to Twitter User Classification

Abstract

This paper addresses the task of user classification in social media, with an application to Twitter. We auto- matically infer the values of user attributes such as po- litical orientation or ethnicity by leveraging observable information such as the user behavior, network struc- ture and the linguistic content of the user’s Twitter feed. We employ a machine learning approach which relies on a comprehensive set of features derived from such user information. We report encouraging experimental results on 3 tasks with different characteristics: political affiliation detection, ethnicity identification and detect- ing affinity for a particular business. Finally, our analy- sis shows that rich linguistic features prove consistently valuable across the 3 tasks and show great promise for additional