AddressHate · Discourse Intelligence
At AddressHate, we build tools to study and empower research related to hate. Our goals and work focus on bridging the infrastructure and knowledge gap between researchers, policy makers, and different disciplines. We aim to track and better understand how ideologies and radicalization transfer between online sources, social media, and AI chatbots. By tracking these sources, studying interactions on them, and building tools that exponentially speed this process up, we aim to better understand and prevent hate. This page showcases our preliminary analytics tools.
Possible Antisemitic content over the last 30 days. These numbers are based on keywords, and not human or machine classified.
The terms and phrases with the most movement over the last week.
Published research
All studies8,400
answers graded
18 models against 400 adversarial prompts on Holocaust denial, the conspiracy canon, and coded dog-whistles.
AddressHate · July 2026
10
models tested
10 models against 200 adversarial prompts on race pseudoscience and the distortion of slavery.
AddressHate · Run ab, July 2026
200
adversarial prompts
10 models against 200 adversarial prompts on “curing,” trans healthcare, and the “groomer” smear.
AddressHate · Run lgbtq, July 2026
Everything on this dashboard
What is being said right now, and where.
How platforms and AI models perform against it.
Platform Signal Grades
An A–F grade per platform for the density of antisemitic lexical signal in our query window. It measures observed signal risk relative to other platforms — it is not a platform-safety or moderation grade.
Compare platformsAI Model SafetyPublished study
How well frontier language models detect antisemitic content, and how often they refuse to repeat antisemitic misinformation. Graded from our own adversarial evaluations, not vendor claims.
The methodology, the studies, and the people behind both.
ResearchPublished study
Our mission, our institutional partners, and every study we have published — each with its methodology and underlying data laid out in full.
TaxonomyReference
Every category we measure, defined in plain language with an example of how it shows up online. The antisemitism scheme follows the Decoding Antisemitism Lexicon.
FAQReference
Plain-language answers on where the data comes from, how content gets labeled, what the grades do and don't mean, and how to read the rest of the dashboard.
Read the FAQStaffReference
The researchers, engineers, and advisors behind the work, and how we work together.
The annotation tool that produces our ground truth.
For UsersReference
What the Annotator does, which sites it runs on, and how to get an account. It captures the reply chain, the quoted post, and the parent video alongside every label.
Why Annotate?Reference
Why hand-labeled context beats scraped text — how annotation feeds moderation tooling, and what gets lost when it is skipped.
Read the case