Gender Prediction for Chinese Social Media Data

Abstract

Social media provides users a platform to publish messages and socialize with others, and microblogs have gained more users than ever in recent years. With such usage, user profiling is a popular task in computational linguistics and text mining. Different approaches have been used to predict users' gender, age, and other information, but most of this work has been done on English and other Western languages. The goal of this project is to predict the gender of users based on their posts on Weibo, a Chinese micro-blogging platform. Given issues in Chinese word segmentation, we explore character and word n-grams as features for this task, as well as using character and word embeddings for classification. Given how the data is extracted, we approach the task on a perpost basis, and we show the difficulties of the task for both humans and computers. Nonetheless, we present encouraging results and point to future improvements.