Social Content Risk Identification Using Tree-Structured Feature Expansion
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Solution Overview
Problem
Existing social content risk identification methods rely on keyword matching, which is unreliable and ineffective in accurately identifying risky content, especially in cyber security and real social security contexts.
Innovation Solution
A method and device utilizing a tree structured machine learning model to extend feature dimensions of social content data, followed by processing with a deep machine learning model, such as deep neural networks, for more accurate risk identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If keyword matching method is used for social content risk identification, then the implementation is simple, but the identification accuracy is low and reliability is poor
Solution Approach 1:
The patent replaces the mechanical keyword matching system with a machine learning-based automated system. The tree structured model and deep machine learning model automatically learn patterns from social content data, substituting manual keyword configuration with intelligent algorithms that adaptively identify risky content, thereby improving reliability while maintaining implementation feasibility through automated processing.
Solution Approach 2:
The patent transforms the identification approach by changing from fixed keyword parameters to dynamic feature representations. Social content is converted into numerical features that capture semantic meaning and contextual relationships, allowing the model to identify risky content based on patterns rather than predefined keywords, significantly improving identification accuracy and reliability.
2Measurement precision
If tree structured machine learning model with dimension extension is used, then the feature representation accuracy is improved, but the model complexity increases
Solution Approach 1:
The patent divides the complex identification task into two sequential stages: first, a tree structured model performs dimension extension on extracted features to create more comprehensive representations; second, a deep machine learning model processes these extended features for final risk identification. This segmentation allows each model to specialize in specific aspects, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The patent applies dimension extension by transforming original features into higher-dimensional feature space using the tree structured model. This dimensional transformation creates more nuanced feature representations that capture subtle patterns in social content, enabling the deep learning model to achieve better identification accuracy without directly increasing the complexity of the core identification logic.
3Measurement precision
If deep machine learning model with large quantity of input nodes is used, then the identification accuracy is improved, but the data processing requirements and computational complexity increase
Solution Approach 1:
The patent performs preliminary feature extraction and dimension extension using the tree structured model before feeding data to the deep machine learning model. This preprocessing step organizes and enriches the input data, ensuring that the deep model receives high-quality extended features that maximize its identification capability while optimizing computational efficiency by reducing the burden on the final classification stage.
Data Source
AI summary
One or more implementations of the present specification provide a social content risk identification method. Social content data to be identified is obtained. Features of the social content data are extracted, including a plurality of features of at least one of social behavior records or social message records in the social content data. The features are expanded by generating dimension-extended features using a tree structured machine learning model. The social content data is classified as risky social content data by processing the dimension-extended features using a deep machine learning model.