Social Network Content Ranking via Sports Algorithms
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Solution Overview
Problem
Existing ranking algorithms like PageRank are not effective in accurately assessing the importance or quality of content on social networks, as they are based on web surfing models that do not accurately represent user interactions on platforms like Twitter.
Innovation Solution
A system and process that ranks content on social networks by treating user interactions as 'games' and applying sports ranking algorithms, such as the Colley method, to determine the importance and quality of users based on their interactions, including following, retweeting, and mentioning, which provides a more accurate representation of user influence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If PageRank algorithm is used to rank social network content, then the ranking can be computed efficiently based on web surfing model, but the accuracy of ranking does not reflect actual user interactions on social networks
Solution Approach 1:
The patent changes the fundamental parameters of the ranking model by replacing the web surfing behavior parameters (random link following, teleportation probability) with social network interaction parameters (following relationships, retweeting, mentioning). This allows the system to accurately capture social network dynamics while maintaining computational efficiency through similar iterative ranking algorithms.
Solution Approach 2:
The patent inverts the traditional web ranking approach by treating social network interactions as the primary ranking signal rather than treating them as secondary to web browsing behavior. Instead of adapting web models to social networks, the patent adapts ranking algorithms to social network-specific interaction patterns, fundamentally reversing the adaptation direction.
2Productivity
If traditional web ranking models are applied to social networks, then the computational framework is available, but the results do not provide insightful and helpful rankings for social context
Solution Approach 1:
The patent modifies the ranking parameters to reflect social network-specific behaviors. Instead of using generic web browsing parameters, it implements parameters that capture following, retweeting, and mentioning relationships, allowing efficient computation that accurately measures user influence within the social context.
3Measurement precision
If sports ranking algorithms are applied to social network interactions, then accurate user influence ranking is achieved, but the system complexity increases compared to simple web models
Solution Approach 1:
The patent creates a universal ranking framework that can handle multiple types of social network interactions (following, retweeting, mentioning) through a single algorithmic structure. This multi-functional approach maintains computational efficiency while accurately capturing diverse user influence patterns, avoiding the need for separate complex models for each interaction type.
Data Source
AI summary
A system and process for ranking at least one of the quality and importance of content on a social network is disclosed. The system and process include monitoring one of actions and information of social network users, determining whether the actions or information of the social network users fits a predefined definition of a game between at least two social network users, determining the results of the game between the at least two social network users, applying a sports ranking algorithm to the results between the at least two social network users, and determining a rank of at least one of the quality and importance of content of the social network users based on an outcome of the sports ranking algorithm.