Abstract
The emergence of interactive online spaces and the evolution of internet-based communication have dramatically changed the way individuals relate to the world and interact with other web users. The specificities of online communication, such as anonymity and mutual reinforcement of web users, have led to an increase and normalisation of hate speech (Troschke and Becker 2019). “Antisemitismus im Internet.
Erscheinungsformen, Spezifika, Bekämpfung.” In Das neue Unbehagen. Antisemitismus in Deutschland und Europa heute, edited by Günther Jikeli and Olaf Glöckner, 151–72. Glöckner Hildesheim: Olms; Becker and Troschke 2023. “Decoding Implicit Hate Speech: The example of antisemitism.” In Challenges and perspectives of hate speech analysis: An interdisciplinary anthology, edited by Christian Strippel, Sünje Paasch-Colberg, Martin Emmer, and Joachim Trebbe. Berlin: Digital Communication Research). This paper presents our qualitative analysis of antisemitic content on Facebook profiles of British, French, and German mainstream media, generated in the framework of the Decoding Antisemitism research project. The online debates of interest were identified in the context of discourse events – real-world events that have the potential to trigger antisemitic reactions – such as the Russian invasion of Ukraine, escalation phases in the Middle East conflict, including the events of October 2023, or scandals and instances of hate crime in Europe and beyond. The results of our analyses point to several commonalities in the three language communities in how Israel is conceptualized and evaluated through stereotypes in these comment sections. On the other hand, there are also consistent differences between the three corpora in the choice of stereotypes. Another significant difference concerns the verbal immediacy and frequency with which these mental concepts are communicated in online debates. This article will attempt to map the qualitative and quantitative patterns, compare and contrast the analyses for the three language communities, and at the same time put forward for discussion possible sociohistorical and -political reasons for this discursive behavior (cf. Ascone et al. 2022. Decoding Antisemitism: An AI-driven Study on Hate Speech and Imagery Online. Discourse Report 4. Berlin: Technische Universität Berlin. Centre for Research on Antisemitism).

Keywords: antisemitism; hate speech; corpus linguistics; qualitative content analysis