We've had means of expressing our opinions on various things for a long time.  
Amazon has had reviews of the books pretty much since the beginning.  But how 
do we know that the reviews are real?  Virus writers took the opportunity 
presented by Amazon to trash my books when they were published.  (Even though 
they used different names, it only took a very simple form of forensic 
linguistics 
to figure out the identities.)

More recently, review spam has become more important, since many people are 
relying on the online reviews when buying items or booking services.  A number 
of 
"companies" have determined that it is more cost effective to have bots or 
other 
entities flood the review systems with fake positive reviews than it is to make 
quality products or services.  So, some nice people from Cornell university 
produced and tested some software to determine the fakes.

http://j.mp/r8vvya+

Note that, from these slides, there is not a lot of detail about exactly how 
they 
determine the fakes.  However, there is enough to indicate that sophisticated 
algorithms are less accurate than some fairly simple metrics.  When I teach 
about 
software forensics (aspects of which are similar to forensic lingusitics, or 
stylistic 
forensics), this seems counterintuitive and surprises a lot of students.  
Generally 
they object that, if you know about the metircs, you should be able to avoid 
them.  
In practice, this doesn't seem to be the case.  Simple metrics do seem to be 
very 
effective in both forensic linguistics, and in software forensics.

======================  (quote inserted randomly by Pegasus Mailer)
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Priority is an attempt to implement the principles of jealousy
and envy in computer networks.                          - Tony Lauck
victoria.tc.ca/techrev/rms.htm http://www.infosecbc.org/links
http://blogs.securiteam.com/index.php/archives/author/p1/
http://twitter.com/rslade
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