Automated Social Connection Labeling via Multi-Source Data Analysis
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Solution Overview
Problem
Users managing multiple social networks face challenges in accurately labeling and understanding the nature of their social connections across different platforms, as existing systems often fail to provide comprehensive and nuanced labeling based on diverse data sources.
Innovation Solution
A method and system that retrieve data from multiple social networking websites to automatically label social connections by analyzing profile, interest, and interaction data, clustering connections, and determining connection strength, enabling rich and accurate labeling of relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated labeling systems use data from multiple social networking websites, then the comprehensiveness and accuracy of connection labels improve, but the system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex task of social connection labeling into distinct processing stages: data retrieval from multiple sources, data cleaning and normalization, feature extraction from profiles and interactions, connection strength calculation, and label assignment. This segmentation allows each component to be optimized independently while maintaining overall system accuracy.
Solution Approach 2:
The patent introduces intermediary components including a centralized data repository that standardizes data from multiple social networks, feature extraction modules that transform diverse data into comparable metrics, and a connection strength calculator that synthesizes multiple factors into a unified measure. These intermediaries bridge the gap between heterogeneous data sources and the labeling function.
2Loss of information
If the system analyzes extensive user data including profiles, interests, and interactions across multiple platforms, then the richness of connection labels improves, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing user profile data, interest data, and interaction data in a standardized format before labeling is needed. Data from multiple social networks is retrieved, cleaned, and normalized in advance, creating a ready-to-use data repository that can be quickly queried when connection labels need to be generated or updated.
Solution Approach 2:
The patent applies local quality by selectively processing and analyzing only the most relevant data features for each specific connection type. Different weighting schemes are applied to different data sources based on their relevance to specific connection contexts, allowing the system to focus computational resources on the most informative features rather than uniformly processing all available data.
3Adaptability or versatility
If the system implements multiple labeling dimensions including mutual interests, interaction patterns, and connection strength, then the usefulness of the labeling system improves, but the complexity of label generation and management increases
Solution Approach 1:
The system implements a universal labeling framework that simultaneously generates multiple types of labels (connection strength, mutual interests, interaction patterns, relationship type) using a single integrated processing pipeline. The same data repository and analysis engine serve multiple labeling functions, reducing redundancy while maintaining versatility across different labeling dimensions.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting the weighting and thresholds of different labeling criteria based on connection context and user preferences. The system can modify label generation parameters to emphasize different aspects (e.g., interaction frequency vs. mutual interests) depending on the specific labeling task, providing versatility without requiring separate systems for each label type.
Data Source
AI summary
First data relating to a first user and second data relating to a second user are retrieved from a plurality of sources. A social connection is identified, by a computing device, between the first user and the second user using the first data and the second data. A label that describes the social connection is identified, by the computing device, using the first data and the second data. A first profile relating to the first user and a second profile relating to the second user is updated by the computing device to reflect the social connection and the label for the social connection.