Web Document Revision Anomaly Detection System
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Solution Overview
Problem
Online content publication platforms face issues with maintaining the integrity of digital content as users can revise published web documents without changing their URIs, leading to anomalies that degrade navigation, accuracy, and overall platform performance.
Innovation Solution
A system and method for detecting anomalies in revisions to web documents by comparing individual elements such as images, text, and numerical values between the original and updated versions, generating anomaly scores, and preventing publication if scores exceed thresholds, with the option to refine thresholds using machine-learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users are allowed to revise published web documents while maintaining the same URI, then content can be updated and improved, but content integrity and accuracy degrade due to anomalous revisions
Solution Approach 1:
The system performs preliminary comparison of web document elements before allowing publication of revisions. By comparing text, images, and other elements against the original published document and evaluating similarity scores, the system proactively identifies anomalous revisions before they are published, preventing integrity degradation while allowing legitimate updates
Solution Approach 2:
The system introduces an intermediary anomaly detection mechanism between the user revision submission and the final publication. This intermediary layer evaluates revision similarity scores and determines whether revisions are anomalous, acting as a mediator that allows legitimate content updates while blocking suspicious changes that would compromise content integrity
2Ease of operation
If revisions are allowed without restriction, then ease of content modification improves, but platform performance and navigation quality deteriorate
Solution Approach 1:
The system enables self-service content modification for users while automatically performing anomaly detection and evaluation. Users can freely submit revisions without manual approval, maintaining ease of operation, while the system autonomously evaluates similarity scores and blocks anomalous revisions, preserving platform performance without requiring human intervention for each revision
3Measurement precision
If anomaly detection is performed on all web document revisions, then content accuracy is maintained, but system complexity increases
Solution Approach 1:
The system segments the web document into distinct elements such as text, images, and other components for individual comparison and analysis. By evaluating each element separately and calculating similarity scores for each segment, the system achieves precise anomaly detection while maintaining manageable system complexity through modular processing
Data Source
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AI summary
Aspects of the present disclosure include a system comprising a machine-readable storage medium storing at least one program and computer-implemented methods for detecting anomalies in revisions to a web document. Consistent with some embodiments, a method includes publishing, at a network-based content publication platform, a web document comprising a plurality of distinct elements generated using data received from a computing device of a user. The method further includes accessing an updated web document that was generated based on modifications to the published web document made by the user. The method further includes generating one or more anomaly scores based on a comparison of the updated web document to the published web document, and determining whether to allow publication of the updated web document based on a result of a comparison of the anomaly score to a threshold anomaly score.