1. A Machine Learning Approach
Detecting Spam Blogs:
A Machine Learning Approach
Pranam Kolari, Akshay Java, Tim Finin, Tim Oates, Anupam Joshi
What are Spam Blogs (Splogs)?
Blogs hosting machine generated posts, each post contributing to web spam
Posts featuring content hijacked from other blogs and/or stuffed keywords
Posts with links interspersed between random text
Blogs with placement of context based ads only to fool users into clicking ads (See 3) 1
A Case of Content Plagiarism
(1) Original Post by Ebiquity
(2) Infiltration in Search Results
(3) A splog result 2 3
Why are Splogs a Problem?
Splogs undermine ranking algorithms(See 6) (Source: MSR Search Defender Report)
Splogs water down search results (See 6)
Splogs threaten the Web advertising model (See 3)
Splogs indulge in “plagiarism” (See 1,2,3)
Splogs skew results of social research tools (See 4)
Splogs stress the Blogosphere infrastructure of ping servers, blog search engines, etc. (See 4,5)
$197
“Holy Grail Of Advertising... “
“Easy Dominate Any Market, Any Search Engine, Any Keyword” 4
This Work
Formalizes the Splog Detection Problem
Supervised Machine Learning Technique
Training set of hand labeled examples – 700 each of positive and negative
Effectiveness of Specialized features
Local Models for Fast Splog Detection
Global link-based models effective for (delayed) Splog Detection
Precision/Recall of 87% for bag-of-words
See 5 6
Blog Features
We, what, was, my, org, flickr, paper, words, me, thank, go, archives
Splog Features
Find, info, news, website, best, articles, perfect, Products, uncategorized, hot, Resources, inc, copyright
Partially supported by NSF award ITR-IIS and ITR-IDM and IBM