Introduction

©2024 Matthias J. Becker et al., CC BY 4.0 https://doi.org/10.11647/OBP.0406.00

This book showcases key findings and analyses of an innovative research project in the field of web-related antisemitism studies. Established at the Centre for Research on Antisemitism (ZfA) at the Technische Universität Berlin in 2020, Decoding Antisemitism: An AI-Driven Study on Hate Speech and Imagery Online¹ brings together researchers from different disciplines with the aim of exploring the patterns of antisemitic communication on social media. Each researcher has brought their particular experiences, insights and interests from the fields of semiotics—including linguistics (semantics and pragmatics) and image analysis—(social) media studies, history, as well as political and social sciences. Such collaboration ensures that the analyses of the online datasets collected as part of the project have been detailed, nuanced and comprehensive.

At the same time, each of the researchers has been making additional observations, in part thanks to the scope and richness of the dataset: the multitude of topics it contains, the varied angles they can be viewed from, and the multiple overlaps and differences between antisemitic hate speech and many other pertinent phenomena. So far, the joint project-related publications have not been able to completely reflect this diversity of both research interests and discourse phenomena. In this volume, we finally provide a space for broader conclusions from the analysis of current expressions of online antisemitism within the political mainstreams of the UK, Germany and France, but also in exploratory studies in relation to the US as well as to other, more extremist online discourse, carried out within this research project from 2020 to 2024.

The eight studies in this volume are therefore not just an extension of the work within the project, but a product of the interdisciplinary format of Decoding Antisemitism—a format designed to explore a complex object of study (antisemitism), produced in varied patterns (user statements in online threads) in a highly dynamic sphere of communication (the interactive web) that in many ways remains a black box, notoriously difficult to illuminate. Intensifying the efforts in describing, raising awareness of, preventing and regulating online antisemitism, and online hate more generally, is an urgent task not only because of its kaleidoscopic multiplicity and evolving nature, but also because its various expressions seem to be increasing in both number and strength (Zannettou et al. 2018). This became particularly evident after the attacks perpetrated by Hamas on 7 October 2023 (CST 2023, RIAS 2023, SPCJ 2023). However, even such a noticeable trend is difficult to capture fully with the analytical methods available so far, due to this complexity present at the different levels.

The first level is the communication space of the interactive web, which “has dramatically changed the very time/space axes of the subject’s existence” (Kramsch 2009: 159). Comment sections are the core dialogical spaces, where web users address each other as well as an imaginary audience, similar to that of mass media (Virtanen and Kääntä 2018). They can interact with people from across the globe in a spontaneous and immediate manner, reproducing oral interactions (Ko 1996, Herring 2010). As a result, their language can differ markedly from traditional written text. The online comment genre is also characterised by a certain fluidity, which can come across as “less correct, complex and coherent than standard written language” (Herring 2008: 616). At the same time, online communication has gained a new type of complexity, enriched, influenced and modified by hashtags, memes and other multimodal elements. It is also affected by various more general conditions online that have a long-term effect on our communication behaviour (Troschke and Becker 2019, see also Schwarz-Friesel 2013). Web users have the possibility to remain anonymous: this identity distance contrasts with accelerated, intensified, and sometimes even escalated communication processes. Everything can be said, at any time; the outlook of being sanctioned or even prosecuted for online statements has existed for only a short time. The fact that explosive sources and radicalising content are accessible at all times and locations further reinforces this escalation. All these aspects or conditions of communication have a lasting effect on the way we behave on the internet, but also on how we think and feel, and thus perceive the world in its entirety. The internet now functions as an amplifier, which “increases our potential for good and productive work as well as for inappropriate and immoral endeavors” (Banschick and Banschick 2003: 161).

