Classifying latent user attributes in twitter

ABSTRACT

Social media outlets such as Twitter have become an important forum for peer interaction. Thus the ability to classify latent user attributes, including gender, age, regional origin, and political orientation solely from Twitter user language or similar highly informal content has important applications in advertising, personalization, and recommendation. This paper includes a novel investigation of stacked-SVM-based classification algorithms over a rich set of original features, applied to classifying these four user attributes. It also includes extensive analysis of features and approaches that are effective and not effective in classifying user attributes in Twitter-style informal written genres as distinct from the other primarily spoken genres previously studied in the user-property classification literature. Our models, singly and in ensemble, significantly outperform baseline models in all cases. A detailed analysis of model components and features provides an often entertaining insight into distinctive language-usage variation across gender, age, regional origin and political orientation in modern informal communication.

References

1. T. Bocklet, A. Maier, and E. Nöth. Age determination of children in preschool and primary school age with gmm-based supervectors and support vector machines/regression. In TSD '08: Proceedings of the 11th international conference on Text, Speech and Dialogue, pages 253--260, Berlin, Heidelberg, 2008. Springer-Verlag.
2. C. Boulis and M. Ostendorf. A quantitative analysis of lexical differences between genders in telephone conversations. In ACL '05: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pages 435--442, Morristown, NJ, USA, 2005. Association for Computational Linguistics.
3. J. Burger and J. Henderson. An exploration of observable features related to blogger age. In Computational Approaches to Analyzing Weblogs: Papers from the 2006 AAAI Spring Symposium, 2006.
4. J. Coates. Language and Gender: A Reader. Blackwell Publishers, 1998.
5. P. Eckert and S. McConnell-Ginet. Language and Gender. Cambridge University Press, 2003.
6. J. Fischer. Social influences on the choice of a linguistic variant. In Proceedings of Word, 1958.
7. N. Garera and D. Yarowsky. Modeling latent biographic attributes in conversational genres. In Proceedings of the Joint Conference of Association of Computational Linguistics and International Joint Conference on Natural Language Processing (ACL-IJCNLP), pages 710--718, 2009.
8. S. