Visual Meme Tracking for Social Media Buzz Overload
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Solution Overview
Problem
Existing systems fail to effectively capture and analyze large amounts of video content for specific topics in social media, leading to 'buzz overload' and making it difficult to characterize repositories, track trends, and identify influential content and creators.
Innovation Solution
A system and method for analyzing visual memes, which involves identifying and tracking visual memes associated with topics, using a processor and memory to collect information on meme generation, propagation, and use, employing a dual model for network and collection analysis, and utilizing cross-modal-matching for annotation and trend determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional search engines and content aggregation services are used to capture video content, then the quantity of content captured increases, but the system becomes overwhelmed by buzz overload and cannot effectively characterize the repository
Solution Approach 1:
The patent extracts visual memes (distinctive visual patterns) from large volumes of video content, separating the essential characteristic elements from the overwhelming bulk of data. This allows the system to characterize repositories by tracking these extracted visual memes rather than processing all raw video content, thus managing complexity while maintaining comprehensive coverage.
Solution Approach 2:
The system transforms the problem from analyzing raw video content to analyzing visual meme propagation patterns. By changing the parameter of analysis from individual video files to visual meme instances and their propagation metadata, the system can effectively characterize large repositories without being overwhelmed by the volume of original content.
2Measurement precision
If visual meme tracking is implemented to characterize video repositories, then the ability to discover trends and identify influential content improves, but the computational complexity and processing requirements increase
Solution Approach 1:
The system creates simplified copies of video content in the form of visual memes and their propagation metadata. Instead of analyzing original high-complexity video files, the system works with these simplified representations that capture the essential propagation patterns, thereby achieving accurate trend discovery with reduced computational complexity.
3Loss of information
If comprehensive video content analysis is performed to identify buzz patterns, then the completeness of topic coverage improves, but the time required for analysis and the resources needed increase significantly
Solution Approach 1:
The system extracts visual memes and their propagation metadata from video content, obtaining the essential information needed for topic characterization without processing the complete video files. This extraction approach maintains information completeness for trend analysis while dramatically reducing the time and computational resources required.
Data Source
AI summary
A system and method for analyzing visual memes includes identifying visual memes associated with at least one topic in a data source. The visual memes propagated over time are tracked to extract information associated with identified visual memes. The information associated with the visual memes is analyzed to determine at least one of generation, propagation, and use of the identified memes.