Right-Wing Social Media and Political Unrest Daniel Karell Andrew M. Linke Edward Holland Department of Sociology Geography Department Department of Geosciences Yale University The University of Utah University of Arkansas and Peace Research Institute Oslo (PRIO) VERSION 3 First version: May 3, 2021 This version: July 30, 2021 _____________________ Daniel Karell and Andrew Linke contributed eQually to the study design and analysis. Edward Holland, Daniel Karell, and Andrew Linke contributed eQually to the writing. We thank Luis Leon Medina for research assistance and Carl Dahlman, Jeffrey Jensen, Andrew Linder, and Abdul Noury for valuable comments. Corresponding author: Daniel Karell ([email protected]). ABSTRACT Does right-wing social media use increase right-wing demonstrations and violence? We create a spatial panel dataset measuring 57,505 right-wing social media posts and 1,765 incidents of right- wing unrest in all United States counties from January 2020 through January 2021. Using spatial regression analysis with county and month fixed effects, we find that county-level right-wing social media activity in a given month increased the freQuency of subsequent right-wing unrest events. Additional analyses using matching, entropy balancing, and fixed-effects instrumental variable methods that account for observable and unobservable confounders have consistent findings, and offer steps towards causal interpretation. A semi-supervised computational analysis of five million right-wing social media comments from January 2020 through January 2021 provides insight into the mechanisms connecting right-wing social media activity to right-wing contentious events. Our study advances research on social media activity and offline political protest by illuminating countrywide patterns of localized instances of unrest. In addition, it helps develop an emerging research agenda on social media and overt violence. KEY WORDS Social media; Right-wing mobilization; Protest; Violence; Parler; Spatial analysis 2 INTRODUCTION Does right-wing social media use increase right-wing demonstrations and violence? In the wake of events like the 2019 Christchurch mosQue shootings and the 2021 United States (U.S.) Capitol riot, we are in the early stages of understanding how—or even if—right-wing online activity leads to offline contentious political activity, such as protests and demonstrations, which sometimes turn violent. On one hand, social media influence political attitudes and behavior (Bond, et al. 2012). For example, some social media users express more polarized views after being exposed to tweets from politicians with opposing ideologies (Bail, et al. 2018), reduce their sectarian language if they are sanctioned online (Siegel and Badaan 2020), and regard ethnic others more negatively after deactivating their Facebook accounts (Asimovic, et al. 2021). In addition, there is evidence that right-wing social media (RWSM) can lead to physical hate crimes. Anti-refugee sentiment on Facebook predicts attacks on refugees in Germany (Müller and Schwarz 2020a) and hate speech on Twitter correlates with racially and religiously aggravated crimes in London (Williams et al. 2020). In the U.S., counties with greater Twitter use than others have more hate crimes, and Donald Trump’s anti-Muslim Tweets predicted subseQuent hate crimes (Müller and Schwarz 2020b). On the other hand, we still have an incomplete picture of how social media activity results in offline political unrest. While there is extensive research linking online engagement to online collective action (e.g., Barberá, et al. 2015, González-Bailón and Wang 2016; see also Tufekci and Freelon 2013), the evidence tying it to offline political unrest is more limited. Some studies of social media and civic and political participation, conceived more broadly, find that social media use has little effect on users’ opinions and behavior (Theocharis and Lowe 2016; Foos, et al. 2020; see also Boulianne 2015 and Guess, et al. 2020). Research specifically on social media and offline protest have focused on authoritarian contexts (e.g., Steinert-Threlkeld 2017; Tufecki 2017; Weidmann and Rød 2019; Enikolopov, et al. 2020) or large but rare protests, such as the 2015 Charlie Hebdo protests (Larson, et al. 2019) and the 2021 U.S. Capitol riot (Van Dijcke and Wright 2021) (see also Caren, et al. 2020; Hsiao 2021). Furthermore, with respect to the political right in liberal democracies, it does not appear at first glance that using RWSM produces widespread unrest. Millions of people do the former, while the latter is comparatively rare (Marwick and Clancy 2020). Previously, analyzing the relationship between social media activity and offline political unrest countrywide over many months has been difficult, if not impossible, due to data limitations. Social media data rarely include location information disaggregated below the state level. If detailed 3 location information is available, the data are sampled from the complete collection of observations using undisclosed methodologies (McCormick, et al. 2017; Kim, et al. 2020) and only describe individuals who voluntarily share their location, which can bias inferences (Malik, et al. 2015; Beauchamp 2017; Flores 2017; Steinert-Threlkeld 2017; Mitts 2019). These limitations have helped focus the literature on high-profile, singular events with many thousands of participants (e.g., Steinert-Threlkeld 2017; Enikolopov, et al. 2020; Van Dijcke and Wright 2021), rather than smaller incidents of political unrest that can happen in communities countrywide. We examine whether activity on RWSM results in localized political unrest in the U.S. and address data limitations related to this question by using a previously unpublished spatiotemporal record of 57,505 posts during 2020 and early 2021 on the RWSM platform, Parler. Parler, which first came online in 2018, has been popular with the U.S. right-wing since early 2020 (Isaac and Browning 2020; Aliapoulios, et al. 2021; Nicas and Alba 2021) and was singled out by the 2021 U.S. Senate report on the U.S. Capitol riot for hosting messages inciting the attack (HSAGAC and CRA 2021). We join the observations of Parler activity with a database of thousands of contentious political events, creating a county-month spatial panel dataset of RWSM activity and political unrest in U.S. counties during 2020 and early 2021 (N = 37,704). Figure 1A shows that right-wing unrest from January 2020 to January 2021 was relatively rare in the U.S. The majority of counties, especially those in the middle portion of the country, experienced no incidents. However, a fifth of all counties had at least one incident and 90 (3%) had five or more. Moreover, right-wing events were not concentrated in counties containing state capitols. Many confrontations occurred in, for example, north-central Washington (Okanogan County), the Upper Peninsula of Michigan (Schoolcraft County), and southwestern Virginia (Smyth County). Compared to the conflict and protest events, Parler use was more common and evenly distributed across the country during 2020 and early 2021 (Figure 1B). Eighty percent of counties had at least one instance of activity (and, we presume, numerous instances of unobserved passive engagement, such as users reading others’ posts); 1228 counties (40%) had at least five instances. Similar to the spatial distribution of right-wing events, we see that, when normalized by population, RWSM activity is not concentrated exclusively in major population centers. For example, we observe relatively high rates of activity in northern California (Humboldt and Trinity counties), western Indiana (Parke County), and northeastern Pennsylvania (Pike County). 4 Figure 1. Right-wing unrest (Panel A) and Parler activity (Panel B) across the United States from January 2020 through January 2021. Weekly temporal trends of activity and events are displayed in Panel C. They are positively correlated (0.52, p < 0.001). Figure 1C presents the temporal distribution of Parler activity and right-wing unrest. Both rose slowly through the first half of 2020, after which Parler plateaued (then rose again) while unrest declined slowly. The two weekly time series are positively correlated; the bivariate Spearman’s rank correlation for non-normally distributed data is 0.52 (p < 0.001). We additionally examine the temporal correlations of Parler and unrest events across up to 10-week lags in either direction from a given week (Appendix A, Figure A1). These results indicate positive and significant correlations forwards and backwards in time within a window roughly of 10 and five weeks, respectively. Some overlap backwards in time is possible because people participating in right-wing events may have used Parler at the time, but the correlation fades after roughly one month. Drawing on the research showing that social media can influence political attitudes, promote participation in large protests, and lead to hate crimes, our core expectation is that RWSM activity will increase confrontations involving right-wing actors. We test this expectation at a county level. The impact of a person using social media could spread throughout their offline community, such as a county, since users of a social media platform are likely to share their online community’s rhetoric and ideologies with friends, colleagues, and neighbors who are non-users. Our hypothesis is: H1. In U.S. counties, Parler activity will lead to an increase in subseQuent right-wing unrest. In testing this hypothesis,