Evaluating text categorization
HLT '91 Proceedings of the workshop on Speech and Natural Language
Modern Information Retrieval
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Spam, damn spam, and statistics: using statistical analysis to locate spam web pages
Proceedings of the 7th International Workshop on the Web and Databases: colocated with ACM SIGMOD/PODS 2004
Identifying link farm spam pages
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Detecting phrase-level duplication on the world wide web
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Learning from labeled and unlabeled data on a directed graph
ICML '05 Proceedings of the 22nd international conference on Machine learning
Detecting spam web pages through content analysis
Proceedings of the 15th international conference on World Wide Web
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Looking into the past to better classify web spam
Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web
A study of link farm distribution and evolution using a time series of web snapshots
Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web
Beyond blacklists: learning to detect malicious web sites from suspicious URLs
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Uncovering social spammers: social honeypots + machine learning
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Survey on web spam detection: principles and algorithms
ACM SIGKDD Explorations Newsletter
Fighting against web spam: a novel propagation method based on click-through data
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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This paper is concerned with mining link spams (e.g., link farm and link exchange) from search engine optimization (SEO) forums. To provide quality services, it is critical for search engines to address web spam. Several techniques such as TrustRank, BadRank, and SpamRank have been proposed for this purpose. Most of these methods try to downgrade the effects of the spam websites by identifying specific link patterns of them. However, spam websites have appeared to be more and more similar to normal or even good websites in their link structures, by reforming their spam techniques. As a result, it is very challenging to automatically detect link spams from the Web graph. In this paper, we propose a different approach, which detects link spams by looking at how web spammers make link spam happen. We find that web spammers usually ally with each other, and SEO forum is one of the major means for them to form the alliance. We therefore propose mining suspicious link spams directly from the posts in the SEO forums. However, the task is non-trivial because there are also other information and even noises contained in these posts, in addition to useful clues of link spam. To tackle the challenges, we first extract all the URLs contained in the posts of the SEO forums. Second, we extract features for the URLs from their relationships with forum users (potential spammers) and from their link structure in the web graph. Third, we build a semi-supervised learning framework to calculate the spam scores for the URLs, which encodes several heuristics such as spam websites usually linking to each other, and good websites seldom linking to spam websites. We tested our approach on seven major SEO forums. A lot of spam websites were identified, a significant proportion of which cannot be detected by conventional anti-spam methods. It indicates that the proposed approach can be a good complement of existing anti-spam techniques.