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Spamming is the abuse of electronic messaging systems to send unsolicited, undesired bulk messages. While the most widely recognized form of spam is e-mail spam, the term is applied to similar abuses in other media: instant messaging spam, Usenet newsgroup spam, Web search engine spam, spam in blogs, and mobile phone messaging spam.

Spamming is economically viable because advertisers have effectively no operating costs beyond the management of their mailing lists, and it has proved difficult to hold senders accountable for their mass mailings. Because the barrier to entry is so low, spammers are numerous and the volume of unsolicited mail has become very high. The costs, such as lost productivity and fraud, are borne by the public and by Internet service providers, which add extra capacity to cope with the deluge. Spamming is widely reviled, and has been the subject of legislation in many jurisdictions. Read more


A Bayesian Approach to Filtering Junk E-Mail  Mehran Sahami, Susan Dumaisy, David Heckermany, Eric Horvitzy -
Efficient and Effective Spam Filtering and Re-ranking for Large Web Datasets  Gordon V. Cormack, Mark D. Smucker, Charles L. A. Clarke -
Learning Rules that Classify E-Mail (1996)  William W. Cohen - Two methods for learning text classifiers are comparedon classification problems that might arise in filtering and filing personal e-mail messages: a "traditional IR" method based on TF-IDF weighting, and a new method for learning sets of "keyword-spotting rules" based on the RIPPER rule learning algorithm.It is demonstrated that both methods obtain significant generalizations from a small number of examples; that both methods are comparable in generalization performance on problems of this type; and that both methods are reasonably efficient, even with fairly large training sets.

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