Online Information Reliability Scoring via Pre-calculation
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Solution Overview
Problem
Existing methods for evaluating the reliability of online information are inefficient and not optimized for real-time updates, especially in the context of rapidly changing social network data.
Innovation Solution
The proposed solution involves pre-calculating reliability scores for a subset of multimedia documents and storing them in a database, allowing for quick retrieval and updating based on intrinsic and extrinsic criteria, including social network comments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If reliability scores are calculated on-demand for each document requested by a user, then measurement precision is improved, but loss of time and productivity deteriorate due to excessive calculation time and resource consumption
Solution Approach 1:
The system pre-calculates reliability scores for documents before they are requested by users. A scoring engine continuously calculates reliability scores for crawled documents and stores them in a database, so that when a user requests a document, the pre-calculated score is immediately available without requiring real-time computation.
Solution Approach 2:
The system segments the document processing into two independent stages: (1) background crawling and pre-scoring of documents, and (2) on-demand retrieval and display of documents with their pre-calculated scores. This segmentation allows the scoring process to occur independently without blocking user requests.
2Reliability
If reliability scores are updated continuously for all documents, then reliability is improved, but device complexity and loss of time worsen due to the huge number of data sources and continuous updates required
Solution Approach 1:
The system implements periodic updating of reliability scores rather than continuous real-time updates. The scoring engine operates in cycles, periodically recalculating scores for documents in the database based on new information from social networks and other sources, thereby maintaining reliability without requiring constant system activity.
Solution Approach 2:
The system automatically monitors and updates reliability scores without requiring manual intervention. The scoring engine continuously crawls sources, detects new information, and autonomously recalculates scores for affected documents, making the system self-maintaining and reducing operational complexity.
3Measurement precision
If all document sources are processed equally, then objectivity is improved, but productivity deteriorates due to lack of prioritization in processing the huge volume of data
Solution Approach 1:
The system applies different processing priorities to different document sources and types based on their local characteristics. High-priority sources (such as authoritative news outlets) and time-sensitive documents receive more frequent updates and faster processing, while lower-priority sources are processed at standard intervals, thereby optimizing overall system productivity while maintaining objective scoring through source-specific weightings.
Data Source
AI summary
The apparatus, systems and methods dynamically provide the reliability of multimedia documents by applying a series of intrinsic criteria and extrinsic criteria by pre-calculating a reliability score for at least a set of multimedia documents of at least one pre-selected source of multimedia documents, and by providing, in response to a request, the multimedia documents from the pre-selected sources associated with the score and the multimedia documents from the other sources associated with a score conditionally calculated.