It's MySpace Spamer.

It's MySapce spamer. Is $234 Million judgement not enough? Well, MySpace has made it very clear that one can't get into his or her defined space. Unless, you are willing to pay the price for it. I don't think it's worth it. Better don't go there..

The name “Spam” comes from a Monty Python sketch where a group of Vikings wish to eat in a restaurant where the menu contains so much Spam (the food) that it is difficult to determine what else is available.

Spam filtering is a difficult classification task for a variety of reasons. Spam is constantly changing as spam on new topics emerges. Also, spammers attempt to make their messages as indistinguishable from legitimate email as possible and change the patterns of spam to foil the filters. Another serious issue is the problem of false positives, i.e. a legitimate email classified as spam. For many email users, false positives are simply unacceptable; thus the requirements on the spam filter are very exacting. As new types of spam emerge and spammers change their behaviour to avoid detection, a content-based system will require updating. Features that are predictive of the new types of spam and rules to handle these features are required. You must have noticed that how serious this spam business can be..

"The popular online hangout MySpace has won a $234 million judgment over junk messages sent to its members in what is believed to be the largest anti-spam award ever, The Associated Press has learned. A federal judge ruled against two of the Internet's most prominent spam defendants, Sanford Wallace and Walter Rines, after the two failed to show up at a court hearing. Wallace has earned the nicknames "Spamford" and "spam king" for his past role as head of a company that sent as many as 30 million junk e-mails a day in the 1990s. Press Release"

There is a fair deal of interest in using Machine Learning (ML) techniques to automate this process. Because of its proven ability in text classification (Lewis & Ringuette, 1994), the Naïve Bayes approach is the most popular ML technique in research on spam filtering (e.g. Sahami et al, 1999).