Content Provenance Tracing via MinHash Clustering
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Solution Overview
Problem
The media industry faces challenges in identifying the origin and spread of news content due to widespread duplication and republishing, leading to misinformation, disinformation, and plagiarism, as existing methods are inefficient in tracing content provenance across large volumes of documents.
Innovation Solution
A system and method that utilizes similarity measurements and hashing algorithms, such as MinHash and Locality Sensitive Hashing, to cluster documents based on content features, identifying source documents and reconstructing content provenance by selecting representative documents within clusters, which are then presented to users through a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional content distribution methods are used, then content can be widely published, but it becomes difficult to identify the origin and spread of news content
Solution Approach 1:
The system performs preliminary hashing of content features and pre-computes similarity scores between documents before actual provenance tracing is needed. This allows the system to quickly identify content relationships when provenance information is requested, without having to analyze all documents from scratch each time.
Solution Approach 2:
The system introduces clustering algorithms as an intermediary mechanism that groups documents by content similarity. This clustering structure serves as a mediator between the vast document corpus and the provenance tracing function, making it feasible to identify content origins by first locating relevant document clusters.
2Measurement precision
If manual analysis of documents is performed, then content provenance can be traced, but it is inefficient for large volumes of documents
Solution Approach 1:
The system replaces manual mechanical analysis of documents with automated computational methods. Hashing algorithms compute content features programmatically, clustering algorithms automatically group documents by similarity, and the system systematically traces provenance through these computational structures rather than human review.
Solution Approach 2:
The system creates hash-based representations (copies) of document content features that can be efficiently stored and compared. Instead of analyzing full documents manually, the system works with these compact hash copies that preserve the essential content characteristics needed for provenance identification.
3Reliability
If all documents are analyzed in detail, then accurate content relationships can be identified, but the computational complexity increases significantly
Solution Approach 1:
The system segments the analysis process into distinct stages: first hashing documents to extract content features, then computing similarity scores between hash representations, and finally applying clustering algorithms to group documents. This segmentation allows each stage to operate on simplified data structures rather than full documents, reducing overall computational complexity.
Solution Approach 2:
The system computes similarity scores for all document pairs (excessive action) but then uses clustering to focus detailed analysis only on relevant groups. This partial action approach ensures thoroughness where needed while avoiding unnecessary computation for unrelated document comparisons.
Data Source
AI summary
Systems and methods for managing content provenance are provided. A network system accesses a plurality of documents. The plurality of documents is then hashed to identify one or more content features within each of the documents. In one embodiment, the hash is a MinHash. The network system compares the content features of each of the plurality of documents to determine a similarity score between each of the plurality of documents. In one embodiment, the similarly score is a Jaccard score. The network system then clusters the plurality of documents into one or more clusters based on the similarity score of each of the plurality of documents. In one embodiment, the clustering is performed using DBSCAN. DBSCAN can be iteratively performed with decreasing epsilon values to derive clusters of related but relatively dissimilar documents. The clustering information associated with the clusters are stored for use during runtime.