Machine-Assisted Publisher Classification for Content Review
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Solution Overview
Problem
Manual review of content by human operators for appropriateness is slow, costly, and inefficient, especially when dealing with large volumes of content that need to be reviewed in a timely manner for copyright and offensive material violations.
Innovation Solution
Machine-assisted publisher classification using a classifier model trained on data from already classified publishers to determine whether a publisher is likely to submit appropriate or inappropriate content, by identifying common traits and patterns among publishers, and calculating a confidence score for classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review by human operators is used, then content can be reviewed for appropriateness, but the process becomes slow and inefficient
Solution Approach 1:
The patent introduces a machine learning classifier as an intermediary between content publishers and human operators. The classifier automatically analyzes publisher data and content metadata to generate preliminary classifications, serving as a mediator that handles the bulk of review work while human operators only intervene when needed, thus resolving the contradiction between maintaining review accuracy and improving processing speed
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated machine learning system. The classifier model processes publisher data and content metadata through computational algorithms, substituting human manual inspection with automated mechanical processing that operates faster while maintaining consistent evaluation criteria, thereby improving productivity without sacrificing measurement precision
2Measurement precision
If manual review by human operators is used, then content can be reviewed for appropriateness, but the cost increases
Solution Approach 1:
The machine learning classifier acts as a cost-effective intermediary that handles the majority of content review tasks automatically. By filtering and classifying content before human operators see it, the system reduces the number of expensive manual review hours required while maintaining accurate identification of problematic content, thus reducing overall review costs without compromising accuracy
Solution Approach 2:
The system enables self-service classification where the machine learning model autonomously evaluates publisher data and content metadata without requiring human intervention for every item. This automated self-assessment capability eliminates the need for costly manual review of clearly appropriate or clearly inappropriate content, reducing energy loss while preserving accurate measurement through the classifier's evaluation
3Measurement precision
If large volumes of content are reviewed manually, then all content can be checked, but the time required increases significantly
Solution Approach 1:
The patent segments the content review process into multiple stages: automated classification of publisher data and content metadata by the machine learning model, followed by selective human review only for borderline cases. This segmentation allows complete review of large volumes of content to be achieved efficiently, as the automated system handles the bulk while human operators focus on specific cases, reducing overall review time while maintaining completeness
Solution Approach 2:
The machine learning classifier performs preliminary action by automatically analyzing and classifying content before it reaches human operators. This preliminary classification identifies clearly appropriate content that can be approved automatically and clearly inappropriate content that requires immediate attention, allowing human operators to focus their time on ambiguous cases and ensuring complete review of large volumes without significant time loss
Data Source
AI summary
One or more computing systems can implement a classifier to classify content publishers as being likely to provide appropriate content or as being likely to provide inappropriate content. The classifier can gather information from previously classified publishers. The information from the previously classified publishers can used to train the classifier. Based on the training, the classifier can learn about traits, characteristics, and/or behavioral patterns, etc., associated with publishers that have been previously classified as being good as well as publishers previously classified as being bad. The classifier can then process information about an unclassified publisher to determine a classification for the unclassified publisher, as being good (and likely to provide appropriate content) or bad (and likely to provide inappropriate content).