Decoding Antisemitism: An AI-driven Study on Hate Speech and Imagery Online
The pilot project “Decoding Antisemitism,” carried out by researchers at the Centre for Research on Antisemitism (ZfA, TU Berlin) in collaboration with HTW Berlin, King’s College London, and HateLab in Cardiff examines explicit and implicit antisemitism on leading social media.
Decoding Antisemitism is a research project aiming to identify online antisemitism. Learn more here.
Our interdisciplinary project brings together scientists from the UK, France and Germany. Meet the team.
News
Stay informed about the latest project developments.
Publications
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1
The first Discourse Report
Read moreThe first Discourse Report provides insight into the methodological approaches and the nature of antisemitic hate speech in selected online discourse spaces in Germany and the UK. -
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The second Discourse Report
Read moreThe second Discourse Report presents the definitional basis of our analyses and for the first time provides comprehensive insights into our corpus analyses relating to Great Britain, France and Germany - with a focus on the escalation phase in May 2021, the vaccine rollout in Israel and three independent case studies.
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3
The third Discourse Report
Read moreThe third Discourse Report sheds light on the differences and similarities of antisemitic responses – particularly Holocaust distortion – to two discourse events in France and Germany: the anti-health pass demonstrations and the prosecution of former concentration camp personnel. -
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The fourth Discourse Report
Read moreThe fourth Discourse Report investigates two major international events: the Russian invasion of Ukraine and an eruption of terrorist violence in Israel in early 2022. It also examines four national case studies which illustrate the adaptability of contemporary anti-Jewish prejudice.
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The fifth Discourse Report
Read moreThe fifth Discourse Report analyses online comments in reaction to statements of rapper Kanye West, to 2022 FIFA World Cup in Qatar, and to the Israeli legislative elections. In addition, it presents an evaluation of existing tools for the automated detection of hate speech and presents an innovative approach based on transfer learning.
