Digital Hate Review
ISSUE 1 – SEPTEMBER 2026
FEATURED
Evaluating Large Language Models for Antisemitic Incident Classification
Most automated tools for detecting antisemitism online have been built to spot hateful language — slurs, threatening rhetoric, coded expressions. The authors argue that this leaves a serious gap. A large share of real-world antisemitic harm does not show up in speech alone. Incidents recorded in civil society reports, news articles, and official records — vandalism, harassment, exclusion from campus events — often contain no recognizable hate terms...
FEATURED
Dr. Matthias J. Becker Leads Conversation on XYZ on the ABC Podcast
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Issue 1
Research
A Computational Approach
Patrick Y. Wu, Venkata Dhanush Kikkisetti, Sunela Maddineni and Nathalie Japkowicz
Automated hate-speech detection systems face a structural limit: they can only flag what they already know to look for. Systems trained on labeled datasets of known slurs will tend to miss whatever new vocabulary hate communities develop next — and the authors note that evading detection is itself a driver of linguistic creativity. When a moderation system learns to flag a term, that term tends to be replaced. New expressions emerge...
Routine Distortion: Beyond the AI Slop
Mykola Makhortykh
Generative AI is increasingly implicated in the production and circulation of distorted representations of the Holocaust, ranging from fabricated historical images — such as the wave of AI-generated images of Nazi concentration camps that circulated on social media in summer 2025, apparently produced to game platforms' monetization programs — to chatbot systems like Grok, which at some point called itself "MechaHitler"...
Mapping Affective Polarization in YouTube Shorts
Daniel Miehling
Miehling addresses a methodological and empirical gap in the study of online political communication, particularly in the context of highly polarized conflicts such as the Israel–Hamas war. The author argues that digital communication consisting of user-generated content is often shaped by emotive cues that signal ideological alignment and provide insight into polarization and sentiment dynamics. However, much of the existing research on communication...
Evaluating Large Language Models
Karina Halevy, Julia Mendelsohn, Chan Young Park, Yulia Tsvetkov and Maarten Sap
Most automated tools for detecting antisemitism online have been built to spot hateful language — slurs, threatening rhetoric, coded expressions. The authors argue that this leaves a serious gap. A large share of real-world antisemitic harm does not show up in speech alone. Incidents recorded in civil society reports, news articles, and official records — vandalism, harassment, exclusion from campus events — often contain no recognizable hate terms...
Selective Sources, Scrutiny, and the Absence of Standards
Tatiana Glezer
The article addresses what the author identifies as a recurring problem in journalism studies: the lack of operational and measurable standards for core professional values such as objectivity, impartiality, and accuracy. Glezer argues that while these ideals are central to journalistic ethics, they remain vague in practice and difficult to translate into systematic indicators. As a result, the field lacks unified tools for identifying, measuring...
Legal Forum
The Legal Forum is a structured, cumulative, citable scholarly space for legal and doctrinal engagement with digital hate, platform governance, and AI accountability.
Without Anchor
Sophie Xiaoyi Liu
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Perspectives
Perspectives bridges scholarship and practice through editorially curated essays and conversations with researchers, platform engineers, civil society actors, policymakers, journalists, and public intellectuals.
The Death of Authenticity Online
Max Shulman-Litwin
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Your Future Is a Mirrored Cage
David Kuszmar
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Governability-by-Design
Mayank Kejriwal
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Generative AI
Natalie Alkiviadou
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Countering Holocaust
Stefania Manca
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SUBMISSIONS
For Authors
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