Problem Definition Document (1–2 pages)
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.
This project builds a Toxicity Detection & Moderation Agent that:
- detects toxic / hateful / threatening language,
- recommends an action (allow / warn / block / human review),
- logs signals for monitoring and continual improvement.
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.
Problem statement
Given a user comment, classify multiple toxicity types (multi-label) and decide an appropriate moderation action.
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.
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
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