Multi-View Learning for Web Spam Detection

TL;DR

This paper proposes a multi-view learning approach for web spam detection that combines multiple feature-based classifiers to improve accuracy and scalability, achieving a 22% increase in AUC.

Contribution

It introduces a multi-view classification system that effectively integrates different feature sets for web spam detection, enhancing performance and efficiency.

Findings

Multi-view learning improves spam classification AUC by 22%.

The system achieves linear speedup with parallel execution.

Classifies web pages accurately using only HTML content.

Abstract

Spam pages are designed to maliciously appear among the top search results by excessive usage of popular terms. Therefore, spam pages should be removed using an effective and efficient spam detection system. Previous methods for web spam classification used several features from various information sources (page contents, web graph, access logs, etc.) to detect web spam. In this paper, we follow page-level classification approach to build fast and scalable spam filters. We show that each web page can be classified with satisfiable accuracy using only its own HTML content. In order to design a multi-view classification system, we used state-of-the-art spam classification methods with distinct feature sets (views) as the base classifiers. Then, a fusion model is learned to combine the output of the base classifiers and make final prediction. Results show that multi-view learning significantly improves the classification performance, namely AUC by 22%, while providing linear speedup for parallel execution.