On social media, web users may be exposed to various and sometimes conflicting viewpoints (Bakshy et al. 2015). However, this exposure does not necessarily result in bridging the divides; instead, web users tend to perceive these divergencies as a threat to their own identities and outlooks. This can lead them to either avoid the confrontation (John and Dvir-Gvirsman 2015) or attack the differing points of view (Mor et al. 2016). They also tend to seek out sources confirming their existing opinions (Stroud 2011, Monnier and Seoane 2019, Wolleback et al. 2019), and to join virtual communities which already share their interests and points of view. Even though the notion of such echo chambers is starting to come under critique (Arguedas et al. 2022), several researchers nevertheless propose their existence (Matuszewski and Szabó 2019, Wolleback et al. 2019). Echo chambers strengthen both the bonds among the web users and the ideologies they express (Pariser 2011), a polarisation which may become particularly dangerous when the ideologies circulating within these communities are hate ideologies, as they may lead to an increased dehumanisation of the Other through the language they employ (Pacilli et al. 2016, Cassese 2019). The spread of hate speech is facilitated, again, by the sense of anonymity such online milieus create (Mondal et al. 2017), which in turn escalates the expression of hateful and exclusionary ideas which web users may not have articulated in offline interactions (Schwarz-Friesel 2013).

Normalisation of hate speech informs the second level of the intricate phenomenon at hand. As hate speech spreads from extremist milieus (Ebner 2023) into mainstream communication, the boundaries of what can be said without fear of condemnation from one’s peers, or banishment from publicly shared spaces are pushed ever further. This emboldens individuals to express hatred in online spaces more frequently and more freely; through repetition, hate-speech fallacies and stereotypes, they create new discourse norms, often mirrored by official or legal regulations. Statements by public figures and internet celebrities explicitly or implicitly encouraging hate can boost and accelerate this process, even as online discourse can equally quickly turn against them. Despite the efforts invested in moderating online communications, the amount of data is so vast that it is difficult for the various platforms to track all the hate speech content. Furthermore, to avoid detection by human or automated moderators, but also to convey messages in an attractive manner, web users resort to regularly updated discursive strategies, such as wordplay, allusions and coded memes.

The effect of normalised verbal violence can perhaps be felt in the rise in physical violence (Saha, Chandrasekharan and De Choudhury 2019, Müller and Schwarz 2020). In recent years, its increased presence has at the very least correlated with the radicalisation of social and political movements and counter movements, as well as political groups (Tappin and McKay 2019) or segregating tendencies through extreme polarisation. It also coincides with the trend of dehumanisation of out-groups and invisibilisation of suffering. When analysing hate speech online, it is difficult, if not impossible, to determine whether the speaker intended to hurt the target. Therefore, in both the project and this volume we adopted the INACH definition of cyber hate, which includes both intentional and unintentional discriminatory statements.2

Antisemitism, the third level of the object of our study, is a chameleon-like hate ideology which has kept morphing and adapting throughout its existence over two millennia (Wistrich 1992, Bergmann 2016; for the distinction between anti-Judaism and antisemitism see Julius 2010, Williams and Wald 2023). From anti-Judaism in times of Christianity to the racially charged antisemitism of modernity, two further forms were added in the twentieth century: secondary (post-1945) and Israel-related antisemitism, which prove how highly complex and adaptable this hate ideology can be, embedding itself in various social and political milieus, and now also thriving online (for secondary antisemitism, see Becker et al. 2024). On the one hand, the conceptual (i.e. content-related) repertoire of antisemitism has become broader; on the other, classical stereotypes such as deicide, greed, evil or mendacity3 have been partly or entirely modernised. The antisemitic notion of Jewish greed (and partly also immorality) has been updated to the idea that Jews or Israel exploit the Holocaust in order to achieve pecuniary or symbolic gains. This new framing has been achieved via the concept of instrumentalisation, of either antisemitism or the Holocaust, centrally anchored in secondary antisemitism. Similarly, the classical concept of innate Jewish evil is now being applied to Israel, in particular in the form of the Nazi analogy. These two instances demonstrate how versatile antisemitism is, and how highly compatible it seems to be with a wide spectrum of political positioning and social environments.

Antisemitism is not only a threat to Jewish communities but is also one of the greatest challenges to social cohesion and the future of democracy, as hatred of Jews often correlates with a resentful attitude and a simplistic binary worldview pitting a supposedly homogenous ‘us’ against a destructive and malign ‘them’ in the arena of politics, the media, as well as in academia and science.4 Moreover, and in stark contrast to other forms of hate, the continuing impact of contemporary antisemitism seems to be dismissed and misunderstood—as shown, for example, by the long gestating but broadly unnoticed antisemitism within the UK left, which finally emerged onto the public domain during Jeremy Corbyn’s leadership of the Labour Party (see the various studies on Labour antisemitism and Jeremy Corbyn; for David Miller, the academic in Bristol accused of spreading conspiracy theories regarding Israel, see Becker et al. 2021). This culture of debate is all too attached to the political positioning or educational background of the person, group or party in question, and loses sight of antisemitism in the process. A similar pattern occurred in the Documenta 15 art exhibition in the German city of Kassel in the summer of 2022, when multimodally conveyed hostility towards Jews was trivialised or indirectly justified through the idea of cultural relativism; the art sector displayed a gross lack of understanding of the subject and simplified, dichotomous world views (see Ascone et al. 2022, Burack 2023).

A sudden awakening in the political and media context could then be observed when fears of a rise in antisemitism (and other hate ideologies) online arose as a result of Elon Musk’s takeover of Twitter (now X), as he announced a reduction in content moderation and a significant cutback in collaborations with the political and academic sectors (Miller et al. 2023; see also Jikeli and Soemer 2023). The antisemitic death wishes and overt conspiracy theories voiced by Kanye West, a successful musician and influencer with a gigantic following, proved that antisemitism has found its place in the mainstream and cultural sector of the West (Chapelan et al. 2023). Repercussions of these events are of international proportions and will not fuel various fires in the US discourse alone; they have an enormous impact on the presence and openness of antisemitism on social media worldwide, which makes hatred of Jews permissible and brings it back to the streets. It is precisely this mainstream antisemitism that—partly camouflaged in its communicative guise, partly legitimised by the speaker’s social position—has the potential to spread throughout society, and is therefore far more dangerous than that hostility towards Jews by radicalised fringe groups, which is rejected from the outset and (in certain cases) sanctioned.

Political and legal answers: Measures adopted to counter antisemitism and hate speech

At a global level, numerous countries and institutions have taken steps to counter hate speech and antisemitism. The past few years saw the implementation of the Loi Avia and NetzDG, in France and Germany respectively. According to the latest report by the European Union Agency for Fundamental Rights (FRA),5 14 European countries have already implemented NetzDG measures in order to tackle antisemitism, while eight countries are currently developing new strategies to adopt. Likewise, the Institute for Strategic Dialogue (ISD), together with B’nai B’rith International and the United Nations Educational, Scientific and Cultural Organization (UNESCO), has provided a toolkit to help civil society tackle antisemitism online.⁶ The Digital Services Act (DSA) is a legislative proposal put forth by the European Commission in December 2020. The aim of the DSA is to regulate digital services and online platforms within the European Union (EU) to ensure a safe and accountable digital environment for web users.7 Furthermore, the Inter-Parliamentary Task Force to Combat Online Antisemitism has recently organised two summits, in Washington, DC (September 2022) and Brussels (June 2023), in order to promote an ongoing dialogue between lawmakers and social media platforms.

Despite the national and international efforts to understand and tackle antisemitism online, various gaps are becoming visible. It is imperative to reflect more deeply on how antisemitic discourse comes about and is circulated in the first place, as language is the most important vehicle of any ideology (Althusser 1970 [2011], Pêcheux 1975). Particular attention needs to be paid to the seemingly acceptable, usually unsanctioned dog whistles or implicit and coded forms that are difficult to detect and can therefore spread into politically moderate (online) milieus. This approach will help to understand the impact of online antisemitism on contemporary social, political and cultural contexts and practices in different language communities and to develop counter-strategies against corresponding trends.

State of the art: Current research on antisemitic communication

The interactive web has generated an incredibly large amount of data. Due to the relatively large presence of hateful content, various new techniques have been developed to track antisemitism and other hate ideologies. The institute CyberWell collects antisemitic statements posted online and offers the possibility to report them to the different social media platforms; ADL and Zannettou et al. (2020) use vector analyses to investigate antisemitism on platforms such as 4chan and Gab. Meanwhile, the London-based Community Security Trust, in collaboration with Signify, has been analysing antisemitic hate speech on Twitter with the use of machine learning.

Qualitative approach to the study of antisemitic web comments has received little attention so far. The goal of these analyses is to examine the way antisemitism is expressed explicitly and/or implicitly, as well as to identify linguistic patterns that might have gone unnoticed when adopting a quantitative approach only (see Schwarz-Friesel 2019, Becker 2021). Furthermore, some of these qualitative studies have been conducted to develop and improve algorithms that would better detect antisemitic content online. In this context, corpus linguistics (Gries 2009, Leech 2014) proves to be a good methodology for investigating the different forms of antisemitic expressions. By collecting a large amount of original data from the web, it is possible to identify the linguistic characteristics specific to online antisemitic discourse as well as to determine its statistically significant features.

