Abnormal User Moderation via Interaction Behavior Probability
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Solution Overview
Problem
Existing content moderation technologies struggle to accurately identify and moderate abnormal users, particularly pedophilia users, due to inaccurate information, adversarial behaviors, and failure to consider interaction behaviors between users.
Innovation Solution
A method and apparatus for moderating abnormal users that involves acquiring history behavior data, extracting predetermined valid features, calculating probabilities of abnormal user behavior, establishing a total probability function, and determining candidate users by solving for the maximum value of this function.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing content modulation technologies are used to identify abnormal users, then the moderation process can be performed, but the accuracy of identifying abnormal users is low due to inaccurate information and failure to consider interaction behaviors
Solution Approach 1:
The patent merges individual user behavior data with interaction behavior data between users to form a comprehensive feature set. By combining these data sources, the system captures both individual abnormal behaviors and collaborative deceptive patterns, significantly improving the accuracy of identifying abnormal users while reducing information loss.
Solution Approach 2:
The patent introduces a new dimension of analysis by examining interaction behaviors between users alongside traditional individual user behavior analysis. This dimensional expansion allows the system to detect abnormal patterns that emerge from user interactions, such as coordinated deceptive behaviors, thereby improving identification accuracy without losing contextual information.
2Measurement precision
If existing content modulation technologies are used, then the moderation process can be performed, but the detection accuracy of pedophilia users is low due to adversarial behaviors and inaccurate information
Solution Approach 1:
The patent employs feedback mechanisms by continuously analyzing user behavior data and interaction patterns to update the identification model. This feedback loop allows the system to adapt to adversarial behaviors and inaccurate information, improving detection accuracy of pedophilia users by learning from observed patterns and adjusting its criteria accordingly.
Solution Approach 2:
The patent performs preliminary analysis of user behavior data and interaction patterns before final identification. By pre-processing and analyzing data to extract meaningful features and patterns, the system prepares a comprehensive assessment that accounts for adversarial behaviors, thereby improving detection accuracy before the final moderation decision is made.
3Measurement precision
If user behavior data is analyzed in isolation, then the analysis process is simple, but the detection accuracy is low due to failure to consider interaction behaviors
Solution Approach 1:
The patent segments the data analysis process into distinct modules: individual user behavior analysis, interaction behavior analysis, and integrated identification. This segmentation allows the system to handle complex data systematically, improving detection accuracy while managing computational complexity through structured processing stages.
Solution Approach 2:
The patent creates a multi-functional analysis framework that simultaneously processes individual user behaviors, interaction patterns, and identifies abnormal users. This universal approach consolidates multiple analysis functions into a unified system, improving detection accuracy without proportionally increasing complexity through shared processing resources and integrated algorithms.
Data Source
AI summary
Provided is a method for moderating abnormal users. The method includes: acquiring history behavior data of a plurality of to-be-moderated users, wherein the history behavior data includes behavior data of interactions of the plurality of to-be-moderated users; extracting a plurality of predetermined valid features from the history behavior data for each of the plurality of to-be-moderated users, wherein the predetermined valid features are features predetermined in sample data; calculating, based on predetermined probabilities of events associated with the plurality of valid features, a probability that each of the plurality of to-be-moderated users is the abnormal user; establishing a total probability function based on probabilities that the plurality of to-be-moderated users are the abnormal users; determining candidate users by solving, based on predetermined conditions, a maximum value of the total probability function; and determining the abnormal users by moderating the candidate users.