P. Kolari, A. Java, T. Finin, T. Oates, and A. Joshi. proceedings of the 21st national conference on Artificial intelligence - Volume 2, page 1351--1356. AAAI Press, (2006)
Abstract
Weblogs or blogs are an important new way to publish information, engage in discussions, and form communities on the Internet. The Blogosphere has unfortunately been infected by several varieties of spam-like content. Blog search engines, for example, are inundated by posts from splogs - false blogs with machine generated or hijacked content whose sole purpose is to host ads or raise the PageRank of target sites. We discuss how SVM models based on local and link-based features can be used to detect splogs. We present an evaluation of learned models and their utility to blog search engines; systems that employ techniques differing from those of conventional web search engines.
%0 Conference Paper
%1 kolari2006detecting
%A Kolari, Pranam
%A Java, Akshay
%A Finin, Tim
%A Oates, Tim
%A Joshi, Anupam
%B proceedings of the 21st national conference on Artificial intelligence - Volume 2
%D 2006
%I AAAI Press
%K blogs features spam-detection svm
%P 1351--1356
%T Detecting spam blogs: a machine learning approach
%U http://dl.acm.org/citation.cfm?id=1597348.1597403
%X Weblogs or blogs are an important new way to publish information, engage in discussions, and form communities on the Internet. The Blogosphere has unfortunately been infected by several varieties of spam-like content. Blog search engines, for example, are inundated by posts from splogs - false blogs with machine generated or hijacked content whose sole purpose is to host ads or raise the PageRank of target sites. We discuss how SVM models based on local and link-based features can be used to detect splogs. We present an evaluation of learned models and their utility to blog search engines; systems that employ techniques differing from those of conventional web search engines.
%@ 978-1-57735-281-5
@inproceedings{kolari2006detecting,
abstract = {Weblogs or blogs are an important new way to publish information, engage in discussions, and form communities on the Internet. The Blogosphere has unfortunately been infected by several varieties of spam-like content. Blog search engines, for example, are inundated by posts from splogs - false blogs with machine generated or hijacked content whose sole purpose is to host ads or raise the PageRank of target sites. We discuss how SVM models based on local and link-based features can be used to detect splogs. We present an evaluation of learned models and their utility to blog search engines; systems that employ techniques differing from those of conventional web search engines.},
acmid = {1597403},
added-at = {2012-07-01T23:03:36.000+0200},
author = {Kolari, Pranam and Java, Akshay and Finin, Tim and Oates, Tim and Joshi, Anupam},
biburl = {https://www.bibsonomy.org/bibtex/2ac5378ab44c371de13d74bd44499793c/beate},
booktitle = {proceedings of the 21st national conference on Artificial intelligence - Volume 2},
description = {Detecting spam blogs},
interhash = {bd85d84dae8efbaf4abf4158af1b957a},
intrahash = {ac5378ab44c371de13d74bd44499793c},
isbn = {978-1-57735-281-5},
keywords = {blogs features spam-detection svm},
location = {Boston, Massachusetts},
numpages = {6},
pages = {1351--1356},
publisher = {AAAI Press},
series = {AAAI'06},
timestamp = {2012-07-01T23:03:36.000+0200},
title = {Detecting spam blogs: a machine learning approach},
url = {http://dl.acm.org/citation.cfm?id=1597348.1597403},
year = 2006
}