  This paper addresses the task of user gender classification in social media,
with an application to Twitter. The approach automatically predicts gender by
leveraging observable information such as the tweet behavior, linguistic
content of the user's Twitter feed and the celebrities followed by the user.
This paper first evaluates linguistic content based features using LIWC
dictionary and popular neighborhood features using Wikipedia and Freebase. Then
augments both features which yielded a significant increase in the accuracy for
gender prediction. Results show that rich linguistic features combined with
popular neighborhood prove valuables and promising for additional user
classification needs.
