The Decoding Antisemitism Project—Reflections, Methods, and Goals

Abstract

Abstract This article introduces the pilot project “Decoding Antisemitism: An AI-driven Study on Hate Speech and Imagery Online.” The aim of the project is to analyse the frequency, content and linguistic structure of online antisemitism, with the eventual aim of developing AI machine learning that is capable of recognizing explicit and implicit forms of antisemitic hate speech. The initial focus is on comments found on the websites and social media platforms of major media outlets in the United Kingdom, Germany, and France. The article outlines the project’s multi-step methodological design, which seeks to capture the complexity, diversity and continual development of antisemitism online. The first step is qualitative content analysis. Rather than relying on surveys, here a pre-existing “real-world” data set-namely, threads of online comments responding to media stories judged to be potential triggers for antisemitic speech-is collected and analysed for antisemitic content and linguistic structure by expert coders. The second step is supervised machine learning. Here, models are trained to mimic the decisions of human coders and learn how antisemitic stereotypes are currently reproduced in different web milieus-including implicit forms. The third step is large-scale quantitative analyses in which frequencies and combinations of words and phrases are measured, allowing the exploration of trends from millions of pieces of data.