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Quantifying Hate: a Year of Anti-Semitism on Twitter

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Quantifying Hate: a Year of Anti-Semitism on Twitter Quantifying Hate: A Year of Anti-Semitism on Twitter Sections 1 Introduction 7 When Anti-Zionism is Anti-Semitic 2 Major Findings 8 QAnon 3 Methodology 9 “Globalist” as Code Word for “Jew” 4 Detailed Findings & Anti-Semitic Themes 10 Holocaust Denial 5 Harvey Weinstein and Jewish Sexual Predators 11 False Flags 6 Rothschild Conspiracy Theories 12 George Soros 13 Recommendations INTRODUCTION 1 / 60 It’s nearly impossible to overstate the impact social media has on our world. That influence is often wielded in the service of good – think viral fund-raising efforts or protecting democratic movements against authoritarian threats – but it also can powerfully amplify society’s most dangerous attitudes. Racism, sexism, homophobia, religious extremism and conspiracy theories have deep roots in social media, and perpetrators have recognized and capitalized on the near- universal reach of popular platforms. This report is an effort to gauge the prevalence of one of these destructive prejudices – anti-Semitism – on one social media platform: Twitter – by examining the one-year period from January 29, 2017 to January 28, 2018. ADL frequently fields reports from constituents who describe an atmosphere of anti-Semitism on Twitter, including harassment from anti-Semitic trolls. WWWWeeee issuedissuedissuedissued pr prprpreeeeviousviousviousvious studies studiesstudiesstudies on ononon this thisthisthis p ppphhhhenenenenomomomomenenenenonononon. Media reports reflect this phenomenon as well. Experts at the ADL’s Center on Extremism (COE) wanted to go beyond those reports, to understand the problem in greater detail while examining tweets for nuance and context. How many anti-Semitic tweets are posted on Twitter during any given week? How do we know the tweets truly are anti-Semitic? What if some of these tweets are intended to convey an ironic or critical message? Using a proprietary, wide-ranging query, as well as statistical methods and expert analysis, COE analysts were able to determine that roughly 4.2 million anti- Semitic tweets were posted and reposted on Twitter in the one-year period specified above. We estimate that the tweets were issued by approximately three million unique handles. Of course, 4.2 million tweets is a very small number out of the trillions of tweets sent on the platform each year. But that does not negate the lived experience of Jews who have found Twitter to be a toxic environment. This number is still large enough to underscore the powerful harassment that exists and the ease with 2 / 60 which a relative handful of users can infect our shared social media environment with negative stereotypes and conspiracy theories about Jews. This report, and the random statistical sample of over 55,000 carefully reviewed tweets which it generated, also will be useful to those designing artificial intelligence-based efforts to counter anti-Semitism and bigotry online. ADL already works with Twitter to help them address anti-Semitism and other forms of bigotry on their platform, and we know this is a difficult challenge to solve. We are proud to be part of Twitter’s Trust and Safety Council and to partner with Twitter as a member of our Problem Solving Lab to explore engineering- based approaches to address online hate and harassment. We already have seen the company make real progress on these issues and demonstrate leadership among social media platforms. And yet, despite recent advancements, intolerance on public platforms like Twitter still endures. It degrades the conversation and poisons the public discourse for all. Even a proportionally small number of Tweets that express anti- Semitism is too many, and any platform that hosts such hate must ensure that it is using all means necessary to tackle the situation even as it seeks to balance legitimate concerns about not inhibiting freedom of expression. For this reason, this report includes not only a detailed review of our findings but a set of recommendations for Twitter to take into consideration as it attempts to address the issue and improve its performance. We look forward to continuing to work with Twitter – and other platforms that seek our help – to ensure these environments are a safe space for all users, regardless of their faith, race or other immutable attributes. MAJOR FINDINGS As a result of a pilot research project, the Anti-Defamation League can estimate that a minimum of approximately 4.2 million English language anti-Semitic 3 / 60 tweets were disseminated between January 29, 2017 and January 28, 2018.The analysis showed that within predefined seven-day periods, the number of tweets expressing anti-Semitic sentiment ranged from a low of 36,800 in week 26 (July 23-29) to a high of 181,700 in week 45 (December 03-09). The average number of tweets expressing anti-Semitic sentiment throughout all 52 weeks of the analysis was 81,400. METHODOLOGY 1. Process The current