  SMS messaging is a popular media of communication. Because of its popularity
and privacy, it could be used for many illegal purposes. Additionally, since
they are part of the day to day life, SMSes can be used as evidence for many
legal disputes. Since a cellular phone might be accessible to people close to
the owner, it is important to establish the fact that the sender of the message
is indeed the owner of the phone. For this purpose, the straight forward
solutions seem to be the use of popular stylometric methods. However, in
comparison with the data used for stylometry in the literature, SMSes have
unusual characteristics making it hard or impossible to apply these methods in
a conventional way. Our target is to come up with a method of authorship
detection of SMS messages that could still give a usable accuracy. We argue
that, considering the methods of author attribution, the best method that could
be applied to SMS messages is an n-gram method. To prove our point, we checked
two different methods of distribution comparison with varying number of
training and testing data. We specifically try to compare how well our
algorithms work under less amount of testing data and large number of candidate
authors (which we believe to be the real world scenario) against controlled
tests with less number of authors and selected SMSes with large number of
words. To counter the lack of information in an SMS message, we propose the
method of stacking together few SMSes.
