Topic Detection via Segmented Content Batching
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Solution Overview
Problem
Social networking systems lack the ability to effectively identify and analyze key topics that are both highly relevant and widely discussed among users, which hinders targeted advertising and user engagement.
Innovation Solution
The system identifies trending topics by analyzing content objects across a social network, determining co-occurring topics, and filtering them to detect key topics based on a threshold number of associated content objects, providing a historical analysis of popular topics for specific user groups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system analyzes all content objects to identify trending topics, then the completeness of topic identification is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent segments the content objects into multiple batches and processes them in stages. First, it identifies trending topics from a initial set of content objects, then uses those trends to filter and analyze additional content objects. This segmentation allows the system to maintain high topic identification accuracy while reducing the computational complexity of analyzing all content at once.
Solution Approach 2:
The system performs preliminary analysis by first identifying trending topics from a subset of content objects before using those results to filter and analyze the remaining content objects. This preliminary action enables the system to leverage identified trends to efficiently process additional content, reducing overall processing complexity while maintaining comprehensive topic identification.
2Reliability
If the system filters topics by threshold number of content objects, then the relevance of key topics is improved, but the quantity of identified topics decreases
Solution Approach 1:
The patent applies different filtering criteria to different sets of content objects based on their relevance to identified trends. Content objects are processed in batches, and the threshold for identifying key topics is dynamically adjusted based on the local characteristics of each batch and its relationship to identified trends. This allows the system to maintain high topic relevance while identifying a sufficient quantity of topics.
Solution Approach 2:
The system dynamically adjusts the threshold parameter for identifying key topics based on the analysis results from previous batches and the identified trends. By changing the threshold parameter adaptively rather than using a fixed value, the system can maintain reliable topic relevance while ensuring enough topics are identified for effective advertising and engagement.
Data Source
AI summary
A method includes accessing a first set of content objects of an online social network posted within a predetermined timeframe, and identifying trending topics. Each object may be associated with one or more topics. The method further includes accessing a second set of content objects, and identifying a plurality of co-occurring topics associated with the second set. Each content object of the second set is associated with at least one of the identified trending topics. Then, the method includes accessing a third set of content objects, and analyzing the plurality of trending topics and co-occurring topics to detect key topics. Each content object of the third set of content objects may be associated with at least one of the identified co-occurring topics or trending topics. The key topics may be detected based on determining the co-occurring topics or trending topics that are associated with a threshold number of content objects.