Social Network Content Influence Measurement System
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Solution Overview
Problem
Current social network service (SNS) technologies lack a method to effectively evaluate the influence of content, which is crucial for marketing and product recommendation, as existing methods focus on influencer impact rather than content value.
Innovation Solution
A method and apparatus for dynamically measuring influence on a social network by collecting SNS data, calculating user and attribute influence values, and curating content based on product influence values, using image tagging and deep learning to extract attributes and normalize influence values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods focus on influencer impact calculation, then advertisement pricing can be determined, but content value evaluation is not achieved
Solution Approach 1:
The evaluation system is segmented into multiple independent modules: user influence index measurement module, attribute influence value measurement module, and product influence value measurement module. Each module handles a specific aspect of content evaluation, making the complex task manageable and measurable.
Solution Approach 2:
Image tagging technology is introduced as an intermediary to bridge the gap between raw image data and meaningful attribute extraction. The tagging system converts visual information into structured attributes that can be quantified and evaluated for influence.
2Loss of information
If image tagging and attribute extraction are applied, then content attributes can be identified, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing images through tagging and pre-extracting attributes before the actual evaluation process. This preparation work is done in advance, so that when evaluation is needed, the attribute data is already available, reducing real-time processing requirements.
Solution Approach 2:
The system applies partial action by selectively extracting only the most relevant attributes for influence evaluation rather than analyzing every possible image feature. This focused approach reduces computational overhead while maintaining evaluation accuracy.
3Measurement precision
If user influence index and attribute influence value are measured, then product influence can be calculated, but data collection and processing complexity increases
Solution Approach 1:
The system merges multiple measurement dimensions (user influence, attribute influence, content engagement) into a unified product influence value. By combining these different data streams through a standardized calculation framework, the system achieves comprehensive evaluation without proportionally increasing processing complexity.
Solution Approach 2:
The system transforms raw data parameters into normalized influence indices through parameter changes. User account information and content information are converted into a standardized user influence index, and image attributes are transformed into attribute influence values, making them comparable and combinable.
Data Source
AI summary
A method for measuring influence on a social network is provided. The method includes collecting social network service (SNS) data from an SNS, measuring a user influence index based on user account information among the SNS data and content information associated with the user account information, measuring an attribute influence value for an attribute included in image information based on the image information among the SNS data, measuring a product influence value for product information based on the user influence coefficient and the attribute influence value with respect to content including the product information, and curating the content including the product information based on the product influence value.