In order to achieve more solid results, some researchers have adopted mixed-method approaches. Jikeli and Soemer (2023) highlight the importance of combining quantitative and qualitative analyses when studying phenomena as complex as online hate speech. Similar approaches have been employed to closely examine antisemitic content in popular social media, such as X (formerly Twitter) (Jikeli et al. 2014), Facebook and YouTube (Allington and Joshi 2020). In the context of the Decoding Antisemitism project, Mihaljević et al. (2023) have tested Google’s tool Perspective API, which uses machine-learning models to identify abusive web comments and provide a score of toxicity, with the goal of assisting readers and moderators in tackling hate content. These tests, conducted on large corpora of data collected from mainstream media, provide new and additional insights to the analysis of online antisemitism in extreme milieus (Hübscher and von Mering 2022).

Decoding antisemitism: An AI-driven study on hate speech and imagery online

The pilot project Decoding Antisemitism is based at the Centre for Research on Antisemitism at the Technische Universität Berlin, carrying out research in close collaboration with the HTW (University of Applied Sciences) in Berlin, and with the support of HateLab at Cardiff University and King’s College London. The project seeks to find new, technologically enhanced ways to identify and analyse antisemitism online, in both its explicit and disguised forms. As mentioned at the start of this introduction, it has brought together an international, interdisciplinary team of expert researchers with the goal of investigating the frequency, content and structure of antisemitic hate speech posted on mainstream news websites and social media platforms in the UK, France and Germany.

At the core of the analyses presented in the chapters of this anthology is the project’s research design and the data collected in its course (more than 130,000 comments from the three language communities). Contrary to the approach adopted in many of the existing studies into hate speech, here the collection of the data is not based on a list of keywords such as ‘Israel’ or ‘Jews,’ but rather on news events that are likely to trigger antisemitic reactions. Such events include—to name but a few—the escalation phase in the Arab-Israeli conflict in May 2021, the war in Ukraine and Kanye West’s antisemitic remarks, which have strongly influenced the online debate culture in Europe as well. The threads—i.e. comment sections of news websites and their official social media platforms—were fed into the analysis while retaining their chronological and dialogue structure. The analysis is based on a mixed-method approach: first, the data is examined within the framework of Mayring’s qualitative content analysis (2015). Here, the experts’ annotation follows a classification system developed for the purposes of this research project, which comprises both deductive and inductive categories (Meibauer 2008), depending on the patterns that emerge in the data studied. The categories in the classification system comprise both classic and new forms of antisemitic concepts (Schoeps and Schlör 1996, Julius 2010), as well as the linguistic and multimodal phenomena employed by web users in the analysed comment sections. For the context-sensitive analysis of a comment within a thread, this means that each statement is examined in terms of content (above-mentioned concepts) as well as form (explicitly vs. implicitly communicated), and care is also taken to consider any references to the article topic as well as other user comments.

The results of these qualitative analyses then form the basis of algorithms that replicate the experts’ decisions and are intended to enhance the detection of antisemitic content on the internet to a completely new level. The iterative process between experts from the fields of humanities and social sciences on the one hand and data science experts on the other will shift the in-depth qualitative analysis to a much broader scale, so that disparately larger amounts of data can be categorised in a reliable way. The findings obtained in the previous steps also form the basis of quantitative analyses in order to identify statistically significant patterns, completing the picture of trends in contemporary antisemitism.

