| # Problem Definition Document (1–2 pages) |
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| ## Business context & motivation |
| Online platforms (community forums, e-commerce reviews, internal enterprise collaboration tools) face: |
| - Increased moderation cost as user-generated content scales. |
| - Brand risk and user churn when harmful content is not handled quickly. |
| - Inconsistent enforcement when moderation relies only on humans. |
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| This project builds a **Toxicity Detection & Moderation Agent** that: |
| 1) detects toxic / hateful / threatening language, |
| 2) recommends an action (allow / warn / block / human review), |
| 3) logs signals for monitoring and continual improvement. |
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| ## Target users / stakeholders |
| - **Content moderators**: faster triage, fewer false negatives. |
| - **Trust & Safety**: policy enforcement analytics. |
| - **Product**: reduced user harm and improved retention. |
| - **Developers**: a deployable API for integration. |
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| ## Problem statement |
| Given a user comment, classify multiple toxicity types (multi-label) and decide an appropriate moderation action. |
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| ## Why NLP is required |
| Toxicity and hate speech are expressed in natural language with context, sarcasm, and ambiguity. |
| Rules/keywords alone are brittle and produce many false positives/negatives. |
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| ## Success metrics |
| ### Business metrics |
| - Moderator time saved (minutes/comment) |
| - Reduction in harmful content exposure (e.g., % toxic comments blocked before publication) |
| - Reduction in escalations / user reports |
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| ### Technical metrics |
| - Multi-label F1 (micro/macro) |
| - ROC-AUC per label |
| - False negative rate for high-risk categories (e.g., threats) |
| - Latency: p50/p95 inference time per comment |
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