Two-Model AI Moderation for Group-Relevant Content
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Solution Overview
Problem
Managing online communities that allow user-generated content submissions is challenging due to the need to balance member engagement with the prevention of spam, self-promotion, and off-topic posts, while manual moderation is arduous and time-consuming, leading to a decline in community contributions.
Innovation Solution
A computer system using two distinct machine-learning models to automate content moderation, one for identifying spam and the other for determining relevance to a group, combined with user feedback to adjust thresholds, ensuring efficient and personalized content recommendation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual moderation of user-generated posts is performed, then community content quality is maintained, but administrative time and effort increase significantly
Solution Approach 1:
The patent introduces an AI moderation system as an intermediary between users and administrators. The system automatically analyzes posts using machine learning models to detect spam, self-promotion, and off-topic content, filtering harmful content before it reaches administrators. This intermediary layer maintains content quality while significantly reducing the time administrators need to spend on manual moderation.
Solution Approach 2:
The patent replaces the mechanical manual moderation process with an automated AI-based system. Instead of administrators manually reviewing each post, the system uses trained machine learning models to automatically classify and filter content. This substitution maintains reliability in content quality assessment while eliminating the time-consuming manual review process.
2Object-affected harmful factors
If active moderation is implemented, then spam and off-topic posts are reduced, but community member engagement decreases due to stricter content filtering
Solution Approach 1:
The patent applies different moderation criteria to different types of content and different user groups. The AI system analyzes the specific context, user history, and content characteristics to apply appropriate moderation standards. This localized approach allows the system to reduce harmful content while maintaining engagement by being less restrictive on legitimate contributions, thus balancing spam reduction with member engagement.
Solution Approach 2:
The patent dynamically adjusts moderation parameters based on community context, user behavior patterns, and content characteristics. The AI system learns from community-specific patterns and adapts its filtering thresholds accordingly. This allows the system to effectively reduce spam while maintaining high engagement by adjusting the strictness of moderation parameters to match the community's needs and norms.
3Device complexity
If a single machine learning model is used for content moderation, then system complexity is reduced, but accuracy in detecting spam and determining relevance decreases
Solution Approach 1:
The patent divides the content moderation task into separate specialized models: one model focuses on detecting spam and harmful content, while another model focuses on determining relevance to the group. Each model is trained on specific data and optimized for its particular function. This segmentation allows each model to achieve higher precision in its specialized domain compared to a single general-purpose model, while the overall system complexity remains manageable through modular architecture.
Data Source
AI summary
A system and method for AI assisted content administration system is described. In one aspect, a computer-implemented method includes accessing group content submissions to an online community platform, computing, using a suggestion retrieval system, a group-to-post relevance score for each post from the group content submissions, identifying, using a first machine learning model of the suggestion retrieval system, a set of recommended posts from the group content submissions having the group-to-post relevance score that at least reaches a group-to-post relevance score threshold for a group, classifying at least one post from the set of recommended posts as relevant or non-relevant to the group using a second machine learning model of the intent-based ranking system, computing a relevance ranking score of at least one post from the set of recommended posts classified as relevant to the group, and identifying a set of suggested posts.