  In recent years, messages and text posted on the Internet are used in
criminal investigations. Unfortunately, the authorship of many of them remains
unknown. In some channels, the problem of establishing authorship may be even
harder, since the length of digital texts is limited to a certain number of
characters. In this work, we aim at identifying authors of tweet messages,
which are limited to 280 characters. We evaluate popular features employed
traditionally in authorship attribution which capture properties of the writing
style at different levels. We use for our experiments a self-captured database
of 40 users, with 120 to 200 tweets per user. Results using this small set are
promising, with the different features providing a classification accuracy
between 92% and 98.5%. These results are competitive in comparison to existing
studies which employ short texts such as tweets or SMS.
