Content Quality Scoring via Interaction Graph Authority Metrics
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Solution Overview
Problem
Current methods for determining content quality in digital content are inadequate, as they fail to effectively differentiate between high-quality and low-quality content, leading to suboptimal search result rankings and user engagement.
Innovation Solution
A content quality system that generates interaction graphs based on user interactions, calculates hub scores and authority scores using stochastic linked-structure analysis, and determines weighted averages to assign quality scores to users and articles, promoting high-quality content and demoting low-quality content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional content quality determination methods are used, then the system is simple to operate, but the measurement precision of content quality is insufficient
Solution Approach 1:
The patent segments content quality assessment into multiple independent dimensions including author reputation scoring, source credibility evaluation, content originality analysis, and user interaction metrics. Each dimension is processed separately through dedicated computational modules, allowing complex quality assessment to be broken down into manageable segments that can be independently optimized and computed.
Solution Approach 2:
The patent transitions from traditional single-dimensional content quality assessment to multi-dimensional evaluation by incorporating author reputation scores, source credibility ratings, content freshness metrics, and user engagement statistics. This dimensional expansion enables more precise quality measurement by considering multiple facets of content quality simultaneously.
2Productivity
If automated content quality scoring is implemented, then productivity of content evaluation increases, but measurement precision may be reduced due to automation limitations
Solution Approach 1:
The patent implements feedback mechanisms where user interactions with content (likes, shares, comments, dwell time) are continuously collected and used to refine quality scores. The system also incorporates feedback loops where newly published content from high-reputation authors automatically receives initial quality boosts, and quality metrics are periodically re-evaluated as new interaction data becomes available, ensuring continuous improvement of measurement precision.
Solution Approach 2:
The system enables self-service quality assessment by allowing content to automatically receive quality scores based on objective metrics such as author reputation, source credibility, and interaction patterns. This automated self-evaluation reduces manual review requirements while maintaining consistent quality standards through algorithmic assessment of multiple quality dimensions.
3Measurement precision
If multiple quality metrics are calculated for each content item, then measurement precision improves, but loss of information increases due to data processing complexity
Solution Approach 1:
The patent extracts and isolates the most critical quality-determining factors from the vast amount of available data, focusing computation on key metrics such as author reputation score, source credibility rating, and primary interaction statistics. By extracting only the most influential features for quality assessment, the system maintains high measurement precision while minimizing unnecessary data processing overhead and information loss.
Data Source
AI summary
A system and method for calculating quality score for digital content are provided. In example embodiments, a first graph is generated comprising a user node and an article node, the user node corresponds to a user and the article node corresponds to an article. An edge is generated between the user node and the article node in the first graph based on a first action. A second graph is generated comprising the user node and the article node. An edge is generated between the user node and the article node in the second graph based on a second action type. A first authority score is calculated for the article node within the first graph. A second authority score is calculated for the article node within the second graph. A quality score is calculated for the article.