Blog Track Open Task: Spam Blog Detection Tim Finin Pranam Kolari, Akshay Java, Tim Finin, Anupam Joshi, Justin Martineau University of Maryland, Baltimore County NIST Blog Pre-Track, 14 Nov 2006 James Mayfield Johns Hopkins University Applied Physics Laboratory

Blogosphere Reputation at Stake!

Spam in the Blogosphere Types: comment spam, ping spam, spam blogs Akismet: “87% of all comments are spam” 75% of update pings are spam (ebiquity 2005) 20% of indexed blogs by popular blog search engines is spam (Umbria 2006, ebiquity 2005) “Spam blogs, sometimes referred to by the neologism splogs, are weblog sites which the author uses only for promoting affiliated websites” 1 “Spings, or ping spam, are pings that are sent from spam blogs” 1 1 Wikipedia

Auto-generated and/or Plagiarized Content Advertisements in Profitable Contexts Link Farms to promote affiliates

Why a problem? Blogosphere increasingly important segment of Web; ~12 hours from post to Google index Splog content provides no additional value Splog content is often plagiarized Splogs demote value of authentic content Splogs steal advertising (referral) revenue from authentic content producers Splogs stress the blogosphere infrastructure Splogs can skew Blog Analytics, as was observed in TREC Blog Track 2006

Nature of Splogs in TREC 2006 Around 83K identifiable blog home-pages in the collection, with 3.2M permalinks 81K blogs could be processed We use splog detection models developed on blog home-pages; 87% accuracy We identified 13,542 splogs Blacklisted 543K permalinks from these splogs ~16% of the entire collection ~17% splog posts injected into TREC dataset 1 1 The TREC Blog06 Collection: Creating and Analyzing a Blog Test Collection – C. Macdonald, I. Ounis

Impact of Splogs in TREC Queries American Idol Cholesterol Hybrid Cars

Higher in Spam Prone Contexts Spam query terms based on analysis by McDonald et al Card Interest Mortgage

Splog Detection Task Proposal Motivation –Detecting and eliminating spam is an essential requirement for any blog analysis –Splog detection has characteristics that set it appart from and web spam detection Constraint –Simulate how blog search systems operate Task Statement –Is an input permalink (post) spam?

Relation to Spam Detection TREC has an Spam Classification Task Similar in –Fast online spam detection Different in –Nature of spamming: links, RSS feeds, web graph, metadata –Users targeted indirectly through search engines, e.g. “N1ST” not relevant for “NIST” query

Relation to Web Spam Detection TREC does not have a web spam track Similar in –Spamming web link structure Different in –Coverage: Blog Analytics Engines don’t look beyond blogosphere –Speed of detection is important, 150K posts/hour –Presence of structured text through RSS feeds presents new opportunities, and challenges

Difficulty We have been experimenting with multiple approaches starting mid 2005 Data:

Difficulty Evolving spamming techniques and splog creation genres Most basic technique spam techniques –Generate content by stuffing key dictionary words –Generate link to affiliates, through link dumps on blogrolls, linkrolls or after post content Evolving spam techniques –Scrape contextually similar content to generate posts –RSS hijacking –Aggregation software, e.g. Planet X –Intersperse links randomly –Make link placement meaningful –Add spam comments and then ping. Repeat.

Task Details - Dataset Creation Similar to TREC Blog 2006, a collection of feeds, blog home-pages and permalinks View dataset D as two sets – D base, D test D base to span (n-x) days, and D test to span the rest of x days for x≤1 D could collected as a combination of – D as collected in 2006 –Sample a subset of pings from a ping server over the period that D is collected

Task Details - Assessment Assessors classify spam post into one or more classes based on the kind of spam this post, or the blog hosting it features –Non-blog –Keyword-stuffed –Post-stitching –Post-plagiarism –Post-weaving –Blog/link-roll spam Each assessment typically takes 1-2 minutes Detailed assessment will enable participants to identify classes they handle well and where they can improve

Non-Blog ping at weblogs.com No RSS Feeds No Dated Entry, no comments Possibly plagiarized content

Keyword Stuffed Blog ‘coupon codes’, ‘casino’

Post Stitching Excerpts scraped from other sources

Post Weaving Spam Links contextually placed in post

Link-roll spam With fully plagiarized text

Evaluation D base distributed first, D test subsequently with 50 independent sets of permalinks D base, D test division will mimic how blog search engines operate –Build models to detect splogs – using individual posts, feeds or blog homepages of what is seen –Detect spam in an incoming stream of new blog postings Teams will be judged by how well they detect “spamminess” for new posts

Input/Output {set Q0 docno rank prob runtag} Individual set of test input. 1 or y such sets can be used, with each set biased to a specific splog genre, blog Publishing host or TLD Each permalink to be judged by participants Output format

Summary Spam Blogs present a major challenge to the quality of blog mining/analytics Splog Detection is different from spam in other communication platforms Development of TREC Task will help furthering state of the art Task requirements can be easily aligned with existing task of opinion identification