Antisemitism on Social Media

Monika Hübscher, Sabine von Mering, Monika Hübscher, Sabine von Mering

• Éditeur: Routledge
• Année: 2022
• ISBN:
  9781000554342
• Édition: 1
• Sujet: Social Sciences
• Sous-sujet: Sociology

Antisemitism on Social Media is a book for all who want to understand this phenomenon.

Researchers interested in the matter will find innovative methodologies ( CrowdTangle or Voyant Tools mixed with discourse analysis) and new concepts (tertiary antisemitism, antisemitic escalation) that should become standard in research on antisemitism on social media. It is also an invitation to students and up-and-coming and established scholars to study this phenomenon further. This interdisciplinary volume addresses how social media with its technology and business model has revolutionized the dissemination of antisemitism and how this impacts not only victims of antisemitic hate speech but also society at large. The book gives insight into case studies on different platforms such as Twitter, Facebook, TikTok, YouTube, and Telegram. It also demonstrates how social media is weaponized through the dissemination of antisemitic content by political actors from the right, the left, and the extreme fringe, and critically assesses existing counter-strategies.

People working for social media companies, policy makers, practitioners, and journalists will benefit from the questions raised, the findings, and the recommendations. Educators who teach courses on antisemitism, hate speech, extremism, conspiracies, and Holocaust denial but also those who teach future leaders in computer technology will find this volume an important resource.

1 A Snapshot of Antisemitism on Social Media in 2021

DOI: 10.4324/9781003200499-2

Monika Hübscher and Sabine von Mering

Introduction¹

The culmination of antisemitism into a genocide is a testament to how technology has been weaponized in history: the Nazis used the technology of IBM’s Hollerith machine for the identification of Jews and other minorities (Black, 2001). In Nazi Germany, the radio, newspapers, and movies that were shown in the smallest villages helped disseminate antisemitic propaganda, which set up the conditions for the mass murder of millions. In fact, technology has played a crucial role in genocide since the industrial revolution. The dissemination of hateful propaganda through radio had a major influence on Hutus who murdered their Tutsi neighbors in Ruanda in the 1990s (Carlson, 2021, pp. 14–25). The invention of social media marks a transition from one-way communication of linear mass media (such as newspapers and the radio) to mass communication by user-driven creation and dissemination of content. Through algorithm-driven technology, which serves to generate profit, the dissemination of hate thus got a major upgrade. In 2018, a devastating UN report underlined the role of Facebook in enabling the unrestricted spread of hate speech which led to genocidal violence against the Muslim Rohingya community in Myanmar (UN Human Rights Council, 2018). Against this terrifying backdrop, and in view of the fact that everyone who is on social media is potentially able to circulate hate on a large scale, we take a look at antisemitism on the platforms. This chapter outlines how social media’s technology and business model are able to amplify antisemitism. After looking at available removal and counter-strategies and their limitations, the chapter turns to the scholarship on the matter and identifies research gaps and challenges, followed by a set of recommendations.

Technology and Business Model

Due to the business model and algorithm-driven technology of social media, we are confronted with an unprecedented dissemination of antisemitic hate on an unknown scale. Because social media is available wherever there is internet connection, it is impossible to know how much antisemitic content is generated and disseminated since it appears in many modes and languages. Antisemitism on social media manifests as memes, gifs, videos, vlogs, and comments, and many other multimodal formats in which people and institutions are attacked for being Jewish, because of their actual or supposed affiliation with Judaism or because of their affiliation with Jews. The generation and dissemination of antisemitism are made possible and reinforced by each social media platform’s features, such as liking, sharing, and commenting, and are often mixed with disinformation and other forms of prejudice, such as misogyny (cp. Hübscher et al., 2019).

Social media is also the place where Jews feel most directly confronted with antisemitism.² In 2018, more than 10,000 Jews in 13 countries, including Germany, were registered for the EU study “Experiences and Perceptions of Antisemitism. Second Survey on Discrimination and Hate Crimes against Jews in the EU.” The study, which collected everyday experiences and perceptions of antisemitism, shows that 89% of Jews surveyed rated antisemitism as the most problematic on social media, even before experiences with antisemitism in public places, in the media, and in politics (FRA, 2018). Another report generated by the Community Security Trust documents anti-Jewish attacks in the United Kingdom in 2019 and not only records an increase in antisemitic incidents in general, but also shows that antisemitism is most commonly communicated on social media (CST, 2019, p. 35). Hate speech on social media can be a precursor to hateful violence in real life. According to a CBS news report, the shooter of the massacre at the Tree of Life Congregation in Pittsburgh on October 17, 2019, who killed 11 and wounded 6 others, had posted antisemitic comments against the Hebrew Immigrant Aid Society (HIAS) on the “free speech” platform Gab, utilized mostly by the extreme right. He accused HIAS of aiding immigrants “that kill our people” (Pegus, 2018).

