Automated Virtual Network Analysis for Influencer Prioritization
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Solution Overview
Problem
Existing methods fail to efficiently identify and prioritize lead and influencer accounts within virtual networks on social media platforms, leading to ineffective targeting and engagement strategies.
Innovation Solution
An automated system and method that creates seed data by determining friends and friends of friends of an online persona, generating edges and nodes lists, and applying social network analysis metrics to identify lead and influencer accounts in priority order, using custom metrics and dictionaries to filter and prioritize accounts based on influence scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to identify lead and influencer accounts in virtual networks, then analysis depth can be achieved, but time consumption and manual errors increase significantly
Solution Approach 1:
The patent replaces manual mechanical analysis methods with automated computer-based systems that use social network analysis algorithms, metrics calculation, and automated filtering to identify lead and influencer accounts, thereby eliminating manual errors and significantly reducing time consumption while maintaining or improving identification accuracy
Solution Approach 2:
The patent introduces automated software systems and algorithms as intermediaries between the raw social network data and the identification process, using programmed metrics and filtering mechanisms to objectively analyze network structures and identify key accounts without human intervention
2Quantity of substance
If comprehensive network analysis is performed to identify all potential leads and influencers, then coverage is improved, but system complexity and computational resources increase
Solution Approach 1:
The patent segments the comprehensive network analysis into distinct modular components including edges list creation, nodes list creation, metrics calculation modules, and filtering stages, allowing the system to process large networks systematically while managing complexity through organized, reusable components
Solution Approach 2:
The patent uses adjustable parameters and thresholds in the filtering process to control the scope and depth of analysis, allowing the system to adapt to different network sizes and analysis requirements without requiring complete redesign, thereby managing computational resources efficiently
3Productivity
If automated systems are implemented for network analysis, then productivity increases, but initial setup complexity and data processing requirements increase
Solution Approach 1:
The patent performs preliminary actions by automatically creating structured edges lists and nodes lists from raw data before the actual analysis begins, preparing the data in advance with proper formatting and relationships established, which streamlines subsequent processing and reduces setup complexity for ongoing analyses
Data Source
AI summary
Methods and systems that evaluate currently operating online personas is automated to establish the relationships between nodes and assign attributes. A virtual network exploitation (ViNE) protocol can create a prioritized list of every account in the extended network based on its influence score, as well as filtering, to create a subset of the influencer list of accounts that meet attribute criteria. Analysis of this data can identify the key accounts for the influencer and lead lists and provide recommendations on the path and strategy the client should use to most effectively engage the accounts of interest. Automated seed list generation for SNA can be operationalized to identify all of the existing leads within an extended social network in priority order and provide an influence score for each account. The system can be scaled to combine individual accounts that focus on a specific organization, personality or region.