  Gender analysis of Twitter can reveal important socio-cultural differences
between male and female users. There has been a significant effort to analyze
and automatically infer gender in the past for most widely spoken languages'
content, however, to our knowledge very limited work has been done for Arabic.
In this paper, we perform an extensive analysis of differences between male and
female users on the Arabic Twitter-sphere. We study differences in user
engagement, topics of interest, and the gender gap in professions. Along with
gender analysis, we also propose a method to infer gender by utilizing
usernames, profile pictures, tweets, and networks of friends. In order to do
so, we manually annotated gender and locations for ~166K Twitter accounts
associated with ~92K user location, which we plan to make publicly available at
http://anonymous.com. Our proposed gender inference method achieve an F1 score
of 82.1%, which is 47.3% higher than majority baseline. In addition, we also
developed a demo and made it publicly available.
