Semantic Analysis System for Financial Social Network Credibility
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for evaluating the credibility of information on social networks and forums are time-consuming, labor-intensive, and ineffective in preventing the spread of false information, as they rely on manual screening and fail to account for user behavior patterns.
Innovation Solution
A semantic analysis system that constructs a knowledge graph to analyze user behavior based on published information, identifies similar user behaviors, and establishes associations between users to verify the correctness of information and grade user credibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual screening and filtering of user information is performed, then false information can be identified, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical screening with an automated semantic analysis system that uses knowledge graphs and natural language processing to identify false information, prediction accuracy, and user behavior patterns automatically, eliminating the need for human reviewers while maintaining or improving detection accuracy
Solution Approach 2:
The system enables self-service by automatically analyzing user-published information, evaluating prediction accuracy against actual outcomes, and grading user credibility without requiring manual intervention, allowing the platform to autonomously manage information quality and user reputation
2Reliability
If manual filtering of false information is performed, then some false information can be removed, but user behavior patterns and associations cannot be effectively analyzed
Solution Approach 1:
The semantic analysis system performs multiple functions simultaneously: it filters false information, evaluates prediction accuracy, analyzes user behavior patterns, and establishes user associations all through the same automated platform, making the system versatile rather than single-purpose
Solution Approach 2:
The knowledge graph serves as an intermediary structure that connects user information, prediction data, and behavior patterns, enabling the system to analyze relationships and associations between users while simultaneously filtering false information through structured semantic relationships
3Productivity
If automated semantic analysis with knowledge graph is implemented, then information verification efficiency is improved, but system complexity increases
Solution Approach 1:
The system segments the complex verification task into distinct modules: knowledge graph construction for semantic relationships, natural language processing for information extraction, prediction accuracy evaluation, and user behavior analysis, allowing each component to be developed and optimized independently while working together efficiently
Solution Approach 2:
The system performs preliminary action by pre-construction a knowledge graph containing domain-specific semantic relationships and user behavior patterns before actual information verification occurs, enabling faster and more accurate analysis during the verification process without requiring complex real-time computation
Data Source
AI summary
The present disclosure relates to a method for semantic analysis performed by a computing device in the financial field. The method includes constructing a knowledge graph with a computer device. The method further includes obtaining, by the computer device, information published by the first user on a social network over the network. The method further includes the computer device generating standard information based on the information. The method further includes generating, by the computer device, a user behavior based on the standard information and the knowledge graph. The method further includes searching, by the computer device, for another user with similar user behavior based on the user behavior.