Reconstructing Social Network Contact Lists via Intermediary Profile Crawling
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Solution Overview
Problem
Social network users' List-Of-Friends (LOF) information is often inaccessible due to privacy policies, making it difficult for Web intelligence systems to collect valuable user interaction data.
Innovation Solution
A method and system that reconstructs a target user's LOF by analyzing and crawling profiles of other social network users, using iterative techniques and heuristics to select potential contacts, and combining these with machine learning to optimize the selection process, thereby minimizing resource usage and exposure to throttling schemes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If social networks implement privacy policies to protect user data, then user privacy and security are improved, but accessibility of contact information for intelligence gathering deteriorates
Solution Approach 1:
The system uses social network profiles as intermediary sources to indirectly obtain contact information. Instead of directly accessing the target user's protected LOF, the system crawls and analyzes profiles of other users who may have the target user in their accessible LOFs, thus obtaining the desired information through an intermediary channel that bypasses privacy restrictions.
2Measurement precision
If iterative profile crawling is performed to reconstruct LOF, then contact identification accuracy is improved, but resource consumption and exposure to throttling increase
Solution Approach 1:
The system performs partial crawling by selectively analyzing only relevant profiles and stopping when termination conditions are met (e.g., when a sufficient number of contacts are identified or when resource limits are approached). This partial action approach achieves acceptable identification accuracy without exhaustively crawling all possible profiles, thus reducing resource consumption.
Solution Approach 2:
The system performs preliminary filtering and analysis to identify promising profiles before conducting full crawling operations. By pre-screening profiles based on available metadata and connection patterns, the system prioritizes which profiles to crawl in detail, reducing overall resource expenditure while maintaining identification accuracy.
3Productivity
If aggressive crawling strategies are used to quickly reconstruct LOF, then information gathering speed is improved, but detection by anti-scraping measures increases
Solution Approach 1:
The system employs periodic crawling strategies with variable intervals between requests, mimicking natural user browsing patterns. Instead of continuously aggressive crawling, the system pauses between operations, randomizes request timing, and alternates between different profiles, thereby maintaining information gathering productivity while avoiding detection by anti-scraping mechanisms that monitor for bot-like behavior.
4Loss of information
If comprehensive profile analysis is performed on all potential contacts, then reconstruction completeness is improved, but processing time and computational resources increase
Solution Approach 1:
The system segments the profile analysis process into multiple stages: initial filtering based on basic criteria, intermediate analysis of connection patterns, and detailed examination only of high-priority profiles. This segmentation allows the system to achieve comprehensive reconstruction by systematically processing profiles in stages rather than analyzing all profiles simultaneously, thus reducing processing time while maintaining completeness.
Data Source
AI summary
When using Web intelligence (“Webint”) to collect information regarding a target social network user, one of the most valuable pieces of information is the target user's List-Of-Friends (LOF). In some cases, however, the LOF of the target user is not accessible in his profile. Herein are described methods and systems for identifying the LOF of a target user. An analysis system crawls the profiles of social network users, other than the target user, and reconstructs the LOF of the target user from the crawled profiles.