1. A computer-implemented method for filtering messages, comprising: receiving a first electronic mail (email) message; analyzing a portion of the first email message by searching for character sequences that are indicative of spam, wherein the character sequences correspond to one or more runs of characters of a particular run length including individual lengths of characters and sub-lengths of characters that are not restricted to whole words or space-separated words; determining a degree of randomness associated with an individual character sequence of the character sequences; generating a feature relating to the individual character sequence based at least partly on the degree of randomness associated with the individual character sequence; training a machine learning filter using at least the feature to generate a trained machine learning filter; employing the trained machine learning filter to obtain a verdict as to whether one or more features of a second email message indicate that the second email message is likely to be spam, and filtering the second email message based at least in part on the verdict.