URL Entity Extraction for Spam Detection
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Solution Overview
Problem
Social networking systems face challenges in detecting and mitigating spam links, as existing methods often incorrectly flag legitimate entities as spammers, and spammers use shared hosting providers and multiple URLs to evade detection, leading to a decrease in user experience and increased traffic to illegitimate entities.
Innovation Solution
The system extracts identifiers from URLs and classifies them within a hierarchical structure, attributing URLs to sequences of identifiers to determine if they correspond to spammer entities by analyzing traffic data and setting thresholds for traffic percentage, thereby flagging specific sequences as spam without affecting legitimate URLs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing spam detection methods are used to identify spammer entities, then spam detection capability is improved, but false positives increase causing legitimate entities to be incorrectly flagged
Solution Approach 1:
The patent segments URL identifiers into hierarchical components (parent entity identifiers and child entity identifiers) and analyzes them separately. This segmentation allows the system to distinguish between legitimate traffic patterns and spam patterns more accurately, reducing false positives while maintaining spam detection capability
Solution Approach 2:
The patent introduces a new dimension of analysis by examining the hierarchical structure of URL identifiers and traffic distribution patterns across multiple levels (parent and child entities). This multi-dimensional approach enables more precise spam detection without incorrectly flagging legitimate entities
2Ease of operation
If spam detection is relaxed to reduce false positives, then user experience is improved, but traffic to illegitimate entities increases
Solution Approach 1:
The patent implements a feedback mechanism that continuously monitors traffic patterns and adjusts spam detection thresholds based on observed behavior. This allows the system to maintain high user experience quality by being lenient with legitimate traffic while still effectively blocking spam through adaptive threshold adjustment
Solution Approach 2:
The patent dynamically changes detection parameters (thresholds) based on traffic analysis results. By adjusting these parameters according to observed patterns, the system can reduce false positives and improve user experience while maintaining effective spam blocking capability
3Measurement precision
If hierarchical URL classification is implemented to improve spam detection, then entity identification accuracy is improved, but system complexity increases
Solution Approach 1:
The patent divides the URL analysis task into segmented hierarchical levels (parent entity and child entity identifiers). This segmentation simplifies the overall complexity by breaking down the classification problem into manageable, independent analysis steps at each hierarchical level
Solution Approach 2:
While the patent introduces hierarchical classification, it manages complexity by adding analysis dimensions rather than creating complex interdependent structures. The hierarchical approach allows independent analysis at each level, reducing overall system complexity despite increased analytical depth
Data Source
AI summary
To allow for detection of links to spam within a social networking system by identifying entities based on uniform resource locators (URLs). Identifiers that include at least one parent entity identifier representing a parent entity and at least one child entity identifier representing a child entity are extracted from URLs. An identifier sequence including the at least one parent entity identifier is designated as an attributable sequence when the child entity accounts for a value of traffic received by the parent entity that does not satisfy a threshold. In an embodiment, the child entity may be identified as an actionable target. The URLs may be classified within a hierarchical structure based on the identifiers. The hierarchical structure may comprise at least one parent node representing the parent entity and at least one child node representing the child entity. The hierarchical structure may be a tree.