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From Anti-Muslim to Anti-Jewish: Target Substitution on Fringe Social Media Platforms and the Persistence of Online and Offline Hate

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Abstract

The 2016 presidential campaign saw high levels of anti-Muslim online and offline hate. But, by the August 2017 ‘Unite the Right’ rally, anti-Muslim discourse and hate crimes had partly receded, despite the group remaining politically salient and despite a sharp increase in White ‘nationalist’ activity targeting another religious minority, Jews. Was this by chance? Because we might expect White nationalist activity to increase hate against all groups, the counter-intuitive decline in anti-Muslim hate could have been coincidental. We argue instead that those shifts in animus toward Muslims and Jews should be considered in tandem, and that these over-time patterns of hate reflected different manifestations of elevated and constant religious ethnocentrism, especially among far-right extremists. Using data on fringe and mainstream social media sites and hate crime databases, we present two core sets of findings. First, increased anti-Jewish speech was partly driven by the same far-right communities and extremists who previously promoted anti-Muslim speech. Moreover, combined anti-Muslim and anti-Jewish rhetoric in fringe far-right social media over this period was sustained at a high and largely constant level, seeing shifts primarily in the targets of hate speech. Second, similar patterns manifest offline: hate crimes were more strongly associated with which group was targeted by hate speech, but not the overall prevalence of hate speech. Together, this study demonstrates a robust link between the dislike toward Muslims and dislike toward Jews, and how fringe groups organize the dissemination of hate.

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Notes

  1. Gab, for example, gained over 500,000 users in the days after the riot (Stimson, 2021). Simultaneously, offline, far-right rallies became increasingly commonplace, influenced, in part, by Trump’s 2016 presidential campaign rhetoric (Newman et al., 2021).

  2. https://www.thearda.com/data-archive?fid=BAM14.

  3. For example, in January 2017, an arsonist set fire to the Victoria Islamic Center, and in September 2017 the Gates of Heaven synagogue in Wisconsin was spray-painted with swastikas and a pro-Trump message. And, in February 2017, vandals in Philadelphia toppled and desecrated at least 275 headstones at the historic Jewish Mount Carmel Cemetery, while in August 2017 the Al Maghfirah Cemetery in Minnesota was vandalized with graffiti and swastikas.

  4. https://www.splcenter.org/news/2018/10/24/splc-announces-policy-recommendations-social-media-internet-companies-fight-hate-online.

  5. https://www.vox.com/2017/8/12/16138246/charlottesville-nazi-rally-right-uva.

  6. https://www.washingtonpost.com/graphics/2020/national/confederate-monuments/.

  7. https://www.washingtonpost.com/graphics/2017/local/charlottesville-timeline/.

  8. Replication data and code for this article have been posted to https://osf.io/j736h/.

  9. Figure B.3 in the SI details the full instructions given to workers.

  10. 90% of the posts about Jews, Muslims, and/or Arabs were considered unambiguously negative by at least one of the two coders, and 62% of the posts by both coders.

  11. We use sources beyond the FBI data because the FBI data depend on voluntary police reporting, yielding both under- and over-reporting concerns (Freilich & Chermak, 2013) In SI Figure B.11 and Table B.11, we compare these sources, and demonstrate abrupt drops in reporting to the FBI compared to both advocacy sources after the 2016 presidential election.

  12. We obtained the CAIR dataset directly from the organization and use publicly available ADL data. See https://www.adl.org/education-and-resources/resource-knowledge-base/adl-heat-map. We downloaded the FBI UCR data from their public website. See https://crime-data-explorer.fr.cloud.gov/downloads-and-docs.

  13. See SI Tables A.5 and A.6 for lists of bias incidents and hate crimes recorded by both organizations.

  14. We document sharp discrepancies between advocacy organization and government data after the 2016 election in SI Section “Comparison of ADL, CAIR, UCR Data.”

  15. We present analyses without ratios/logging because the findings do not change when only using subtraction and addition—and prior readers have struggled to interpret the ratios and/or logged estimates.

  16. Corresponding with a shift in rhetoric by Donald Trump on Twitter, see Figure B.10 in SI Section B.8, around a month after an escalation of coalition airstrikes on ISIS in Syria.

  17. See Table A.3 in the SI.

  18. Of the websites, 4chan /pol/ is the most extreme (use of a Black slur is more frequent than the word ‘Black’, for example), slurs appear in less than 0.1% of posts on the mainstream site Reddit (that have not been deleted by moderators or moderation bots prior to archiving), and Gab, a website advertised for “free speech” lies between the two. In these analyses, the main text figures show the ratio of ‘Muslims or Arabs’ to ‘Jews’ mentions using both keywords and supervised predictions from crowd-sourced hand labels. ‘Muslim’ and ‘Jew’ here are coded using keywords only (‘muslim’, ‘jew’, ‘islam’, ‘judaism’, ‘ arab ’) See SI Sections B.2 and B.3 for labeling and model training details.

  19. See SI Figure B.4 for analyses of all ‘subreddits’ which demonstrate the same finding.

  20. See B.6 for a full summary table of logged coefficients.

  21. Hate speech labels shown in this figure were assigned using supervised models trained on data from the Gab Hate Corpus (Kennedy et al., 2018) and group mentions similarly use predicted probabilities from supervised models trained on hand labels. See SI Sections B.4, B.2, and B.3 for details.

  22. Note that we do not only use a fixed effects model here because we need to evaluate associations between two within-user shifts of activity, and which will be measured with significant error.

  23. Not all studies posit that online hate speech will increase hate crimes (Chan et al., 2013; Glaser et al., 2002).

  24. Note that this SI table displays the untransformed logged ratio coefficients from the quasi-Poisson models, rather than the exponentiated coefficients—ratios—reported here.

  25. These models use the full FBI data because included lags prevent the abrupt shifts in hate crime reporting in late 2016 and early 2017, as documented in SI Section B.10.3 and which suggests potential long-term under-reporting of simple assaults and vandalism after the 2016 election and 2017 inauguration, from meaningfully influencing the models.

  26. As noted in the social media analysis section, we do not log variables in visualizations only to increase their accessibility to readers. The log ratio of mentions is a transformation of these count variables.

  27. https://www.start.umd.edu/gtd/.

  28. Note the p-values in these models do compare the difference in mentions with the combined mentions.

  29. https://www.adl.org/resources/reports/white-supremacists-step-up-off-campus-propaganda-efforts-in-2018.

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Correspondence to William Hobbs or Nazita Lajevardi.

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Replication materials for this article have been posted to: https://osf.io/j736h/.

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We are grateful to the editors and the anonymous reviewers for their invaluable feedback. We also thank Marisa Abrajano, Per Adman, Abbas Barzegar, Taylor Carlson, Vicky Fouka, James Fowler, John Kuk, Zachary Steinert-Threlkeld, and Jakana Thomas for their helpful comments and suggestions as we developed the manuscript. Finally, we thank Sophia Lada and Marissa Rivera for their research assistance. All remaining errors are our own.

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Hobbs, W., Lajevardi, N., Li, X. et al. From Anti-Muslim to Anti-Jewish: Target Substitution on Fringe Social Media Platforms and the Persistence of Online and Offline Hate. Polit Behav (2023). https://doi.org/10.1007/s11109-023-09892-9

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