Gender Prediction in Random Chat Networks Using Topological Network Structures and Masked Content

Social media is becoming a critical avenue for businesses today to target new customers and create brand loyalty. In order to target users effectively, companies need to know basic information about their users. However, in many cases, user profiles are either incomplete or completely wrong, and one of the most critical pieces of private information is gender. In this paper we examine the case of gender prediction in random chat networks using masked content and topological network structures. Random chat networks (e.g., Chatous.com) are significantly different from most existing social networks, because users do not get to see who they are going to talk to before they engage in a conversation, and the system itself brings users into chats together. Due to the network´s random nature, users have very little information about peers in the network. Additionally, privacy is an ever growing concern in today´s society, thus data analytic tools often need to work with data which has been masked to prevent a possible breach of confidential information. In the paper, we first analyze some fundamental characteristics of random chat networks when broken down by gender. Then we propose an approach for gender prediction using masked words as features and show that gender prediction performance can be boosted by incorporating network topology statistics. Finally, we will examine network statistics which are most useful for gender prediction.