Identification (biology)

Biology

Politics

World Wide Web

Machine learning

Engineering

Political science

Artificial intelligence

Systems engineering

Set (abstract data type)

Biology and political orientation

Programming language

Natural language processing

Computer science

Task (project management)

Social media

Botany

Proceedings of the International AAAI Conference on Web and Social Media

This paper addresses the task of user classification in social media, with an application to Twitter. We automatically infer the values of user attributes such as political orientation or ethnicity by leveraging observable information such as the user behavior, network structure 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 detecting affinity for a particular business. Finally, our analysis shows that rich linguistic features prove consistently valuable across the 3 tasks and show great promise for additional user classification needs.

A Machine Learning Approach to Twitter User Classification