Multi-Factor Social Media Reliability Scoring for Accurate Extraction
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Solution Overview
Problem
Existing techniques for extracting useful information from social media contributions lack accuracy, as they rely solely on evaluating credibility based on a user's daily life sphere, leading to insufficient evaluation accuracy.
Innovation Solution
A processing apparatus and method that compute the reliability of social media contributions using multiple factors such as the activity area of the contributor, reliability of activity area estimation, disclosure of the contributor's profile, attribute of the contributor, and category of the content, to extract information with higher accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If credibility evaluation is based solely on daily life sphere (activity area), then the evaluation process is simple, but the accuracy of credibility evaluation is insufficient
Solution Approach 1:
The credibility evaluation process is segmented into multiple independent evaluation dimensions: activity area evaluation, friend relationship evaluation, and content category evaluation. Each dimension contributes to the overall credibility score, allowing the system to maintain simplicity in each individual evaluation while achieving high accuracy through the combination of multiple segments.
Solution Approach 2:
The evaluation system transitions from a single-dimensional evaluation (only activity area) to a multi-dimensional evaluation framework that incorporates activity area, friend relationship characteristics, and content category. This dimensional expansion enables the system to capture credibility from multiple angles simultaneously, resolving the contradiction between simplicity and accuracy.
2Measurement precision
If multiple factors are used for reliability computation, then the accuracy of information extraction is improved, but the computational complexity increases
Solution Approach 1:
The computational process is segmented into distinct modules: activity area computation, friend relationship computation, and content category computation. Each module processes specific factors independently and contributes to the final reliability score. This segmentation allows the system to handle multiple factors systematically without overwhelming computational complexity, as each factor is processed through dedicated computational pathways.
3Reliability
If contribution information is filtered by reliability threshold, then the quality of output information is improved, but the quantity of available information decreases
Solution Approach 1:
The filtering mechanism applies different reliability thresholds and evaluation criteria to different types of contribution information based on their content categories. Rather than applying a uniform filter, the system evaluates each contribution's reliability locally according to its specific characteristics and context, allowing high-quality information to be identified while preserving relevant information that may not meet absolute thresholds but remains useful for specific purposes.
Data Source
AI summary
In order to extract, with high accuracy, useful information from contribution information of social media, the present invention provides a processing apparatus 10 including: an acquisition unit 11 that acquires contribution information relating to a target area; a computation unit 12 that computes a degree of reliability of the contribution information, based on at least two of an activity area of a contributor of the contribution information, a degree of reliability of an estimation result of the activity area, a degree of disclosure of a profile of the contributor, an attribute of the contributor, an attribute of the activity area for the contributor, and a category of a content of the contribution information; and an output unit 13 that outputs the contribution information in which a degree of reliability of the contribution information is higher than a threshold value.