Utilizing improved Bayesian algorithm to identify blog comment spam

Author

Aiwu, Li ; Hongying, Liu

Author_Institution

Dept. of Comput. Sci., Guangdong Vocational Coll. of Posts & Telecom, Guangzhou, China

fYear

2012

fDate

3-5 June 2012

Firstpage

423

Lastpage

426

Abstract

In this paper, according to the blog website team dealing with comment spam demand more and more, analyzed the traditional Bayesian algorithm based on statistical method of defects, pointed out the deficiency in practical application, improved the rough Bayesian algorithm, utilized a string of comments appear based on the garbage probability value, calculated the number of geometrical average algorithm. The experimental results show that the modified Bayesian classification algorithm can effectively improve the classification of spam effect, garbage afr, legal review comments afr and average afr has dropped substantially.

Keywords

Bayes methods; Web sites; pattern classification; security of data; statistical analysis; Bayesian classification algorithm; average AFR classification; blog Website team; blog comment spam identification; comment spam; defects statistical method; garbage AFR classification; garbage probability value; geometrical average algorithm; legal review comments AFR classification; spam effect classification; Algorithm design and analysis; Bayesian methods; Blogs; Classification algorithms; Libraries; Unsolicited electronic mail; Bayesian algorithm; blog comment spam; geometric mean algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

Robotics and Applications (ISRA), 2012 IEEE Symposium on

Conference_Location

Kuala Lumpur

Print_ISBN

978-1-4673-2205-8

Type

conf

DOI

10.1109/ISRA.2012.6219215

Filename

6219215