Real-Time Content Quality Scoring Using Asset Analysis
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Solution Overview
Problem
Content providers face challenges in determining the number of interactions a content item will receive without first publishing it, as post-publishing assessments like click-through rate are imperfect and costly, delaying the understanding of content item quality and impact.
Innovation Solution
A method and system that analyze numerical data from text strings and target keywords to estimate the quality of content items by calculating asset mix values, categorical coverage, and keyword coverage, providing a real-time content item score before publication, allowing for pre-launch campaign assessments and suggestions for modification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content providers publish content items and gather data after the fact to assess quality, then they can obtain actual interaction data, but it becomes costly and time-consuming with delayed insights
Solution Approach 1:
The system performs preliminary analysis of content item assets (text strings, images, video) before publication to calculate an estimated quality score. This pre-assessment allows content providers to evaluate potential performance without waiting for post-publishing data, thereby reducing the time loss while maintaining assessment accuracy through multiple asset analysis dimensions.
2Measurement precision
If content providers publish content items to gather interaction data, then they can assess content quality, but the cost of publishing and gathering data increases
Solution Approach 1:
The system creates a digital model or copy of the content item assets and analyzes this copy to generate an estimated quality score without requiring actual publication. By working with replicas of the content assets (text strings, images, video files) in an analysis environment, the system eliminates publication costs while maintaining assessment accuracy through comprehensive asset evaluation.
3Measurement precision
If content providers use traditional post-publishing assessment methods, then they can measure actual interactions, but the ability to optimize content before launch is limited
Solution Approach 1:
The system performs preliminary analysis of content item assets (text strings, images, video) before publication to calculate an estimated quality score. This pre-assessment allows content providers to evaluate potential performance without waiting for post-publishing data, thereby reducing the time loss while maintaining assessment accuracy through multiple asset analysis dimensions.
Solution Approach 2:
The system provides feedback on the estimated quality score and asset analysis results to content providers before publication. This feedback mechanism enables content providers to optimize their content assets based on the analysis results, improving content quality before launch while maintaining the ability to measure actual interactions after publication.
Data Source
AI summary
Systems and methods are disclosed for dynamically analyzing and providing the quality of one or more content items at the time, or substantially close to the time, they are received by a data processing system. The systems and methods described herein can maintain and update the quality score for improving previously created content items after they have been published. The one or more content items can include one or more assets (e.g., one or more headlines, one or more descriptions, images, video, etc.). The data processing system can use numerical analysis methods to determine an overall quality (e.g., estimated clicks) of the content items received by the data processing system using a trained model.