AI-Powered Content Moderation: How Platforms Combat Harmful Content with AI

This article discusses how AI technologies are applied to content moderation (content risk control) on online platforms. With the explosive growth of user-generated content across text, images, live streaming, and short videos, platforms face increasing challenges in detecting and filtering pornographic, vulgar, and other harmful content.

The content covers three main types of malicious content that require moderation:

1. Pornographic Text Detection: Text is the largest information carrier on the internet. Malicious text often uses variant characters, similar-looking characters, and homophones to evade detection. The article recommends combining text strategies with algorithmic models (such as TextCNN) to improve recall and accuracy. Key techniques include using character-based and pinyin-based Word2Vec for semantic enrichment, data augmentation through character splitting, and combining static and dynamic word embeddings.

2. Pornographic Image Detection: Images are the second largest information form. The article divides image moderation into two categories: (a) detecting explicit harmful elements using classic image classification/object detection with Attention mechanisms; (b) handling abstract concepts (like sexual teasing) using multi-label recognition technology with label-level dynamic sampling to address class imbalance.

3. Live Streaming and Video Detection: Video content moderation faces challenges including real-time requirements (under 500ms), performance optimization through frame skipping and parallel processing, and audio detection involving VAD (Voice Activity Detection), feature extraction (MFCC/Fbank), and TDNN-based embedding extraction.

The article also discusses different solutions for small and large enterprises, emphasizing that continuous model improvement and active collection of missed samples are essential for effective content moderation.