Social Network Post Scoring via Machine Learning Quality Filtering
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Solution Overview
Problem
Social networking systems face challenges in efficiently identifying and presenting high-quality user posts related to trending topics due to the vast amount of non-uniform content from a large number of users, which requires advanced technical capabilities for sorting through massive data and providing relevant content to users.
Innovation Solution
The system employs machine-learning techniques to score posts based on quality features such as informative content, sentiment, relevance, and temporal freshness, and presents them in a commentary module, allowing for efficient identification and display of high-quality posts related to trending topics, reducing the need for multiple search queries and saving processing resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the system displays all user posts related to trending topics, then users can access comprehensive information, but the system becomes inefficient and overwhelming due to the vast amount of non-uniform content
Solution Approach 1:
The patent extracts only the most relevant and high-quality posts from the vast amount of user-generated content. By using machine learning models to identify and extract posts that are most likely to be of interest to users based on quality features, the system filters out irrelevant content while maintaining comprehensive coverage of trending topics.
Solution Approach 2:
The system changes the parameter of content selection from quantity-based to quality-based. Instead of displaying all posts, the system uses machine learning to evaluate and rank posts based on quality features such as relevance, engagement, and information value, thereby transforming the content delivery approach to be more efficient and targeted.
2Measurement precision
If the system uses machine learning to score and filter posts, then high-quality content can be efficiently identified, but the computational resources and processing complexity increase
Solution Approach 1:
The system performs preliminary scoring and filtering of posts using machine learning models before presenting them to users. By pre-evaluating content quality features and ranking posts in advance, the system reduces the computational burden during user interaction and can efficiently deliver high-quality content without requiring complex real-time processing.
Solution Approach 2:
The machine learning model acts as an intermediary between the raw user-generated content and the final content delivery. This intermediary component automatically assesses quality features and mediates the selection process, reducing the direct computational complexity of manually evaluating and filtering content while maintaining high measurement precision.
3Reliability
If the system requires multiple search queries to find relevant posts, then comprehensive search can be performed, but user time and processing resources are wasted
Solution Approach 1:
The system uses feedback from user interactions and content performance data to continuously improve the machine learning models. By learning from user behavior patterns and content engagement metrics, the system refines its ability to predict and prioritize relevant posts, reducing the need for multiple search queries while maintaining search completeness.
Solution Approach 2:
The system performs self-service by automatically learning and adapting to user preferences through machine learning. Instead of requiring users to perform multiple search queries, the system autonomously identifies and delivers relevant content based on learned patterns, saving user time while maintaining comprehensive search results through intelligent prediction.
Data Source
AI summary
In one embodiment, a method includes receiving a query associated with a trending topic selected by a user of an online social network from multiple trending topics and rewriting the query into a query command including multiple query constraints. The method also includes identifying one or more posts matching the query command, where each identified post has privacy settings making the post visible to all users of the online social network, and calculating, for each of the identified posts, a score for the post based on one or more post-quality features, where the score is calculated using a machine-learning model that assigns a particular weight to each of the one or more post-quality features. The method also includes sending to the user a commentary module including at least a portion of each of one or more of the identified posts having scores higher than a threshold score.