Message Relevance Scoring via Regard Phrase Parsing
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Solution Overview
Problem
Existing methods for evaluating and sorting user-generated content in online communities face challenges due to variability in quality and the presence of biases in user feedback, which can be influenced by social obligations, making it difficult to reliably identify high-quality content.
Innovation Solution
A computer-implemented method that assigns relevance scores to message elements by parsing content and metadata to detect regard indicators such as appreciative phrase marks and actions, using a link-based rank computation method to determine the intrinsic value of content, thereby ranking messages based on user-regard expressions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If user-generated feedback (votes, ratings) is used to evaluate content quality, then content sorting and triage become possible, but the feedback becomes biased due to user-felt social obligations
Solution Approach 1:
The patent introduces an intermediary system that analyzes linguistic expressions of regard in message contents rather than directly using user feedback metadata. This intermediary layer (language analysis) mediates between the biased user feedback and the final quality assessment, extracting objective signals from textual expressions while filtering out social obligation biases.
Solution Approach 2:
The patent replaces the mechanical system of direct user feedback (votes, ratings) with a linguistic analysis system that processes message contents. Instead of relying on users to explicitly rate content, the system substitutes this with automated detection of regard-expressing language patterns, transforming the evaluation mechanism from explicit mechanical feedback to implicit linguistic analysis.
2Ease of manufacture
If traditional user feedback mechanisms are used, then content evaluation is simple to implement, but the quality assessment becomes unreliable due to social biases
Solution Approach 1:
The patent replaces the simple but biased mechanical feedback system with a sophisticated linguistic analysis system. The new system processes message contents through parsing and pattern recognition to detect regard indicators, substituting the simple vote/rating mechanism with a more complex but accurate language-based evaluation approach.
Solution Approach 2:
The patent changes the evaluation parameter from explicit feedback metadata (votes, ratings) to implicit linguistic features within message contents. By analyzing the presence and frequency of regard-expressing phrases and patterns in the text itself, the system transforms the measurement parameter from user actions to linguistic characteristics, improving accuracy while maintaining implementation feasibility.
3Ease of operation
If user feedback is collected and processed, then content sorting is enabled, but the feedback contains biases from social interactions that reduce evaluation reliability
Solution Approach 1:
The patent introduces linguistic analysis as an intermediary between user interactions and content evaluation. Instead of directly using feedback from social interactions, the system inserts a mediation layer that analyzes message contents for regard indicators, filtering out biases inherent in social feedback while preserving the sorting capability.
Solution Approach 2:
The patent extracts relevant evaluation signals from message contents by detecting specific linguistic patterns that express regard. This extraction process separates the trustworthy linguistic indicators of quality from the biased social feedback metadata, taking out only the reliable signals needed for accurate content evaluation and sorting.
Data Source
AI summary
A structured collection of message elements comprising message elements and oriented child-parent links each connecting a message element to a parent message element is provided. Each message element comprises a message content and metadata including an author identity and a timestamp. The message contents are parsed to generate appreciative phrase marks assigned to the message elements. An appreciative phrase mark is generated in response to detecting that the parsed message content of a later message element comprises a string of characters that matches an entry within a predefined dictionary of regard-expressing phrases. The appreciative phrase mark is assigned to an earlier message element that is connected to the later message element by a sequence of child-parent links. The metadata is parsed to detect the marks and further regard indicators assigned to the message elements. Relevance scores of the message elements are computed as a function of the regard indicators.