Social Media Influencer Identification via Activity Pattern Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing social TV technologies lack the ability to effectively identify and quantify the influence of individual users on their peer groups' media viewing choices in real-time, limiting personalized interactions and targeted content recommendations.
Innovation Solution
A system and method that monitors user activity patterns to identify trendsetting individuals by capturing media content selection requests from various devices and analyzing them to determine cumulative influence scores, which are then used to rank users within a social group.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If social TV technologies enable real-time interaction and sharing of viewing preferences among peer groups, then user engagement and interaction quality are improved, but the ability to identify and quantify individual user influence remains insufficient
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring user activity patterns and providing real-time influence scores to users and advertisers. The server analyzes viewing behavior data and feeds back influence metrics that enable targeted interactions and advertising, resolving the contradiction by providing both interaction capability and measurement precision through a closed-loop system.
Solution Approach 2:
The patent replaces manual or simple interaction mechanisms with an automated electronic system that uses servers, databases, and algorithms to track and measure user influence. This substitution enables precise quantification of influence through digital data analysis, maintaining ease of interaction while achieving accurate measurement through technological automation.
2Measurement precision
If the system monitors and analyzes user activity patterns to identify trendsetting individuals, then influence identification accuracy is improved, but data processing complexity and computational resources increase
Solution Approach 1:
The system segments the complex task of influence analysis into distinct modular components: data collection modules that gather viewing patterns, analysis modules that process the data, and scoring modules that generate influence metrics. This segmentation reduces overall system complexity by making each component independent and manageable while maintaining high measurement precision through specialized processing at each stage.
Solution Approach 2:
The patent introduces intermediary elements such as standardized data structures, protocol layers, and buffer systems between data collection and analysis components. These intermediaries simplify the interface between complex modules, enabling accurate influence identification while reducing the perceived complexity through standardized communication protocols and abstraction layers.
3Measurement precision
If the system captures and analyzes media content selection requests from multiple devices, then user behavior tracking accuracy is improved, but implementation complexity across different platforms increases
Solution Approach 1:
The system implements universal data collection protocols that can operate across multiple device types and platforms (smart TVs, mobile devices, set-top boxes). The server architecture is designed to handle diverse input formats and normalize them into a common data structure, enabling accurate viewing behavior tracking across all platforms without requiring platform-specific implementations, thus achieving both precision and versatility.
Data Source
AI summary
Data indicative of first and second members of a social group, a media content accessed by the first and second members of the social group, and a time at which the media content was accessed by the first and second members of the social group is collected. A system then compares the received data to determine if the second member of the social group accessed the same media content as the first member of the social group and if the second member of the social group accessed the same media content within a predetermined amount of time. When the second member of the social group accessed the same media content as the first member of the social group within the predetermined amount of time the first member of the social group is identified as a social influencer.