findings are based on a complex Boolean query designed to identify language frequently used by anti-Semites. The query was broadly written to encompass obvious expressions of anti-Semitism, including classic anti-Semitic stereotypes; code words and symbols sometimes used in an anti-Semitic fashion; and also subtle references to anti-Semitic conspiracy theories. Experts at ADL’s Center on Extremism assessed statistically significant samples of the tweets captured by this query for each week. The assessments were designed to identify the percentage of tweets not intended to express anti-Semitic sentiment or disseminate anti-Semitic conspiracy theories. Of particular interest were tweets that used or cited anti-Semitic language in order to condemn it, or that used anti-Semitic language ironically. In these cases, analysis of the thread 4 / 60 context (as well as accompanying videos and images) was critical. Occasionally, the self-reported info in the tweeter’s “bio” field was also consulted to help determine intent. When ADL experts were not reasonably confident that a given tweet was anti-Semitic, it was marked as not containing anti-Semitic sentiment. A sufficient number of tweets were assessed for each time period to result in a 3% margin of error. The resulting percentage of anti-Semitic tweets in the representative sample was then extrapolated back to the entire population of tweets pulled in by the query for its time period. This yielded the number of anti-Semitic tweets reported in the findings. This project is ADL’s second attempt to assess the amount of anti-Semitism on Twitter. In an October 2016 report, ADL used keywords correlating with anti- Semitism to assess the total number of tweets which could potentially contain anti-Semitic content that were directed at journalists. The current report refines the methodology by moving beyond simple keyword detection and estimating the number of instances of actual anti-Semitic sentiment or content. The current report also expands the scope of the first study to address anti-Semitism in all English language tweets. A third project, the Online Hate Index being developed by ADL’s Center on Technology and Society, has experimented with artificial intelligence and machine learning to detect the presence of hate speech on selected Reddit forums. The current project focuses solely on anti-Semitism, including difficult- to-detect anti-Semitic conspiracy theories, and was designed and implemented by human experts without any automation or machine learning. ADL hopes that the project’s resulting corpus of tweets — which have been evaluated for anti- Semitism — will be useful in training machine learning engines to detect anti- Semitism. 2. Defining Anti-Semitism 5 / 60 The current report is based on a proprietary Boolean query designed by experts at ADL’s Center on Extremism. The query is designed to detect anti-Semitic content in the following categories: 1. Classic anti-Semitic stereotypes (e.g. references to Jews as greedy; controllers of banks, media, governments and academia; under-miners of culture and racial purity; cursed for killing Jesus; etc.) 2. Positive references to or promotion of known anti-Semitic personalities, authors, books, articles, videos and podcasts 3. References to anti-Semitic conspiracy theories (e.g. Jewish control of the Federal Reserve; the existence of a “Zionist Occupation Government,” etc.) 4. Holocaust denial 5. Epithets used for Jews (e.g. “kike”) and against Jews (e.g. “goddamn Jews”) 6. Code words and anti-Semitic symbols such as the “echo symbol” (“((( )))”) The current report includes criticism of Israel or Zionism when such criticism makes use of classic anti-Semitic language or conspiracy theories, or when it ascribes evil motivations to significant numbers of Jews. General criticism of Israel or its policies is not counted as anti-Semitism. [See below — When Anti- Zionism is Anti-Semitic.] 3. Caveats The current study is concerned with all instances of tweets intended to convey anti-Semitic content, including retweets. However, cases where anti-Semitic tweets were retweeted for the purpose of condemning them were excluded by the study, as described above. Because the query was run on Twitter data captured in early January 2018, it does not include a large number anti-Semitic tweets that were deleted by their owners or from accounts previously shut down by Twitter for violating its terms of 6 / 60 service. As a result, the actual number of anti-Semitic tweets is undoubtedly larger than the number estimated in this study. By the same token, the data for this study was supplied before the so-called “Twitter Purge” of late February 2018, when Twitter deactivated thousands of accounts associated with automated bots. It is possible that some anti-Semitic tweets were promulgated by such bots, and so a new search of Twitter might identify fewer anti-Semitic tweets than reported in this report. The Boolean query was text-based, so tweets containing anti-Semitic imagery without anti-Semitic text would not have been pulled in.
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