Social Media Feedback Propagation Across Repost Levels
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Solution Overview
Problem
Users who share content on social networks are unaware of the popularity of their posts beyond the first-level receivers, as they cannot track reposts and re-shares downstream, limiting their understanding of content popularity and engagement.
Innovation Solution
A method to propagate user feedback from reposts back to the original post, using a tree structure to aggregate user indications such as '+1' and comments, with natural language processing to determine comment types and update feedback data, while considering privacy settings, allowing feedback to be propagated upstream regardless of the repost level.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If content is reposted multiple levels downstream on social networks, then the reach and dissemination of content increases, but the original poster's awareness of content popularity and engagement deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where user interactions (likes, comments, shares) on reposted content are automatically tracked and propagated back to the original poster. This creates a closed-loop information flow that resolves the contradiction by maintaining poster awareness despite multi-level content dissemination. The system collects feedback data from downstream users and transmits it upstream to the original post, ensuring the poster receives comprehensive engagement metrics regardless of repost depth.
2Measurement precision
If the system tracks all reposts and user indications across multiple levels, then comprehensive feedback data is collected, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent segments the feedback tracking system into modular components: (1) feedback collection modules at each repost level, (2) data aggregation services that consolidate feedback, and (3) propagation mechanisms that transmit aggregated data upstream. This segmentation allows the system to achieve comprehensive feedback tracking while managing complexity through distributed, independent modules that can be developed and maintained separately.
Solution Approach 2:
The patent introduces intermediary aggregation services that act as mediators between downstream reposts and the original post. These intermediaries collect feedback from multiple sources, perform data consolidation and filtering, then propagate the aggregated results upstream. This intermediary layer reduces the direct complexity burden on the original poster's interface while maintaining comprehensive feedback collection across all repost levels.
3Loss of information
If user feedback is propagated upstream from any repost level, then original posters receive comprehensive engagement data, but the difficulty of tracking and verifying feedback sources increases
Solution Approach 1:
The patent implements a nested tracking structure where each repost level contains embedded identification data about its parent repost and ultimate origin post. This nested hierarchy of identifiers (similar to nested dolls) allows the system to trace feedback sources through multiple levels by following the embedded reference chain. Each feedback record contains nested metadata that progressively traces back to the original poster, enabling verification without requiring complex external tracking infrastructure.
Data Source
AI summary
Propagating user feedback on shared posts is described, including receiving a user indication associated with repost content. The repost content is associated with post content. The post content includes at least one item provided on a website. The repost content includes at least one other item provided on another website or the website, and the at least one other item is associated with the at least one item. The repost content is determined to be associated with the post content. Post feedback data based on the user indication is identified. The post feedback data is associated with the post content, and the post feedback data represents previous user indication. The post feedback data is updated based on the user indication.