Pre-moderation Service for Web Store Content Compliance
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Solution Overview
Problem
Conventional manual moderation services for online content take days or weeks to review and may incorrectly identify non-compliant content, leading to frustration and delayed product listings on web stores.
Innovation Solution
A pre-moderation feature using recognition algorithms like OCR and image recognition to identify non-compliant content before publication, generating warnings and allowing users to correct issues, with automated compliance checks and dynamic blacklist updates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual moderation service is used to review submitted content, then content compliance can be verified, but the review process takes days or weeks and may make mistakes
Solution Approach 1:
The system performs preliminary automated compliance checks using recognition algorithms (OCR, image recognition, natural language processing) before content is submitted for manual review. This pre-screening identifies obviously compliant or non-compliant content, allowing manual moderators to focus only on borderline cases, thus reducing overall review time while maintaining accuracy
Solution Approach 2:
An automated moderation system acts as an intermediary between content submission and manual review. This intermediary layer filters content using machine learning models and recognition algorithms, flagging only uncertain cases for human review, thereby eliminating the need for complete manual review of all content while preserving reliability
2Reliability
If manual moderation service is used, then content can be reviewed for policy violations, but moderators may mislabel content or apply policies incorrectly
Solution Approach 1:
The moderation system is segmented into multiple specialized modules: OCR text extraction, image recognition, natural language processing, policy rule engines, and machine learning classifiers. Each module handles specific aspects of compliance checking, distributing the complexity across independent components that can be maintained and updated separately, reducing overall system complexity while improving reliability
Solution Approach 2:
The system implements feedback loops where automated moderation decisions are continuously evaluated against manual review outcomes and user appeals. Machine learning models are retrained on this feedback data, progressively improving policy application accuracy. The system also provides feedback to users about why content was flagged, allowing them to understand and correct issues
3Productivity
If automated recognition algorithms are used for pre-moderation, then review speed increases, but false positives may occur
Solution Approach 1:
The automated system applies multiple layers of recognition algorithms (OCR, image recognition, NLP) and confidence thresholding. Content is only flagged for manual review when the automated system's confidence is below a high threshold, meaning most clearly compliant content passes through automatically while potentially flagging some borderline cases for additional review, balancing speed and accuracy
Data Source
AI summary
Techniques for providing a pre-moderation feature for content that is submitted for a web page are described herein. For example, data may be received for a content submission of a web site prior to the content being incorporated into the web site. Text and images included in the data may be extracted based on a recognition algorithm. A determination that at least one of the extracted text or the extracted images include non-compliant content may be made based on compliance policies. In embodiments, a warning may be generated that corresponds to non-compliant text or non-compliant images included in the extracted text or images. The warning may be transmitted to a user device associated with the content submission.