Navigation Path Data Structure for Content Sharing Tracking
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Solution Overview
Problem
Current social network systems lack the ability to effectively track and analyze the navigation paths of content sharing within their environments, which limits the understanding of content dissemination and user influence.
Innovation Solution
A system that includes a processor programmed to detect content sharing and automatically determine a navigation path data structure, allowing for the identification of paths from one content to another within the network environment, and initiating actions based on this data, such as attributing contributions and suggesting related content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If social network systems allow users to share content freely, then content dissemination speed increases, but the ability to track and analyze navigation paths is lost
Solution Approach 1:
The system embeds navigation path tracking functionality into the content sharing infrastructure before content is shared. Navigation path data structures are created and populated as content moves through the network, enabling retrospective analysis of dissemination paths without interfering with the natural sharing process.
Solution Approach 2:
A navigation path data structure acts as an intermediary between the content sharing process and the analysis system. This data structure captures navigation information (user IDs, timestamps, content identifiers) as content moves through the network, allowing tracking without disrupting the free sharing mechanism.
2Loss of information
If the system tracks navigation paths of content sharing, then understanding of content dissemination improves, but system complexity increases
Solution Approach 1:
The tracking system is segmented into independent components: navigation path data structures for individual content items, separate detection mechanisms for sharing events, and analysis modules that process the collected data. This modular approach allows the tracking functionality to be added without fundamentally complicating the core social network system.
Solution Approach 2:
Instead of modifying the core content sharing system to add tracking capabilities, the invention creates copies of relevant data (navigation paths, user interactions, timestamps) in a separate data structure. This parallel tracking system maintains simplicity by not altering the original content sharing mechanism.
3Measurement precision
If the system automatically determines navigation path data structures, then content attribution accuracy improves, but processing time increases
Solution Approach 1:
Navigation path data structures are built incrementally as content is shared, rather than being constructed entirely after the fact. Each sharing event adds data to the navigation path structure, so when analysis is needed, the data is already organized and ready for processing.
Solution Approach 2:
The navigation path data structures are self-updating through automated detection of sharing events. The system automatically captures navigation information and populates data structures without requiring manual intervention or complex real-time processing, reducing the time burden on the system.
Data Source
AI summary
A current sharing, in a system by a first user, of first content with at least one other user can be detected. A navigation path data structure indicating at least one navigation path from a second content to the first content within a network environment can be automatically determined. Responsive to determining the navigation path data structure indicating the at least one navigation path from the second content to the first content within the network environment, performance of at least one action can be automatically initiated based on the determined navigation path data structure.