Extending off Tim's comments:
I want to briefly mention another way of looking at this issue, and will use Curtis' comments to start:
For the past few years I've looked at this issue (author identification through text content analysis) a bit from a psycholinguistic point of view. ... A 1983 paper (which I also do not have the cite handy for) by Dr. Murray Miron of Syracuse University gave his equations for analyzing two texts (of roughly similar lengths) and establishing a probability that the two writings were produced by the same individual. In his paper, Dr. Miron ... The same idea applies here - for CUSUM or similar analysis to be valid, an analyst needs large volumes of messages where one of the authors is known
One can view this problem in terms of Shannon's theorem about the transmission of a message in the presence of noise:
* Signal -- the identity of the poster (true name, pseudonym, whatever) - characteristic usage of words, of punctuation, and whatnot (see)
Absolutely true, though the question is whether or not this analysis provides a *unique* style signature for a given individual. Note how quickly jargon is passed around and how quickly you, in conversation, acquire new phrases from your surroundings.
- even the ideologies expressed (which LD incorrectly used to conclude Jamie Dinkelacker and I "must" be the same person)
This is where I begin to disagree. Suppose one or two individuals wanted to conduct a debate on a subject and steer the discussion to a predetermined issue space. By creating multiple identities (as Peter and Val did in _Ender's Game_ by Orson Scott Card, *shameless plug: GREAT BOOK*), one could achieve this or another goal by varying the ideologies expressed in the writing and foil this style of analysis. Also, there is a question about the reliability of context-sensitive text analysis engines. If the engine is knowledge base-driven, it is unreliable outside its realm of expertise; if it's a neural net-based package, the training collection is very important.
* Noise -- variations in spelling, usage, etc.
The Net doesn't place a lot of emphasis on spelling and most posts/messages aren't spell-checked, increasing the noise in the system.
- many people use similar constructions and whatnot (like this)
Again, the "unique signature" problem. Perhaps one could determine regional background (New England, Deep South of the US etc.) from this reliably, though I'm not sure how much farther the analysis could be extended.
Now Shannon's theorem, which can be applied here if some care is taken (that is, don't apply it too simplistically or too mechanistically), says that no matter how much noise is present, one can extract the signal if one samples enough.
(Caveats: for a stationary sequence, etc., whereas one's writings may change with time, with the topic at hand, etc.)
I would believe that these caveats are a significant barrier to establishng a unique signature.
But to Shannon's basic view one must also add _intereference_, whether deliberate (spoofing) or not. If I try to emulate the style of S. Boxx, for example, by writing in the form "I am becoming INCREASINGLY DISGUSTED by the blatant disregard for the Cypherpunks CAUSE and ...", then this "intereference" could greatly complicate the signal extraction.
In fact, more obscure correlations would have to be looked at, ones which might require many more messages to analyze...possibly more message samples than exist.
There is also the question of whether or not current text analysis programs are capable of making these distinctions/correlations and whether or not these distinctions can be communicated to the program overseers once they are established. My basic position is that text analysis packages are probably not advanced enough to reliably analyze more or less extemporaneous utterances transmitted in the form of email or posts. However, I second Tim's call for a text analysis on various messages, beginning with those of single authors to check the reliability of the system. I'm just an amateur and of no real help technically, but there's bound to be someone in one of the CS/AI departments around the world that could provide us with a reasonable text analysis engine. Curtis D. Frye cfrye@ciis.mitre.org "If you think I speak for MITRE, I'll tell you how much they pay me and make you feel foolish."