Webcrawler Content Key Generation for Duplicate Detection
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Solution Overview
Problem
The internet is plagued with spam and duplicated content, making it challenging for webcrawling systems to distinguish between original and aggregated online content, which is often copied from legitimate sources without proper attribution.
Innovation Solution
A computer-implemented webcrawling system that analyzes RSS feeds and generates content keys using the SHA-1 algorithm to identify and differentiate between original and aggregated content by considering factors such as authorship, publication dates, and update frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If webcrawling systems collect all online content without filtering, then the quantity of collected data increases, but the quality of data decreases due to inclusion of spam and duplicated content
Solution Approach 1:
The system performs preliminary actions by generating content keys and creating a content database before final content collection. Webcrawlers use this pre-established database to identify and filter duplicate content in real-time, preventing low-quality data from being collected in the first place
Solution Approach 2:
The patent introduces content keys as an intermediary mechanism between the web content and the collection system. These keys serve as mediators that enable efficient comparison and identification of duplicate content without requiring direct analysis of the actual content, thus maintaining data quality while processing large quantities
2Measurement precision
If the system implements comprehensive content analysis to identify aggregated content, then the accuracy of detection improves, but the processing time increases
Solution Approach 1:
The system segments the content identification process into distinct components: generating content keys from URLs, comparing keys against the content database, and only analyzing full content when necessary. This segmentation allows rapid filtering of obvious duplicates through key comparison alone
Solution Approach 2:
The system performs partial analysis by using content keys as a preliminary filter before conducting full content analysis. This partial action approach identifies and eliminates duplicate content through lightweight key comparison, reserving more time-consuming full content analysis only for unique content items
3Reliability
If webcrawlers process every content item in detail, then the completeness of content analysis improves, but the computational resources required increase
Solution Approach 1:
The system performs preliminary content key generation and database creation before detailed content processing. This preliminary action establishes a framework that enables efficient filtering, ensuring that detailed computational analysis is only applied to content items that pass the initial key-based filtering stage
Solution Approach 2:
The patent extracts the essential identifying features of content into content keys, separating these from the full content. This extraction allows the system to perform lightweight key-based comparisons for all content items while reserving resource-intensive full content analysis only for items that require detailed processing
Data Source
AI summary
A computer-implemented method is presented herein. The method obtains a first content item from an online source, and then generates a characterizing signature of the first content item. The method continues by finding a previously-saved instance of the characterizing signature and retrieving data associated with a second content item (the second content item is characterized by the characterizing signature). The method continues by analyzing the data associated with the second content item, corresponding data associated with the first content item, and decision criteria. Thereafter, either the first content item or the second content item is identified as an original content item, based on the analyzing. The other content item can be flagged as an aggregated content item.