Online Content Distribution Maturity Scoring
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Solution Overview
Problem
Existing online content management systems fail to effectively distribute content based on its maturity and the strength of relationships between content creators and viewers, leading to inefficient propagation and engagement.
Innovation Solution
A method and system that dynamically manage the distribution of online content by calculating a maturity score using cognitive analysis and machine learning, allowing content to be viewable by a larger audience as it matures, based on viewer engagement and relationship strength.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If content is made viewable by a large number of viewers immediately upon posting, then content propagation speed is improved, but content maturity and viewer engagement quality deteriorate
Solution Approach 1:
The system dynamically adjusts the viewer audience based on content maturity. Initially, content is viewable only by a first number of viewers (e.g., close connections). As the content matures through viewer engagement components, the system automatically expands the viewer audience to a second number of viewers (larger audience). This dynamic adjustment resolves the contradiction by making the distribution strategy flexible rather than static.
Solution Approach 2:
The system performs preliminary actions by initially limiting content visibility to a small, targeted audience before broader distribution. This preliminary phase allows content to mature with engaged viewers who can provide meaningful feedback and engagement components. Only after this preliminary maturation phase does the system expand visibility to the larger second number of viewers, ensuring content quality before wide propagation.
2Reliability
If content is restricted to a small number of initial viewers, then content maturity quality is improved, but content propagation efficiency deteriorates
Solution Approach 1:
The system implements periodic expansion of the viewer audience based on maturity thresholds. Content progresses through stages: initially viewable by a first number of viewers, then upon reaching maturity thresholds through engagement components, it periodically expands to a second number of viewers. This periodic action ensures quality maturation while maintaining propagation efficiency through scheduled expansion phases.
Solution Approach 2:
The system uses feedback from viewer engagement components to determine when to expand the viewer audience. Viewer interactions (comments, likes, shares) serve as feedback signals that trigger the transition from the first number of viewers to the second number of viewers. This feedback mechanism ensures content has matured sufficiently before broader distribution, resolving the contradiction between quality and efficiency.
3Measurement precision
If manual management of content distribution is used, then distribution control precision is improved, but system complexity and operational effort deteriorate
Solution Approach 1:
The system performs self-service by automatically calculating maturity scores and managing viewer audience expansion without manual intervention. The system autonomously monitors viewer engagement components, calculates maturity scores, and triggers viewer audience expansion when thresholds are met. This self-service capability maintains precise distribution control while eliminating the operational complexity of manual management.
Solution Approach 2:
The system replaces manual mechanical management with automated computational processes. Instead of manual tracking and decision-making for content distribution, the system uses automated maturity score calculations based on viewer engagement data. This substitution of mechanical manual processes with automated computational systems maintains precision while reducing operational complexity.
Data Source
AI summary
Embodiments for managing distribution of online content by one or more processors are described. Content posted to an online channel is detected. The content is viewable by a first number of viewers. A maturity score for the content is calculated. If the calculated maturity score is above a predetermined threshold, the content is caused to be viewable by a second number of viewers. The second number is greater than the first number.