Detailed Documentation

1. Introduction

This document outlines the algorithm for the Explore tab—drawing inspiration from Instagram’s content discovery—and details the methodologies for filtering video content to remove nudity, explicit material, or dark language. The goal is to build an app that blends TikTok’s engaging short-form video experience with Instagram’s subscription model, ensuring safe and appropriate content.

2. Explore Tab Algorithm

The Explore tab is designed to surface content that is both engaging and personalized. The algorithm comprises several key components:

2.1. Data Collection & User Profiling

• User Interactions: Collect data on likes, comments, shares, video watch time, follows, and search queries.
• Behavior Analysis: Track browsing history to identify preferred content categories.
• Profile Building: Create user profiles that capture content preferences and engagement patterns.

2.2. Content Scoring & Ranking

• Engagement Metrics: Assign scores based on likes, shares, comments, and view duration.
• Creator Credibility: Factor in creator reputation (follower count, engagement rate).
• Virality: Identify content with rapidly increasing engagement.
• Ranking: Combine these factors to rank content for each user.

2.3. Personalized Recommendations

• Recommendation Engine: Utilize a mix of collaborative filtering and content-based filtering.
• User Similarity: Recommend content based on similarities in engagement and interests.
• Machine Learning Models: Consider models like Matrix Factorization, Neural Collaborative Filtering, or Deep Learning-based ranking systems (e.g., TensorFlow’s TFRS).

2.4. Trending & Diversity Boosting

• Trending Content: Highlight viral or trending content separately.
• Diversity: Ensure a mix of content genres and creators, including emerging talents.
• Content Rotation: Prevent overexposure to a single content category.

2.5. Continuous Feedback Loop & A/B Testing

• Real-Time Adjustments: Continuously refine recommendations based on user feedback.
• A/B Testing: Regularly test algorithm variants to optimize performance.

3. Content Moderation for Videos

To maintain community standards, the app employs a multi-layered approach to filter out nudity, explicit content, and dark language.

3.1. AI-Based Content Moderation

A. Nudity & Explicit Content Detection

• Computer Vision Models: Utilize tools like Google Vision AI, AWS Rekognition, or OpenAI’s CLIP.
• NSFW Detection: Consider open-source models (e.g., Yahoo’s Open NSFW) or third-party APIs for nudity detection.
• Frame Sampling: Analyze video frames at intervals to identify inappropriate imagery.

B. Speech-to-Text for Language Moderation

• Conversion: Convert spoken content to text using APIs such as Google Speech-to-Text or OpenAI’s Whisper.
• NLP Filtering: Apply Natural Language Processing to detect profanity, hate speech, or dark themes.
• Toxicity Scoring: Use models like Google’s Perspective API or Detoxify for assessing content toxicity.

C. Text Moderation for Captions & Comments

• Profanity Filters: Run captions, titles, and user comments through filtering systems.
• Sentiment Analysis: Detect negative sentiment, hate speech, or abusive language.

3.2. Pre-Upload & Post-Upload Moderation

• Pre-Upload Filtering: Scan content before allowing it to be published.
• Post-Upload Monitoring: Continuously monitor and re-scan content after publication.

3.3. Community Reporting & Human Review

• User Reports: Enable users to report questionable content.
• Human Moderators: Use manual review for edge cases where AI confidence is low.
• Feedback Mechanism: Integrate reports and moderator decisions to further train and refine AI models.

3.4. Enforcement: Shadow Banning & Penalties

• Visibility Restrictions: Temporarily limit the visibility of flagged content until reviewed.
• Strike System: Implement a tiered penalty system leading up to account restrictions or bans.

3.5. Age-Based Filtering

• Age Verification: Incorporate measures for verifying user age.
• Content Restrictions: Use AI to ensure age-appropriate content for underage audiences.

3.6. Tech Stack & APIs

• Computer Vision: Google Vision API, AWS Rekognition, OpenAI CLIP, NSFW detection models.
• Speech & Text Processing: OpenAI Whisper, Google Speech-to-Text.
• Language Moderation: Google Perspective API, Detoxify, HateSonar.

4. Implementation and Integration Considerations

• Modular Architecture: Design the system so that the Explore tab and content moderation modules work independently.
• Real-Time Processing: Ensure low-latency for real-time user interactions and content filtering.
• Scalability: Plan for horizontal scaling, especially for AI processing components.
• Privacy & Security: Comply with data protection regulations and ensure secure handling of user data.
• Feedback Loop: Regularly update and retrain machine learning models based on moderation outcomes and user engagement.

5. Conclusion

This document provides a comprehensive overview of the Explore tab algorithm and the multi-faceted approach to video content moderation. By integrating AI-driven techniques with human oversight, the app aims to provide a personalized and safe environment for users, balancing content discovery with community standards.

For further enhancements, consider continuous monitoring, user feedback incorporation, and regular updates to AI models and moderation policies.