1. A cross-validation method that facilitates verifying reliability and trustworthiness of user classifications comprising: excluding one or more suspected user's classifications from data employed to train a spam filter, performed at a server side, based on the user incorrectly classifying email as spam or as not spam; training the spam filter using all other available user classifications; running the suspected user's polling messages through the trained spam filter to determine how it would have classified the messages compared to the suspected user's classifications; validating users trustworthiness to classify email based on their classification of incoming email as spam that is known or subsequently determined to be spam or their classification of incoming email as not spam that is known or subsequently determined to not be spam; and discounting existing and future classifications provided by users who are determined to be untrustworthy until the users are determined to be trustworthy.