The chapters collected in this anthology reflect the project’s research design. While the research is based on a solid foundation of traditional antisemitism studies, as well as seminal works from the fields of linguistics, semiotics, history and philosophy, it is innovative in terms of both the data used for analysis, and the approach applied to it. The studies presented here employ empirical analysis of content published in the comment sections of online news outlets and different social media platforms in the past few years. This is crucial for a body of work that emphasises the characteristics of current hate speech expressions, and of online hate speech in particular. The fact that it has been sourced from platforms within the political mainstream makes it highly relevant as well: while there is, naturally, a great value to the study of extremist milieus (Barna and Knap 2019, CST 2019, Zannettou et al. 2020, ADL 2021, Hübscher and von Mering 2022), our focus is on the discourse that can directly impact the majority of web users in the language communities we explore. Moreover, so-called mainstream antisemitism poses an enormous challenge not only for academic analysis, but also for Jewish communities and society as a whole. While recent antisemitic shootings in Pittsburgh, Halle and Poway are clearly rejected across society, antisemitism in politically moderate contexts—in art, culture and academia—is all too often minimised, as the position of the discourse absolves it of antisemitism. The results presented in this publication make it clear that this is a misguided judgement. In this respect, the chapters are to be understood as a plea to take a closer look at this desideratum in the context of web-related antisemitism studies and hate studies in general.

Owing to the integrative nature of the Decoding Antisemitism project, the authors of the work presented in this collection have also been able to incorporate a similarly interdisciplinary approach into their individual research. In doing so, they offer a comprehensive view of the issues they focus on, which enriches their findings and creates interest for a wider audience. It is also mindful of the frameworks of examination, where the subject matter is treated in a holistic and intersectional manner and operationalised within its methodologically rigorous analysis. In terms of content analysis, it focuses on conceptual units as well as the linguistic and visual patterns carrying these units. Finally, the data is analysed both qualitatively and quantitatively—the former still being underrepresented in the field of internet studies. By reflecting the current reality of contemporary antisemitic hate speech online in mainstream discourses, and by analysing its ability to remain hidden in plain sight by continuously adapting to the current context, this anthology aims to give a full picture of contemporary antisemitism on every level: in terms of its mixed-methods approach, the cross-disciplinary outlook, and the wide range of themes encompassing media, society and culture.

The volume begins with the development of selected conceptual questions in the context of antisemitism studies, which are presented on the foundation of our empirical analysis of language data. Karolina Placzynta explores the intersections of antisemitism and misogyny in online debates around public figures (Chapter 1). Next, we present linguistic and discourse analytical case studies centred on the reproduction, support and rejection of antisemitic tropes: Matthias J. Becker examines the dividing line between conservative and far-right antisemitism by analysing projections onto Jordan Peterson, a conservative intellectual, after interviewing the Israeli PM (Chapter 2); Alexis Chapelan’s study shows the way web users express their support to contested media personalities such as Dieudonné and Kanye West (Chapter 3). Matthew Bolton investigates the concept of GENOCIDE and its use in the context of the discourse around the Arab-Israeli conflict, a topic that has been of intense interest in the wake of the 7 October attacks and Israeli retaliation in Gaza (Chapter 4), while Laura Ascone assesses the links between the web comments conveying antisemitism and those countering it, and how counter-narratives can sometimes fuel antisemitism and other forms of hate speech (Chapter 5). We also include the emergence of new forms of hate speech: this aspect is examined by Marcus Scheiber in his qualitative analysis of antisemitic memes and the potential of verbal and visual elements to mutually integrate antisemitism into online communication (Chapter 6).

The qualitative analyses are complemented and enriched by quantitative assessments prepared by Chloé Vincent, who looks at the structure of the comment trees in online discussions in relation to the occurrence of antisemitic comments, using the dataset accumulated in the project so far (Chapter 7). Finally, to integrate research questions from the field of data science, Elisabeth Steffen, Milena Pustet and Helena Mihaljević elaborate on recent work regarding the capabilities of content-moderation tools in recognising antisemitic posts as toxic, and report on current achievements in training deep-learning-based models for automated detection of such content (Chapter 8).

Practical considerations

Across all the chapters, the authors use numerous examples from the project dataset; they have been taken from the comment sections of mainstream news outlets of the UK, France and Germany. The examples have been anonymised; however, in order to present the data as faithfully as possible, they retain their original spelling, punctuation and grammar, including any errors, inconsistencies or offensive terms. Whenever French or German comments are used to illustrate the text, they have been translated into standard British English, with the original provided in footnotes. The list of specific sources of the examples can be found at the end of each chapter.

The frequent mentions of antisemitic concepts, such as stereotypes and analogies, are presented in small caps, in accordance with the conventions of cognitive linguistics, which uses this format to highlight phenomena that exist on the mental level and can be reproduced through language. Linguistic phenomena, such as irony, puns or death wishes are not distinguished in such a way.

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