Finding baby mothers on Twitter

Abstract

In this paper, we study the task of detecting mothers of babies on Twitter. This could be beneficial for baby mother users to find friends, and for companies, organizations or experts to deliver accurately targeted information. Prior works have proposed supervised classification methods to detect generic latent attributes of Twitter users such as age, gender, and political orientation. However, methods and features for classifying generic attributes do not perform well for more specific attributes, such as whether a user is a mother of a young baby. We design feature sets based on followed accounts and profile pictures, which are largely overlooked in existing work. Comparing to three established feature sets, the experimental evaluation shows that our specifically-designed feature sets considerably improve classification accuracy.