Linked Content Spam Detection Using Multimodal Feature Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing online services face challenges in efficiently identifying and mitigating spamming, malicious, and otherwise undesirable content linked to their hosted content items, as traditional methods struggle with accuracy and efficiency.
Innovation Solution
A system utilizing a spam detection engine that processes textual, media, and structural features of linked content pages through a multi-lingual distilled BERT model and a trained machine learning model, such as a deep neural network, to determine the presence of spamming, malicious, or undesirable content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spam detection methods are used, then the system is simple to implement, but the detection accuracy is low
Solution Approach 1:
The patent introduces multiple intermediary components including a feature extraction module that converts content into structured features, a BERT model that processes linguistic patterns, and a machine learning classifier that makes final spam determinations. These intermediaries transform the simple detection task into a multi-stage process that significantly improves accuracy while managing complexity through modular design
Solution Approach 2:
The patent replaces traditional mechanical spam filtering methods (keyword matching, rule-based systems) with advanced computational approaches including transformer-based language models and neural network classifiers. This substitution enables the system to understand contextual nuances, semantic relationships, and sophisticated spam patterns that mechanical rules cannot detect
2Measurement precision
If comprehensive content analysis is performed, then the detection accuracy improves, but the processing time increases
Solution Approach 1:
The patent segments the content analysis process into distinct modular stages: feature extraction (converting content to structured data), BERT model processing (analyzing linguistic patterns), and machine learning classification (making spam determinations). This segmentation allows each component to specialize in specific tasks, improving overall efficiency while maintaining comprehensive analysis capabilities
Solution Approach 2:
The patent performs preliminary feature extraction and text processing before the main classification step. By pre-processing content into structured features and extracting key linguistic patterns beforehand, the system reduces the computational burden during the final classification stage, thereby decreasing overall processing time while maintaining detection accuracy
3Measurement precision
If manual review of linked content is performed, then the detection accuracy is high, but the productivity is low
Solution Approach 1:
The patent implements a self-service automated detection system that independently analyzes linked content without requiring human intervention. The system autonomously extracts features, processes text through the BERT model, and generates spam classifications, thereby achieving both high accuracy and high productivity by eliminating the bottleneck of manual review while maintaining comprehensive analysis capabilities
Data Source
AI summary
Systems and methods for determining whether a linked content page may include spamming, malicious, and/or otherwise undesirable content. The linked content page may be crawled, scraped, and/or parsed to extract various information associated with the text, media items, and/or structure of the linked content page. The text, media, and/or structure information may be analyzed and processed to generate one or more textual features, media features, and/or structural features, which may then be processed by a trained machine learning model to determine whether the content page includes spamming, malicious, and/or otherwise undesirable content.