5ZN-9 A Topical Study on the Web Spam

スポンサーリンク

概要

• 論文の詳細を見る

• In this paper, we study the topical characteristic of spam hosts. To categorize spam hosts, we extract link spam structures from multiple time snapshots of Japanese Web archive using graph algorithms. Next, we define several spam topic categories and classify spam hosts in those structures into such spam topics using their uniform resource locator(URL)s and a machine learning approach. We analyze the spam topic distribution on the Web in different years and observe the change in spam topics through the time.

• 2010-03-08

著者

• Chung Young-joo

  東大

• TOYODA Masashi

  東大

• KITSUREGAWA Masaru

  東大

• Toyoda Masashi

  Institute Of Industrial Science The University Of Tokyo

• Kitsuregawa M

  Institute Of Industrial Science The University Of Tokyo

• Kitsuregawa Masaru

  Univ. Tokyo Tokyo Jpn

関連論文

• 5ZN-9 A Topical Study on the Web Spam
• Mining Communities on the Web Using a Max-Flow and a Site-Oriented Framework(Data Mining)
• Compact Encoding of the Web Graph Exploiting Various Power Distributions(Discrete Mathematics and Its Applications)
• Finding Neighbor Communities in the Web Using an Inter-Site Graph(Database)
• Join Strategies on Grid-Files
• Join Strategies on Multi-Dimensional C1ustered Relations
• Web Community Chart : A Tool for Navigating the Web and Observing Its Evolution
• Detecting Hijacked Sites by Web Spammer Using Link-Based Algorithms
• A Study of Link Farm Evolution Using a Time-series of Web Snapshots
• A Study of Link Farm Evolution Using a Time-series of Web Snapshots
• 3ZK-7 Study on the Structure and Behavior of Web Spam by Link Hijacking
• A Study on Characteristics of Topic-Specific Information Cascade in Twitter (データ工学)
• A Study on Characteristics of Topic-Specific Information Cascade in Twitter
• Semi-supervised Sentiment Classification in Resource-Scarce Language : A Comparative Study
• Semi-supervised Sentiment Classification in Resource-Scarce Language : A Comparative Study

もっと見る 閉じる

スポンサーリンク