We created a fine-grained AI system for the detection of antisemitism. This Explainable AI will identify English and German anti-Semitic expressions of dehumanization, verbal aggression and conspiracies in online social media messages across platforms, to support high-level decision making.

Online hate speech proliferation has created a difficult problem for social media platforms. A particular challenge relates to the use of coded language by groups interested in both creating a sense of belonging for its users and evading detection. Coded language evolves quickly and its use varies over time. This paper proposes a methodology for detecting emerging coded hate-laden terminology. The methodology is tested in the context of online antisemitic discourse. The approach considers posts scraped from social media platforms, often used by extremist users. The posts are scraped using seed expressions related to previously known discourse of hatred towards Jews. The method begins by identifying the expressions most representative of each post and calculating their frequency in the whole corpus. It filters out grammatically incoherent expressions as well as previously encountered ones so as to focus on emergent well-formed terminology. This is followed by an assessment of semantic similarity to known antisemitic terminology using a fine-tuned large language model, and subsequent filtering out of the expressions that are too distant from known expressions of hatred. Emergent antisemitic expressions containing terms clearly relating to Jewish topics are then removed to return only coded expressions of hatred.

The Alfred Landecker Foundation has announced its support for an initiative that will aim to combat the spread of antisemitism and hatred online by using artificial intelligence (AI). Titled "Decoding Antisemitism," the project was financially backed by the Foundation, which donated an additional 3 million Euros to the budget. By supporting the project, the Foundation is joining forces with the Center for Research on Antisemitism at the Technical University of Berlin, King's College London and other renowned scientific institutions in Europe and Israel. The international team, comprised of discourse analysts, computational linguists and historians, is currently focusing its efforts on developing an AI-driven approach to identifying online antisemitism, a feat that may be harder to achieve than expected. Studies have shown that the majority of antisemitic defamation is expressed in implicit ways – through the use of codes for instance ("juice" instead of "Jews") and allusions to certain conspiracy narratives or the reproduction of stereotypes through images.