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Discourse Report 2

Research output: Book/ReportCommissioned report

Matthias J. Becker, Daniel Allington

Original languageEnglish
Place of PublicationBerlin
PublisherTechnical University of Berlin Centre for Research on Antisemitism
Number of pages47
PublishedAug 2021

Publication series

NameDecoding Antisemitism: An AI-driven Study on Hate Speech and Imagery Online
PublisherTechnical University of Berlin Centre for Research on Antisemitism


King's Authors


For the second discourse report on the pilot project “Decoding Antisemitism,” the research team studied in detail more than 15,000 comments, mainly coming from Facebook profiles of leading mainstream media outlets in Great Britain, France, and Germany.

Regarding responses online to the recent escalation phase of the Arab-Israeli conflict in May, the results confirm that the conflict is a central facilitator for antisemitic expressions. Even in the context of politically moderate discourses, the presence of antisemitic topoi is 12.6% in the French, 13.6% in the German, and – more than twice as much – 26.9% in
the British dataset.

Analysis of web comments on the Israeli vaccination campaign (in connection with the accusation of Palestinians being excluded from the vaccine rollout) again suggests that even media stories about Israeli logistical successes that are entirely unrelated to the conflict quickly become opportunities for the articulation of antisemitic ideas and stereotypes. As with the escalation event, analysis demonstrates that antisemitism appears far more frequently in British social media debates than their French and German
counterparts – but also indicates a marked difference in the types of stereotypes regularly deployed in the respective countries.

Three other discourse events on the national level were accusations of antisemitism against three prominent individuals – hailing from a diversity of political milieus and professional backgrounds – David Miller, Dieudonné M’bala M’bala and Hans-Georg Maaßen. The scrutiny of the web users’ reaction to these cases points to the remarkable adaptability of antisemitism. At the same time, antisemitism in this context functions as part of a broader process of construction of enemy images, targeting electoral rivals, political or corporate elites as well as minority groups.

The datasets coded for this report will serve as first training material for classifiers as the machine learning phase of our project gets underway. The ongoing development of such categorised datasets will help increase the accuracy of the tested algorithms.

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