Preserve the context behind online hate.
Capture. Verify. Understand. Act.
A classifier asks whether one post looks hateful. HateWatch asks whether the evidence needed to understand an entire incident has been preserved — and shows you exactly what is missing.
api checking
what you receive
- IMG_3821.PNG
- Screenshot_2026-08-21.png
- twitter-link.txt
- IMG_3822.PNG
- dm-2.jpeg
- unknown-account.png
- tiktok.txt
- IMG_3830.PNG
what you get
HW-2026-0142
Hostility after community centre open day
- evidence
- 14
- platforms
- 3
- known gaps
- 4
- 01
Capture
screenshots, text, URLs
- 02
Verify
human confirms extraction
- 03
Organize
chronology across platforms
- 04
Analyze
patterns tied to evidence IDs
- 05
Review
classification + audit trail
- 06
Route
preserve, report, escalate
- 07
Export
packet from verified fields
Context Integrity is not a hate score.
It is a transparent completeness checklist: whether enough context exists for a human to review this responsibly. Eight weighted elements, every one of them inspectable. Nothing here rates a person, and nothing here invents precision it does not have.
context integrity = available context ÷ applicable context
Evidence 03 — checklist
67%- ✓evidence artifactreq
- ✓platform10
- ✓content text15
- ✓timestamp15
- ✕source url20
- ✓target context15
- ✕parent context15
- ?capture provenance10
HateWatch never does this.
- ✕scrape social networks
- ✕monitor individuals
- ✕infer religious identity
- ✕identify anonymous users
- ✕publicly score accounts
- ✕automatically accuse anyone
- ✕determine whether something is illegal
- ✕contact police on your behalf
We classify content, visible behaviour, and patterns between evidence — never people. Every finding stays tied to its source evidence and subject to human review.