Online Active Learning Probit Model for UGC Filtering
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Solution Overview
Problem
Monitoring user-generated content (UGC) streams for abusive or interesting content is challenging due to concept drift, where content creators adapt to hard-and-fast rules, making it difficult for websites to effectively filter out abusive content while also identifying and promoting interesting content, especially given the high cost and inefficiency of human labelers in online active learning.
Innovation Solution
A processor-executed method using software for online active learning that converts UGC into elemental representations, inputs them into a probit model to predict abusive or interesting content, calculates importance weights, and updates the model with acquired labels, removing abusive content or relocating interesting content based on predictive probabilities, even when labels are unavailable, leveraging an instrumental distribution for conditional updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hard-and-fast rules are used to filter abusive content, then the filtering process is simple and fast, but content creators adapt to these rules making the filtering ineffective over time
Solution Approach 1:
The patent implements dynamic rule updates through online active learning, where the filtering system continuously adapts to new content patterns and creator behaviors. The model is updated in real-time as new data arrives, transforming static hard-and-fast rules into dynamic adaptive rules that maintain effectiveness against evolving abusive content strategies
Solution Approach 2:
The system incorporates feedback loops where filtering results and new content are continuously fed back into the model updating process. Human labelers provide feedback on borderline cases, and the system learns from both automated decisions and human corrections, creating a closed-loop system that improves filtering effectiveness while maintaining speed through automated processing
2Measurement precision
If human labelers are used extensively in online active learning, then the accuracy of content classification improves, but the time and cost increase significantly
Solution Approach 1:
The patent applies partial action by selectively involving human labelers only for specific cases rather than all content. The system uses automated processing for clear-cut cases and reserves human labeling for ambiguous or high-value examples, achieving good classification accuracy without the time cost of exhaustive human review
Solution Approach 2:
The system implements self-service through automated model updating and classification for the majority of content. The active learning framework enables the system to autonomously identify which examples need human labeling and automatically updates the model based on acquired labels, reducing overall dependence on continuous human intervention
3Adaptability or versatility
If the probit model is updated frequently with new data, then the model adapts better to concept drift, but the computational complexity and processing time increase
Solution Approach 1:
The patent implements periodic updating where the probit model is updated at regular intervals or based on accumulated data batches rather than continuously for every new data point. This periodic approach maintains adaptability to concept drift while reducing computational complexity by processing updates in manageable cycles rather than continuous real-time operations
Data Source
AI summary
Software for online active learning receives content posted to an online stream at a website. The software converts the content into an elemental representation and inputs the elemental representation into a probit model to obtain a predictive probability that the content is abusive. The software also calculates an importance weight based on the elemental representation. And the software updates the probit model using the content, the importance weight, and an acquired label if a condition is met. The condition depends on an instrumental distribution. The software removes the content from the online stream if a condition is met. The condition depends on the predictive probability, if an acquired label is unavailable.