How toxic is antisemitism? Potentials and limitations of automated toxicity scoring for antisemitic online content
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Abstract (arXiv)
The Perspective API, a popular text toxicity assessment service by Google and Jigsaw, has found wide adoption in several application areas, notably content moderation, monitoring, and social media research. We examine its potentials and limitations for the detection of antisemitic online content that, by definition, falls under the toxicity umbrella term. Using a manually annotated German-language dataset comprising around 3,600 posts from Telegram and Twitter, we explore as how toxic antisemitic texts are rated and how the toxicity scores differ regarding different subforms of antisemitism and the stance expressed in the texts. We show that, on a basic level, Perspective API recognizes antisemitic content as toxic, but shows critical weaknesses with respect to non-explicit forms of antisemitism and texts taking a critical stance towards it. Furthermore, using simple text manipulations, we demonstrate that the use of widespread antisemitic codes can substantially reduce API scores, making it rather easy to bypass content moderation based on the service's results.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 13 of its 28 references that are in the map. First posted on arXiv 5 Oct 2023.
Builds on
13 of 28 references.
References · 13
- 2020RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Modelscited by 1,235
- 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 724
- 2019Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classificationcited by 396
- 2020HateCheck: Functional Tests for Hate Speech Detection Modelscited by 215
- 2016Ex Machina: Personal Attacks Seen at Scalecited by 208
- 2020Social Biases in NLP Models as Barriers for Persons with Disabilitiescited by 182
- 2017Deceiving Google's Perspective API Built for Detecting Toxic Commentscited by 99
- 2020Do Platform Migrations Compromise Content Moderation? Evidence from r/The_Donald and r/Incelscited by 15
- 2021On the Globalization of the QAnon Conspiracy Theory Through Telegramcited by 14
- 2022Understanding and Detecting Hateful Content using Contrastive Learningcited by 13
- 2020Measuring and Characterizing Hate Speech on News Websitescited by 12
- 2019Annotating Antisemitic Online Content. Towards an Applicable Definition of Antisemitismcited by 7
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Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2025Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variationscited by 12
- 2026Bye Bye Perspective API: Lessons for Building and Governing Measurement Infrastructurecited by 1
- 2026Evaluating Large Language Models for Antisemitic Incident Classificationcited by 0
- 2026You Frame It: How Conceptual Representations Shape LLM Detection and Reasoning about Antisemitismcited by 0
- 2026You Frame It: How Conceptual Representations Shape LLM Detection and Reasoning about Antisemitism6 Jul 2026
- 2026Evaluating Large Language Models for Antisemitic Incident Classification6 Jul 2026
- 2026Bye Bye Perspective API: Lessons for Building and Governing Measurement Infrastructure28 Apr 2026
- 2025Lost in Moderation: How Commercial Content Moderation APIs Over- and Under-Moderate Group-Targeted Hate Speech and Linguistic Variations3 Mar 2025