  Much attention has been given to the task of gender inference of Twitter
users. Although names are strong gender indicators, the names of Twitter users
are rarely used as a feature; probably due to the high number of ill-formed
names, which cannot be found in any name dictionary. Instead of relying solely
on a name database, we propose a novel name classifier. Our approach extracts
characteristics from the user names and uses those in order to assign the names
to a gender. This enables us to classify international first names as well as
ill-formed names.