With the help of cheaply available social bots and organized troll farms,³ social media can be used to target Jewish people and institutions on a large scale. During the 2016 presidential election campaign in the United States, 800 Jewish journalists were followed and harassed on Twitter in an organized troll attack with antisemitic content. The majority of the hate messages that were signed with pro-Trump slogans were aimed at ten Jewish journalists who were targeted daily by around 1,600 trolls (Green, 2016). This example shows how antisemitism can be weaponized on social media for political campaigns, to polarize, and above all to intimidate. Additionally, the manifestos written by the perpetrators of terror attacks on Jews and Jewish institutions have been circulated widely on social media (Amend, 2018).

Social media companies like to claim that they are merely offering a platform for users to connect with others, but all social media content is moderated by algorithms and edited, e.g., through removal by content moderators (Gillespie, 2018, p. 5 ff). Most social media users are unaware of the fact that what they are seeing on their social media is decided by algorithms who adapt content to the users’ online behavior⁴ (Lanier, 2018, p. 6; also cp. Nield, 2020). Furthermore, users subconsciously learn that outrageous content, such as Holocaust denial, creates buzz (attention) in the form of reactions (likes, dislikes, comments, and shares), which in turn is profitable (Lanier, 2018, p. 13). All content, also hateful and polarizing content like antisemitism and Holocaust denial, creates profit for social media companies. As Roger McNamee, one of the early investors in Facebook, now one of its strongest critics, said in the PBS Frontline documentary The Facebook Dilemma

polarization was the key to the model, this idea of appealing to people’s lower-level emotions, things like fear and anger, to create greater engagement, and, in the context of Facebook – more time on site, more sharing, and therefore more advertisement value.

(PBS, 2018)⁵

Social media businesses depend on user-generated posts for their revenue. There are two revenue streams: users generate content, which, if it creates a lot of buzz, becomes more attractive for advertisers, which in turn means more revenue for social media companies. However, often revenue is derived from advertisements based on users’ personal data, which social media companies collect and monetize. Once collected, it is impossible to prevent that data from being abused by bad actors, the police, or the government. Data protection experts have been warning about this for years. Here are a few recent examples of minorities specifically being targeted: the Russian state is surveilling students’ social media accounts regarding LGBTQ activity (Baume, 2020); in Pittsburgh, police surveilled social media accounts of Black Lives Matter activists (Riehl, 2020). There is no reason to believe Jews could not also become targets.

Social media algorithms are adaptive and favor content with lots of engagement. The buzz from engagement with negative, harmful, or hateful content creates something that Chamath Palihapitiya, Facebook’s former vice president of user growth called “dopamine-driven feedback-loop,” which he fears has a massive negative impact on society (Brown, 2017). To exemplify, it can be said that antisemitic content, when it is liked and shared and commented on, constitutes social validation and reward for the user who posted it, and, thus, social media encourages the creation and dissemination of hateful content, such as antisemitism. A recent study shows how Facebook, with the help of a “stimulus-response loop,” prioritizes incendiary content, such as hateful speech and visuals, which subsequently becomes normalized. The study further shows how recommendation algorithms, a feature of many social media platforms, lead users toward consuming more extreme content (Munn, 2020). It can be concluded that through algorithmic selection, elevation, and recommendation of content that has lots of engagement (which often contains incendiary content), social media companies shape and manipulate users’ perception of what is acceptable.

Social media companies also provide digital infrastructure to extremists. Despite deplatforming efforts, terror organizations are continuously and extensively using Twitter and YouTube more or less openly. Recently, accounts of the so-called Islamic State have been found on the relatively new social media platform TikTok, which is mostly used by children and young adults (Wells, 2019). The extreme right has successfully utilized several platforms and their technology to recruit followers and to widely disseminate hateful content so extensively that social scientist Julia Ebner has called them “radicalization machines” (2020). Efforts to tackle hate speech in general and antisemitism in particular have been irresponsibly ineffective, and the resources dedicated to these efforts have been inconsequential in light of the astronomical profits some platforms have accumulated. As the number of incidents where a connection between hate content on the platforms and offline violence seems to exist continues to mount, the task of removing hateful content is left to algorithmic hate speech detection and the problematic concept of content moderators (see below).

