Inferring your expertise from Twitter: combining multiple types of user activity

Understanding the expertise of users in social networking sites like Twitter is a key component for many applications such as user recommendation and talent seeking. A range of interactions between users on Twitter can provide important information that implicitly reflects a user's expertise. This paper proposes a learning model that tries to infer a user's topical expertise from Twitter using information such as tweets posted by the user and the characteristics of their followers. The model takes various types of user-related data from Twitter as input and considers their inference consistency in the process of learning. It aims to deliver accurate and effective inference results, even in cases where some types of data are missing for a user, e.g. the user has yet to post any tweets. The experiments reported in the paper were conducted on a large-scale Twitter dataset. Experimental results show that our model outperforms several baseline approaches and outperforms approaches which use only a single type of user data for inference.