Automated Content Correlation for Credibility Evaluation
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Solution Overview
Problem
Evaluating an individual's credibility and position on a topic over time is challenging due to the vast volume of statements made by public figures across various content sources, requiring efficient collection, processing, and comparison of metadata from multiple sources.
Innovation Solution
A computer-implemented method that receives and extracts metadata from content objects, stores records of statements made by individuals regarding specific topics, identifies correlated content by comparing metadata, and generates a correlated content object for easy comparison, including a credibility or consistency score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual collection and comparison of statements from multiple content sources is performed, then evaluation of individual credibility and position over time is possible, but the process becomes inefficient and resource-intensive
Solution Approach 1:
The patent replaces manual mechanical processes of collecting, storing, and comparing statements with an automated computer-implemented system. The computing device automatically receives content objects from multiple sources, extracts metadata, stores records in a repository, and compares statements across different time periods, eliminating the need for manual evaluation while maintaining high accuracy.
Solution Approach 2:
The system creates structured copies of statements from various content sources by extracting metadata and storing them as standardized records in a repository. This allows efficient duplication and comparison of statements without handling the original diverse formats, enabling rapid analysis of an individual's positions over time.
2Loss of information
If comprehensive metadata is extracted and stored from all content sources, then complete comparison of statements is achieved, but computer resource consumption increases
Solution Approach 1:
The system extracts only the essential metadata from content objects that is necessary for comparison purposes, such as statement content, timestamp, and source information. This selective extraction maintains information completeness for credibility evaluation while avoiding storage and processing of redundant or irrelevant data, thus reducing computer resource consumption.
Solution Approach 2:
The patent divides the comprehensive metadata into structured records with specific fields (statement content, date, source, etc.) stored in a repository. This segmentation allows the system to efficiently query and compare only relevant portions of the data when evaluating statements, reducing overall resource consumption while maintaining complete information availability.
3Adaptability or versatility
If multiple content sources are monitored for statements, then comprehensive coverage of individual positions is achieved, but system complexity increases
Solution Approach 1:
The computing device is designed with universal functionality to receive and process content objects from multiple different content sources (newspapers, magazines, electronic repositories, etc.) through a standardized interface. This multi-functionality allows comprehensive coverage of various sources without requiring separate specialized systems for each source, thereby managing complexity while maintaining versatility.
Solution Approach 2:
The system applies homogeneous processing methods and standardized metadata extraction across all diverse content sources. By treating different sources uniformly through the same computational framework and storage structure, the system achieves comprehensive coverage without proportionally increasing complexity, as the same processes handle all sources.
Data Source
AI summary
A computer-implemented method includes: receiving, by a computing device, a plurality of content objects from one or more computer content source devices; extracting, by the computing device, metadata from the plurality of content objects; storing, by the computing device, a plurality of records having the extracted metadata in a repository, wherein each record identifies a time in which a statement was made by an individual regarding a topic; identifying, by the computing device, correlated content between the plurality of content objects based on comparing the metadata in the records, wherein the correlated content includes a plurality of statements made by the individual regarding the topic at different periods of time; generating, by the computing device, a correlated content object having the correlated content; and providing, by the computing device, the correlated content object to a user device.