Dating App Moderation That Actually Works

What I Built

AI moderation system for dating apps that doesn't flag "Want to grab coffee?" as harassment.

Most moderation tools treat dating apps like Twitter - zero tolerance, immediate bans. I built something that understands dating conversations are different.

Key features:

• Progressive warnings instead of instant bans
• "You're beautiful" scored as appropriate, not harassment
• Crisis intervention for self-harm (support, not punishment)
• Dual system: gentle handling for normal chat, escalation for real threats
• Interactive Streamlit demo with professional UI
• Robust error handling for AI safety filters and edge cases
• Consistent output parsing with graceful fallbacks

Why It Matters

Dating apps lose users when moderation is too aggressive. Over-moderation kills engagement.

The business problem:

• False positives frustrate users into leaving
• Support tickets flood in from wrongly banned users
• Appeal processes waste time and money

Dating app context matters:

• "You're hot" between matched users isn't harassment
• Phone number requests are normal after conversation builds
• Hook-up language should score 2-3, not 8-9

How It Works

Built two different prompts that route automatically:

Normal conversations → Gentle scoring with progressive enforcement Serious issues → Immediate escalation (hate speech, self-harm, fraud)

Evaluation process:

• Tested on 45+ real dating app messages
• Manual scoring to find false positive patterns
• Langfuse tracking for every decision
• Systematic prompt improvements based on failures

Crisis handling: Self-harm detection doesn't remove content - it provides mental health resources and notifies appropriate support teams.

Interactive Demo

Professional Streamlit interface with:

• Clean two-column layout with proper spacing
• Quick test buttons for common scenarios (Hate Speech, Self-Harm, Fraud)
• Loading states and success feedback
• Expandable technical analysis view
• Mobile-friendly responsive design

Quick tests available:

• Hate speech detection and scoring
• Self-harm crisis intervention
• Fraud/scam identification
• Normal dating conversation handling

Example Outputs

Boundary-Pushing Content Analysis:

Crisis Intervention for Self-Harm:

Hate Speech Detection:

Setup & Usage

Prerequisites

• Python 3.8+
• OpenAI API key
• Langfuse account (for tracking)

Core Files

• hinge_moderation_v2.py - Main moderation engine
• web_demo.py - Streamlit interface
• hinge-terms-of-use.txt - Reference guidelines

Results from Testing

• Fixed AI output consistency issues - Resolved parsing errors and format inconsistencies
• Optimized token usage - Reduced specialized prompts from 10,177 to ~744 tokens (93% reduction)
• Enhanced safety filter handling - Graceful responses when OpenAI safety systems trigger
• Improved hate speech detection - Enhanced keyword detection for more accurate routing
• Reduced false positives in severity scoring for dating app contexts
• Progressive enforcement maintains safety while improving user experience
• Crisis intervention provides support rather than punishment for self-harm content

Technical Architecture

• GPT-4 dual-prompt system for routing and analysis
• Optimized prompt engineering with token limit management
• Robust safety filter detection for OpenAI content policy triggers
• Langfuse integration for observability and improvement tracking
• Streamlit frontend with professional UI/UX and visual examples
• Consistent output parsing with improved format handling
• Session state management for interactive testing

🚀 Coming Soon

RAG Implementation

• Policy Knowledge Base: Vector database of dating app guidelines and precedents
• Context-Aware Decisions: Retrieve relevant policy examples for consistent enforcement
• Appeal Case History: Learn from previous moderation decisions and outcomes
• Dynamic Policy Updates: Automatically incorporate new guidelines without code changes

Structured Outputs

• JSON Schema Validation: Guaranteed consistent API responses for production integration
• Typed Moderation Results: Structured data for downstream systems and analytics
• Audit Trail Format: Standardized logging for compliance and review processes

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Built for AI Product Manager interviews - Demonstrates systematic approach to trust & safety, user experience focus, and technical implementation skills.

Stack: Python, GPT-4, Streamlit, Langfuse