDeutschland" (Alternative for Germany, AfD hereafter), a relatively new right-wing party that became the third-strongest faction in the German parliament following the 2017 federal election. The AfD has positioned itself as an anti-refugee and anti-immigration party; with more than 300,000 likes, it also has more followers than any other German party on Facebook (see Appendix A for a history of the AfD). This widespread reach makes the AfD's Facebook page uniquely suited for an analysis of the relationship between social media and hate crime in Germany. In contrast to established political parties like Angela Merkel's Christian Democratic Union (CDU) or the German Social Democrats (SPD), the AfD allows users to directly post messages on its Facebook wall; it is also the only party that does not explicitly outlines rules of conduct, e.g. by threatening to remove racist, discriminating, or otherwise hateful comments. As a result, the AfD Facebook page contains far more post and comments than those of other parties. With over 176,000 posts, more than 290,000 comments, and 500,000 likes by over 93,000 individual users, our data provide a unique insight into far-right Facebook usage and hate speech. This rich dataset enables us to understand the scale of the consumption of hate media online and analyze their real-world impact. Building on a large body of research on “confirmation bias" { the human tendency to 2 favor information consistent with pre-existing views { we posit that such Germany-wide bursts of anti-refugee sentiment may be particularly incendiary for users who opt into receiving right-wing content on social media. The widespread reach of Facebook, and the AfD page in particular, implies that a non-negligible fraction of social media users in Germany is exposed to such content. This, in turn, may contribute to pushing some potential perpetrators over the edge to commit violent acts when tensions are high. We exploit differences in Facebook usage at the municipality level to study the effect of anti-refugee sentiment in social media on hate crime in a Bartik-type difference-in-differences framework. We attempt to isolate the effect of social media propagation by crossing our measure of anti-refugee salience with hand-collected information on the location of Facebook users. Essentially, we compare how social media posts { which we show are highly volatile { affect crimes within the same municipality compared to other locations in the same week. This lets us abstract from average differences in anti-refugee sentiment across regions and time. In our setting, the share of a municipality's population that use the AfD Facebook page is an intuitive proxy for right-wing social media use; however, it is also correlated with differences in a host of observable municipality characteristics { most importantly general internet usage and the prevalence of right-wing ideology. This opens the door for alternative explanations: because culture is highly persistent, periods of high salience of refugees may be associated with more attacks through a local culture of hatred for foreigners (Voigtlander and Voth, 2012). We address these concerns twofold. First, we control for the interaction of observable differences with our weekly social media post measure in the estimations. As it turns out, using our regression framework to \match" municipalities on these characteristics (Angrist and Pischke, 2008) makes little difference for our point estimates. Taken at face value, this suggests a separate role for social media in the transmission of Germany-wide anti-refugee sentiment. Second, we isolate the local component of social media usage that is uncorrelated with observables by using the share of the population that is active on the \Nutella Germany" page. With over 32 million likes, Nutella has one of the most popular Facebook pages in Germany and therefore provides a reasonable measure of social media use at the municipality level. Intuitively, Nutella likes are highly correlated with AfD usage. These data enable us to create a dummy for towns with many Nutella users within a county, where we essentially compare the effect of social media refugee salience on neighboring municipalities. Crucially, this measure of social media activity is balanced across a plethora of observable characteristics, 3 most importantly general internet usage, voting patterns, and proxies of xenophobic attitudes. As such, our empirical strategy is related to Enikolopov et al. (2016), who use an instrument to generate variation in social media usage unrelated to observables.1 We find that anti-refugee hate crimes are more likely to occur in areas with higher exposure to anti-refugee hate speech online. The effect is especially pronounced for violent incidents against refugees, such as arson and assault. We do not find significant predictive power for other posts, e.g. those related to Islam or Muslims more generally, which suggests that we are capturing variation in refugee-specific sentiment. Consistent with the hypothesis that hate speech is transmitted through social media in particular, the size of the effect on hate crime is higher in regions where AfD users show higher average engagement on Facebook via likes and comments. Importantly, these engagement proxies are essentially orthogonal to per capita social media use and thus provide meaningful additional variation. It also appears that we are not solely capturing refugee salience generated by the news cycle. While our measure of social media sentiment is necessarily correlated with media attention about refugees, we find that coverage by major German news outlets does not have an independent effect on hate crimes. As a placebo test, we also consider whether posts about refugees on the Facebook pages of all other major parties predict refugee attacks. However, we find that after controlling for overall media attention, only posts on the AfD page have consistent predictive power. These findings suggest that right-wing sentiment on social media has an independent effect on hate crimes. To narrow down the social media transmission channel, we provide quasi-experimental evidence using local internet disruptions. We draw on user-reported data to identify munici- palities and weeks with large-scale internet outages, which we verify using news articles in regional and national media outlets. Intuitively, we find that such local outages decrease the social media activity of AfD users in the affected municipalities in a sub-sample of geo-matched Facebook posts, comments, and likes. Importantly, internet disruptions are geographically dispersed and uncorrelated with AfD likes on Facebook; that is, they are unrelated to right-wing ideology. Our social media use proxies, in turn, are orthogonal to measures of general internet affinity. As such, they capture differences in Facebook usage, but not internet usage.
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