Cross-Platform Social Identity Correlation via Feature Vectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for identifying social network accounts associated with the same entity across different platforms are inadequate due to variations in measurable characteristics across social networks, failing to account for distinct subcategories.
Innovation Solution
A method involving data processing hardware that queries social networks for user account data, stores it in a social graph structure, determines derived characteristics, generates feature vectors, and compares them to identify accounts associated with the same entity by satisfying a predetermined condition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional photo matching and name similarity methods are used to correlate user accounts, then the correlation process is simple, but the accuracy is insufficient due to variations in measurable characteristics across different social network subcategories
Solution Approach 1:
The patent transforms diverse social network data from different subcategories into a unified parameter space using feature vectors. Each user account is represented by standardized features (photo embeddings, name embeddings, posting patterns, etc.) that can be compared across platforms. This parameter transformation enables accurate correlation by converting heterogeneous data into homogeneous comparable metrics.
Solution Approach 2:
The patent introduces feature vectors as an intermediary representation layer between raw social network data and correlation results. Instead of directly comparing raw data from different networks, the system converts all data into feature vectors that capture essential characteristics in a standardized format, enabling accurate cross-platform correlation.
2Ease of operation
If social networks are treated as uniform platforms, then data collection is simplified, but the distinct characteristics of different network subcategories are lost leading to inaccurate correlations
Solution Approach 1:
The patent applies local quality by extracting features specific to each social network subcategory while maintaining a unified comparison framework. Different networks contribute their unique characteristics through specialized features (e.g., Instagram photo patterns, Twitter posting frequencies), and these local qualities are preserved in the feature vectors for accurate correlation.
Solution Approach 2:
The patent segments the correlation problem into multiple independent feature dimensions (photo similarity, name similarity, posting patterns, account creation timing). Each dimension is analyzed separately and then combined, allowing the system to handle diverse network characteristics while maintaining overall correlation accuracy.
3Adaptability or versatility
If multiple user accounts per individual are assumed, then the system can capture diverse online identities, but determining when accounts belong to the same individual becomes more difficult
Solution Approach 1:
The patent replaces manual account verification with automated machine learning-based feature vector comparison. The system uses algorithms to automatically analyze and compare multiple accounts across networks, substituting mechanical human judgment with computational analysis that can handle large volumes of data and identify patterns invisible to human observers.
Data Source
AI summary
A method for analyzing social network accounts includes querying a first social network for data about a first user account associated with the first social network. The received data is stored in a social graph structure and used to determine derived characteristics of the first user account. The derived characteristics are quantified and used to generate a first feature vector for the first user account. The first feature vector of the first user account is compared with a second feature vector of a second user account associated with a second social network different from the first social network. Based on the comparison of the first and second feature vectors, it is determined whether the first user account and the second user account are associated with a same entity.