This document summarizes research characterizing spam blogs (splogs) and distinguishing them from authentic blogs. Key findings include: - Splogs make up a significant portion (20-75%) of blogs indexed by search engines and receiving ping updates. - Splogs are motivated by hosting ads and promoting affiliate links to increase page rank. They use automated tools to generate blogs at large scale. - Analysis of two blog datasets found splogs have shorter content, higher outbound link counts, and abnormal ping frequency patterns compared to authentic blogs. - Spam detection using local word models on blog content achieved 87% accuracy, but link-based and ping-based features provide additional signals. - A multi

1. Characterizing the Splogosphere Tim Finin http://ebiquity.umbc.edu/paper/html/id/299/ Pranam Kolari, Akshay Java and Tim Finin University of Maryland, Baltimore County 3 rd Annual Workshop on the Weblogging Ecosytem: Aggregation, Analysis and Dynamics 22 May 2006

4. Blogosphere/Splogosphere

6. Motivation: host ads

7. Motivation: index affiliates, promote pageRank

8. Spings from weblogs.com

9. Where do Splogs come from? “ Honestly, Do you think people who make $10k/month from adsense make blogs manually? Come on, they need to make them as fast as possible. Save Time = More Money! It's Common SENSE! How much money do you think you will save if you can increase your work pace by a hundred times? Think about it…” “ Discover The Amazing Stealth Traffic Secrets Insiders Use To Drive Thousands Of Targeted Visitors To Any Site They Desire!” “ Holy Grail Of Advertising... “ “ Easily Dominate Any Market, Any Search Engine, Any Keyword.” $ 197

11. Our splog bait was picked up and used by dozens of sploggers

13. Our feed is RSSjacked by at least one splogger

17. This Work By characterizing the splogosphere, we aim to achieve the following: (i) Get a handle on the seriousness of the problem, (ii) Develop new techniques for splog detection, and (iii) Recommend placement of splog filters on the blogging infrastructure. Characterization is based on comparing the nature of authentic blogs against splogs to identify discriminating features

20. Splogs vs. Blogs – Word Count blogs splogs blogs and splogs

21. Splogs vs. Blogs – In-degree http://www.engadget.com 1942 http://www.huffingtonpost.com/theblog 905 http://www.crooksandliars.com 637 http://blogs.guardian.co.uk/news 616 http://www.littlegreenfootballs.com/weblog 611 http://spaces.msn.com/members/pony-girl 505 http://spaces.msn.com/members/black-puss 505 http://spaces.msn.com/members/amputee-women 505 http://spaces.msn.com/members/free-stories 505 http://spaces.msn.com/members/first-time-girl 505 Top 5 Top 5

22. Splogs vs. Blogs – Out-degree http://www.xanga.com/home.aspx?user=hit_me_layoutz 273 http://www.xanga.com/home.aspx?user=i_jock_layouts 271 http://www.xanga.com/home.aspx?user=slp_layouts_slp 198 http://spaces.msn.com/members/cyrustse1986 193 http://www.xanga.com/home.aspx?user=layouts_n_codes2005 180 http://worldseriesofpokerchipscardguard.blogspot.com 898 http://rule-wsop.blogspot.com 898 http://worldseries-ofpoler.blogspot.com 898 http://qsopcom-1.blogspot.com 898 http://weopcom.blogspot.com 898 Top 5 Top 5

25. Ping times – Italian Blogs

26. Sping vs. Ping times

27. Spings vs. Pings: frequency blogs vs. their ping frequency follows a power law, but splogs vs. spings does not

31. Implications (2) – Filter Design Heuristics Spam Blog Filter Language Identifiers Spam Blog Detectors Blog Identifier 1 2 3 4 Authentic Blogs Spam Blogs IP Blacklists Supporting Info (OPTIONAL)

34. Questions?

35. Blogs – A Specialized Domain Update Pings Update Pings Ping Stream 1 2 Update Stream Fetch Content 3 4 1 2 3 4 ( )