Improving Web Spam Detection with Re-Extracted Features

摘要：

Web spam detection has become one of the top challenges for the Internet search industry. Instead of using some heuristic rules, we propose a feature re-extraction strategy to optimize the detection result. Based on the predicted spamicity obtained by the preliminary detection, through the host level web graph, three types of features are extracted. Experiments on WEBSPAM-UK2006 benchmark show that with this strategy, the performance of web spam detection can be improved evidently.

关键词： Link spam Content spam Web spam Machine learning.

作者: Guang-Gang Geng Chun-Heng Wang Qiu-Dan Li

作者单位: Institute of Automation Chinese Academy of Sciences Beijing 100080, P. R. China

会议类型: 国际会议

会议名称: 第十七届国际万维网大会(the 17th International World Wide Web Conference)(WWW08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-04-21（万方平台首次上网日期，不代表论文的发表时间）