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Stop Spam - A Simple Trick To Get The Most Out Of Your Bayesian Filter

If you sat a person infront of a computer, and asked them to remove all the spam messages in someone's inbox, you would expect them to achieve close to 100% accuracy. This implies that there is something innate in a spam email that is recognisable as 'spammy'.

 

This is the idea that Bayesian Filters work on. They process emails before they are delivered to your inbox, assigning a 'spam score' to each word of each message. The average 'spam score' is then used to determine whether the email is to be spammed or not.

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Bayesian Filters are used by most email providers in some manner. Generally ISP's will use some form of Bayesian filter to skim the most blatant spam messages off the top, and then leave the filtering of the marginal messages to the email user themselves. Webmails use very efficiently Bayesian filters which draw on both individual and collective data to determine if a message is wanted.

 

Every message that is delivered by webmail is either read or deleted, but webmails, and Bayesian Filter add-ons to conventional mail clients split the deleted messages into two types. They are either deleted because they are no longer needed, or they are deleted as spam. When a message gets deleted as spam the Bayesian filter analyses it and processes the data. The more messages are marked as spam the easier it becomes for the Bayesian filter to identify spam.

So whether you use a webmail or a Bayesian filter add-on with outlook express, the key to achieving the best efficiency from your filter is ensuring you use the 'delete as spam' function properly. This is not as easy as it sounds. People have a tendency simply to delete mail from their inbox that is not wanted, be it spam or otherwise. But by doing this you are actually marking the spam that has got through your filter as 'not-spam', which makes it easier for the same message to get through again.

Likewise, people have a tendency to spam legitimate mailings from mailing lists they no longer want to receive. In these cases they have opted in to a list, but no longer want to receive messages so begin spamming them.

After enough spammings the message will carry a high spam value and no longer make it to their in box. What these people may not realise is they are also raising the spam value of the mailer for other users, who also signed up and want to receive the messages.

   

In both cases the problem is down to the user only wanting to delete mail once - so they either delete everything as spam, or they delete everything normally. To achieve the most efficiency you need to shake this habit. Delete spam as spam and everything else as 'not-spam'. You'll find that the more you follow this simple rule, the more effective your Bayesian filter will become, and you will have both less 'false positives' and less spam slipping through.

With an estimated 70-90 billion spam messages sent every day, the problem is not going away. Don't wait for someone to solve the problem for you, visit The Stop Spam Now Site and review the very best methods of stopping spam.

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