QQ客服 官方微博 反馈留言

	Let web spammers expose themselves
	Cheng, Zhicong ; Gao, Bin ; Sun, Congkai ; Jiang, Yanbing ; Liu, Tie-Yan
	2011
英文摘要	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. Copyright 2011 ACM.; EI; 0
语种	英语
DOI标识	10.1145/1935826.1935902
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/325822]
专题	软件与微电子学院
推荐引用方式 GB/T 7714	Cheng, Zhicong,Gao, Bin,Sun, Congkai,et al. Let web spammers expose themselves. 2011-01-01.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。