Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
User behavior oriented web spam detection
Proceedings of the 17th international conference on World Wide Web
Exploring linguistic features for web spam detection: a preliminary study
AIRWeb '08 Proceedings of the 4th international workshop on Adversarial information retrieval on the web
Combining anchor text categorization and graph analysis for paid link detection
Proceedings of the 18th international conference on World wide web
Graph regularization methods for Web spam detection
Machine Learning
Web spam classification: a few features worth more
Proceedings of the 2011 Joint WICOW/AIRWeb Workshop on Web Quality
An overview of Web search evaluation methods
Computers and Electrical Engineering
Survey on web spam detection: principles and algorithms
ACM SIGKDD Explorations Newsletter
Hi-index | 0.00 |
Web spam has a negative impact on the search quality and users' satisfaction and forces search engines to waste resources to crawl, index, and rank it. Thus search engines are compelled to make significant efforts in order to fight web spam. Traffic from search engines plays a great role in online economics. It causes a tough competition for high positions in search results and increases the motivation of spammers to invent new spam techniques. At the same time, ranking algorithms become more complicated, as well as web spam detection methods. So, web spam constantly evolves which makes the problem of web spam detection always relevant and challenging. As the most popular search engine in Russia Yandex faces the problem of web spam and has some expertise in this matter. This article describes our experience in detection different types of web spam based on content, links, clicks, and user behavior. We also review aggressive advertising and fraud because they affect the user experience. Besides, we demonstrate the connection between classic web spam and modern social engineering approaches in fraud.