Content Input Layer

The Content Input Layer serves as the platform’s primary data intake gateway and the foundational stage for AI model semantic understanding and intelligence generation. This module is responsible for receiving, parsing, and structuring user-submitted social content to ensure high-quality, semantically rich, and traceable data inputs—meeting the needs of downstream model training and personalized strategy generation.

The input system is built around three core dimensions: cross-platform integration, semantic labeling, and data permission control.

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Cross-Platform Input Support

Users can submit content published on mainstream external social media platforms via links or text summaries, enabling cross-platform data aggregation and semantic capture. The platform allows one-click pasting of content links or uploading of original text summaries. AI automatically parses metadata (such as timestamps, publishers, and engagement metrics) to enhance training efficiency.

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Optional Semantic Labeling System

Users may optionally add information such as topic tags, emotional tone, and training participation status. These labels can be manually provided or automatically generated and assisted by the platform’s AI models, utilizing NLP-based sentiment analysis and entity extraction modules.

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Privacy & Permission Layer

Supports multiple training privacy modes: public, semi-private, and anonymous. Additional data permission mechanisms include:

• Content Withdrawal Rights: Users can revoke previously submitted content at any time, halting its use in further AI training.

• Transparent Usage Records: Every instance of content training, reference, and model feedback can be traced via a user-accessible activity log.

• Encrypted Flags: Users can mark sensitive content with encryption, making it readable only by themselves.