Quality-Based Scoring of User-Generated Content
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Solution Overview
Problem
User-generated content on internet platforms like forums and community question-answering sites often suffers from low quality due to subjective user voting, leading to biased and inaccurate representation of content quality, with many posts remaining unvoted or receiving sparse votes that do not align with user needs.
Innovation Solution
An algorithmic approach that combines social, semantic, and syntactic signals to score content quality by evaluating multiple perspectives such as surface word similarity, Explicit Semantic Analysis, answer similarity, query performance predictor, sentiment analysis, answerer reputation, and textual characteristics, and weights these measures to provide an overall quality score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If user voting is used to measure content quality, then content can be ranked by quality measure, but the measure becomes biased and inaccurate due to sparse votes and user preferences
Solution Approach 1:
The patent introduces an automated quality measurement system as an intermediary between user voting and content ranking. This system uses multiple signals (user voting, answer quality, recency, popularity) to compute a comprehensive quality score, mediating the gap between simple user votes and accurate content quality assessment. The automated system processes sparse user votes alongside other objective metrics to produce a more reliable ranking measure.
2Measurement precision
If multiple quality measures are combined to improve accuracy, then content quality assessment becomes more objective, but the system complexity increases
Solution Approach 1:
The patent segments the content quality assessment into multiple independent signal components: user voting signal, answer quality signal, recency signal, and popularity signal. Each signal is measured and weighted separately, allowing the system to handle complexity through modular processing. This segmentation enables accurate multi-factor assessment while maintaining manageable system architecture through independent signal processing channels.
Solution Approach 2:
The patent dynamically adjusts the weight parameters of different quality signals based on content type and context. The system changes parameters (signal weights) to optimize quality assessment for different scenarios, such as adjusting the importance of recency versus answer quality for different question types. This parameter adaptation allows accurate assessment across diverse content while using a unified measurement framework.
Data Source
AI summary
Methods and devices for assessing the quality of user-generated content are described. In one embodiment, a method is disclosed for measuring the quality of a user-generated answer to a question by combining various factors, including question-answer surface word vector similarity, question-answer explicit semantic analysis vector similarity, answer-answer explicit sematic analysis vector similarity, query performance predictor, sentiment analysis, textual analysis of the answer, and reputation of the answerer. The method uses a learning procedure to determine the best algorithm for measuring the overall quality of the answer based on these factors.