SVMs for the Blogosphere: Blog Identification and Splog Detection

SVMs for the Blogosphere: Blog Identification and

Splog Detection

Pranam Kolari, Tim Finin, Anupam Joshi

Computational Approaches to Analyzing Weblogs, Stanford, March 27-29, 2006

http://ebiquity.umbc.edu

March 29, 2006 P. Kolari, T. Finin, A. Joshi :-: Blog Identification and Splog Detection 2

Blogosphere - the brighter side

• Panel View– Market Research– PR Monitoring

• From Presentations– Opinion Extraction– Demography based analysis


Blogosphere - the darker side (1)

• From the Panel– Blogger is cracking down splogs– SixApart and TypePad– Content Hijacking

• From Presentations– Removing SPAM an essential part of

blog search engine– Cost of cleaning up splogs and its effect

on results


Blogosphere - the darker side (2)


The Blogosphere

Blogger

msn-spaces

livejournal

InformationAudienceBLOG HOSTS

PING SERVERS

SPINGS SPLOGS


Spings – weblogs.com


Spings – weblogs.com (2)


Spings – weblogs.com (3)


Splogs – icerocket.com


Splogs – icerocket.com (2)


A Featured Splog?


Splogs – technorati.com (2)


“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, AnySearch Engine, Any Keyword.”

Splogs – The Source!


Spam we target -- summarized

• Non-blogs– For increased search engine exposure– Through BLOG IDENTIFICATION

• Splogs– Adsense clicks for high-paying contexts (i)– Unjustifiably increase page-rank

(importance) of affiliates – link farms (ii)– Combination of (i) and (ii)– Through SPLOG DETECTION


This work

Can machine learning models be effective to counter splogs on the blogosphere?

How do they perform when using features local to a blog?


Dataset for Training

• Technorati random sampling• 500K blogs – May/June 2005• Dropped those from top blogging

hosts– Blog Identification is an easy tasking

using just URL patterns/domains• Sampled the rest in different ways to

create training datasets


Blog-HomePage/Non-Blog

• Sampled for blog home-pages • Sampled for external links from these

blogs to capture contextually similar pages – but from non-blogs

• All samples were manually verified• Training set consists of 2100 positive

and 2100 negative samples – multiple languages

• Lets call this (BH, NB)


Blog-SubPage/Non-Blog

• Sampled for local-links from BH• Sampled for out-links similar to NB• No manual verification• 2600 positive and 2600 negative

samples• Lets call this (BNH, NB)


Authentic Blog/Splog

• Manually identified 700 splogs (English) in the BH sample

• Sampled for 700 blogs from the rest

• 700 positive and 700 negative samples

• Lets call this (AB, S)


Comparison Baselines

Feature Precision Recall F1

meta 1 .75 .85RSS/Atom .96 .90 .93Text - blog .88 .79 .83Text – comment .83 .87 .85Text – trackback .99 .18 .30Text – 2005 .56 .97 .71

• Blog Identification

• Splog Detection is a known problem!


Evaluation - Background

• SVMs as implemented by libsvm• Leave-One-Out cross-validation• No stop word elimination• No stemming• Mutual Information for feature selection

– Frequency count provided similar results• Binary feature encoding

– Others encodings give similar results


New features for blogs

• Hyper-links on a page– Tokenized by “/” and “-”

• Anchor-text on a page• Meta tags

– From HTML HEAD element• 4-grams

– Contiguous blocks of 4 characters• Combinations

– words and urls– meta and link– urls, anchors, meta


Blog Identification – (BH, NB)

Feature Precision Recall F1 Feature SizeWords (w) .976 .941 .958 19000Urls (u) .982 .962 .972 7000Anchors (a) .975 .926 .950 8000Meta (m) .981 .774 .865 3500w+u .985 .966 .975 26000m+LINK .973 .939 .956 4000u+a .985 .961 .973 15000u+a+m .986 .964 .975 185004grams .982 .964 .973 25000


Blog Identification – (BNH, NB)



Splog Detection - (AB, S)



An quick Analysis

• Ping Servers– Our analysis in December 2005– At least 75% of pings are spings

• Technorati Index– Data from week of March 20, 2006– Random queries to sample for 10K blogs– 3K blogspot, 2.5K livejournal, 1.8K msn– We predict that 1.5K blogspot, 250 from LJ are

splogs– Overall 2.5K/10K are splogs ~ 25% of the

fresh index!


Blogosphere Spam - Summary

Blogger

msn-spaces

livejournal

InformationAudienceBLOG HOSTS

PING SERVERS

75%

25%50%

10%


And its not getting easier …

But spammers still leave trails that can be exploited


Conclusion

• Blogosphere is prone to spam at various infrastructure points

• Local content based models can be quite effective by itself

• 75% of pings and further downstream, 25% of fresh content is spam

• Blogger’s problem is now livejournal’s problem, and now everyone’s problem

• Combining local and global splog models is our current direction


Questions?

• Google “Splog Detection”• memeta

– http://memeta.umbc.edu• eBiquity

– http://ebiquity.umbc.edu– http://ebiquity.umbc.edu/blogger

• Check out Umbria’s report on splogs– http://www.umbrialistens.com/files/

uploads/umbria_splog.pdf

http://memeta.umbc.edu/

http://memeta.umbc.edu/

http://ebiquity.umbc.edu/

http://ebiquity.umbc.edu/blogger

http://ebiquity.umbc.edu/blogger

Documents

SVMs for the Blogosphere: Blog Identification and Splog Detection