1. A method for analyzing character codes in text of a message to determine a probability that the message is spam, the method comprising: (a) receiving a message that includes character codes in text of the message; (b) identifying character codes of the text that are likely being used to obfuscate a word or phrase of the text; (c) deobfuscating each word or phrase of the text that is identified at step (b) as likely being obfuscated, to produce deobfuscated text; and (d) determining an extent that the character codes of the text are identified as likely being used to obfuscate a word or phrase of the text by determining one or more of the following a quantity of words or phrases identified at step (b) as likely being obfuscated; which particular words or phrases are identified at step (b) as likely being obfuscated, and (e) analyzing the deobfuscated text by comparing the deobfuscated text to text of one or more other messages known to be spam; (f) determining a probability that the message is spam based on both (i) results of the analyzing the deobfuscated text, by comparing the deobfuscated text to text of one or more other messages known to be spam, performed at step (e), and (ii) results of the determining the extent that the character codes of the text are likely being used to obfuscate a word or phrase of the text, performed at step (d); wherein at least steps (b), (c), (d), (e) and (f) are performed by one or more processors.
10. The method of claim 1 , wherein: step (b) includes identifying one or more words that are likely misspelled; step (c) includes replacing one or more words that are likely misspelled with one or more words that were likely intended if the one or more words were correctly spelled, when producing the deobfuscated text; step (d) includes searching the deobfuscated text for words and phrases that are known to be used in spam; step (e) includes comparing the deobuscated text to text of one or more other messages known to be spam to determine a degree of sameness; step (f) includes taking into account whether one or more word that is likely misspelled is a word or is part of a phrase that is known to be used in spam, when determining the probability that the message is spam; and also taking into account the degree of sameness when determining the probability that the message is spam.