Counter-strategies

Artificial Intelligence (AI) to Remove Hate?

Antisemitism on social media exemplifies the difficulties of algorithmic hate detection, which could also be called “AI to remove hate.” To detect antisemitic hate on social media, AI has to be fed with antisemitic keywords and content. But with constant changes in technology and changing restrictions to combat hate, expressions of antisemitism also adapt. Thus, AI needs to adapt continuously as well, in addition to the challenge of having to be fed with antisemitic keywords and examples in virtually all languages, and staying up-to-date on forms in which antisemitic expressions occur. Another challenge is that algorithmic detection by keywords does not discriminate between hateful and educational content. This became apparent in efforts to educate about the Holocaust to counter Holocaust denial on the platforms. Educational content ended up being removed because of the platforms’ community standards on hate speech due to AI’s inability to distinguish Holocaust denial from Holocaust education (Sales, 2021). There is another issue with AI and that is bias, because as mathematician Cathy O’Neil said in an interview with NPR in 2018: “Algorithms embed existing bias into code.” AI can never be ethical. It can only reflect the algorithms’ creators – mathematicians and computer scientists who are still predominantly white, cisgender, and male (cp. O’Neil, 2017, p. 3).

In addition to using automatic hate speech detection, social media companies rely on users, Think Tanks, and NGOs that deal with the matter to flag and report hate. The project “Decoding Antisemitism” at the Center for Antisemitism Research Berlin combines qualitative antisemitism studies with machine learning and quantitative research in German, English, and French (ZfA/KCL, 2021). This pioneering project has a lot of potential. However, relying solely on artificial intelligence to solve this issue is problematic, for the reasons explained above. Moreover, there is no process that prevents content that has been removed – whether detected by AI or by a content moderator – from simply being posted again.

AI has mostly been trained to detect hate speech and is in its infancy with regard to detecting hateful visuals. While it may currently be able to detect symbols like swastikas, it might take a long time until it can identify the semiotic complexity of an antisemitic meme.⁶

Content Moderation

In a 2021 report, researchers at the Center for Countering Digital Hate (CCDH) evaluated antisemitic content on Facebook, YouTube, TikTok, Twitter, and Instagram for six weeks. As a result, CCDH found that the social media companies acted on less than 84% of the reported antisemitic incidents on their platforms. Altogether the CCDH collected 714 antisemitic posts, which had been viewed a shocking 7.3 million times by social media users. There is an excess of violent content on social media, including livestreams and videos of rape, murder, suicides, beheadings, and torture. This may be one reason why antisemitic hate speech is not being treated as a top priority. The failure t...

Table des matières

• Cover
• Half-Title
• Endorsements
• Title
• Copyright
• Dedication
• Contents
• List of illustrations
• Foreword
• Acknowledgments
• Notes on Contributors
• Introduction
• 1 A Snapshot of Antisemitism on Social Media in 2021
• 2 Deep State, Child Sacrifices, and the “Plandemic”: The Historical Background of Antisemitic Tropes within the QAnon Movement
• 3 Tertiary Antisemitism in Social Media Posts of Germany’s Alternative für Deutschland
• 4 “Everyone I know Isn’t Antisemitic”: Antisemitism in Facebook Pages Supportive of the UK Labour Party
• 5 Attacks on Democracy?: A Troll-Attack on YouTube
• 6 Social Media and System Collapse: How Extremists Built an International Neo-Nazi Network
• 7 Antisemitic Rhetoric in Urdu on YouTube: An Analysis
• 8 Antisemitic Narratives on YouTube and Telegram as Part of Conspiracy Beliefs about COVID-19
• 9 Reconstructing an Antisemitic Meme on Social Media through Objective Hermeneutics
• 10 New Antisemitism on TikTok
• 11 The Impact of Antisemitic Content and Hate Speech on Social Media on Young Jewish Social Media Users
• 12 Toward an AI Definition of Antisemitism?
• 13 “To Report or Not to Report”: Antisemitism on Social Media and the Role of Civil Society
• 14 Antisemitism on Social Media Platforms: Placing the Problem into Perspective
• Index