Trust and Safety Harmful Content Classification for a Social Media Platform
A social media platform needed harmful content classified consistently across several languages to train its trust and safety model, after uneven coverage left some of its non-English-speaking markets significantly under-moderated.
Client snapshot
| Industry | Social media and content platforms |
|---|---|
| Region | Global, concentrated in the client's fastest-growing non-English-speaking markets |
| Engagement type | Multilingual harmful content classification for trust and safety |
The challenge
The client's moderation model performed well in English but had meaningfully weaker coverage in several of its fastest-growing markets, where harmful content was both harder to detect and, when missed, carried outsized platform risk.
The approach
Jwuma assigned native-speaker annotators across the client's priority languages to classify content against a detailed harm taxonomy, with disputed or borderline classifications escalated through a review tier and gold-task calibration used to monitor classification consistency across languages.
Results
The client extended consistent harmful content classification coverage into markets that had previously been significantly under-moderated, closing a meaningful gap in its trust and safety coverage.
Frequently asked questions
Why is multilingual harmful content classification harder than English-only moderation?
Because harmful content often relies on local slang, cultural context and coded language that a model trained primarily on English data will not reliably catch.
How does Jwuma maintain classification consistency across many languages?
Through a shared harm taxonomy applied by native-speaker annotators in each language, with gold-task calibration monitoring consistency across the full annotator pool.
What happens when a content classification is disputed or unclear?
It escalates through Jwuma's multi-tier review ladder rather than resting on a single annotator's judgment, given the platform risk of an incorrect call.
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Published 2026-10-02 by Jwuma, operated by Corpshore AI. This case study is an anonymized composite representative of the kind of work Jwuma performs in this industry, described by industry, region and engagement type rather than by company name, since this engagement is not yet cleared for public naming. Discuss a similar program at client.corpshore.ai.