  Establishing authorship of online texts is fundamental to combat cybercrimes.
Unfortunately, text length is limited on some platforms, making the challenge
harder. We aim at identifying the authorship of Twitter messages limited to 140
characters. We evaluate popular stylometric features, widely used in literary
analysis, and specific Twitter features like URLs, hashtags, replies or quotes.
We use two databases with 93 and 3957 authors, respectively. We test varying
sized author sets and varying amounts of training/test texts per author.
Performance is further improved by feature combination via automatic selection.
With a large number of training Tweets (>500), a good accuracy (Rank-5>80%) is
achievable with only a few dozens of test Tweets, even with several thousands
of authors. With smaller sample sizes (10-20 training Tweets), the search space
can be diminished by 9-15% while keeping a high chance that the correct author
is retrieved among the candidates. In such cases, automatic attribution can
provide significant time savings to experts in suspect search. For
completeness, we report verification results. With few training/test Tweets,
the EER is above 20-25%, which is reduced to < 15% if hundreds of training
Tweets are available. We also quantify the computational complexity and time
permanence of the employed features.
