Design a UGC content moderation system (text + images).

To design a moderation system for UGC (user-generated content) with text and images, the following approach can be used:

1. Data collection and preprocessing:

   • Save content to a moderation queue immediately upon upload.
   • For text — normalization, tokenization.
   • For images — resizing, converting to the required format.

2. Automatic filtering:

   • Text: use NLP models to detect toxicity, spam, offensive content (e.g., classifiers based on BERT or specialized models).
   • Images: apply computer vision models to detect prohibited content (e.g., NSFW, violence).

3. Manual moderation:

   • Content that raises doubts or has low model confidence is sent for review by moderators.
   • An interface for moderators to quickly review and make decisions.

4. Feedback and learning:

   • Manual moderation results are used to retrain models and improve automatic filtering quality.

5. Scalability and fault tolerance:

   • Use microservices for processing text and images.
   • Message queues (e.g., Kafka) for asynchronous processing.
   • Caching results to speed up repeated checks.

6. Security and privacy:

   • Data encryption.
   • Access control to moderation tools.

Example architecture:

User -> Upload API -> Moderation Queue ->
[NLP module for text] + [CV module for images] ->
Results -> Automatic decision or -> Manual moderation ->
Storage and publication

This approach allows effective filtering of unwanted content, reducing the load on moderators and ensuring moderation quality.