Social Media Analysis via External Data Correlation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Analyzing social media content with very small quantities of data is challenging due to the difficulty in determining the topic from brief messages, as existing techniques like hash tags are unreliable if not used consistently or contain errors.
Innovation Solution
Correlation analysis is performed by reviewing external data such as the message sender's past writings, contemporaneous messages, and location/time information to deduce the context of social media message snippets, even when they contain limited data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hash tags are used to provide context for social media messages, then the analysis accuracy of messages with small data quantities is improved, but the reliability decreases when users fail to use hash tags consistently or make typographical errors
Solution Approach 1:
The patent introduces an intermediary analysis system that uses correlation analysis between messages and external data sources to infer message context. This intermediary approach doesn't rely on user-provided hash tags but instead mediates between the limited message data and external knowledge bases to determine topic classification, thereby achieving both accuracy and reliability
Solution Approach 2:
The system performs preliminary correlation analysis between message content and external data before final topic determination. By pre-establishing relationships between message patterns and external knowledge bases, the system can reliably infer message topics even when hash tags are absent or incorrect, improving both measurement precision and reliability
2Adaptability or versatility
If correlation analysis is performed by reviewing external data outside the message content, then the ability to analyze messages with very small data quantities is improved, but the device complexity increases
Solution Approach 1:
The analysis system is designed to handle multiple types of social media messages with varying data quantities using the same correlation analysis framework. The system universally applies external data correlation techniques across different message types (tweets, forum posts, blog comments) without requiring separate analysis methods for each message type, thereby achieving versatility without proportionally increasing complexity
Solution Approach 2:
The system dynamically adjusts the scope and depth of external data correlation based on message characteristics. For messages with very small data quantities, the system correlates with broader external data sets, while for messages with more content, it uses more specific correlations. This parameter adaptation allows the system to maintain high adaptability to different message types while managing computational complexity through selective data correlation
Data Source
AI summary
Disclosed is an improved method, system, and computer program product for analyzing social media content. Correlation analysis is used to analyze the social media data snippets. The correlation analysis is performed by reviewing other items of data that are outside of the message content itself. The present approach can advantageously be used to analyze and understand the content of social media message even where only very small quantities of data are provided within each message posting.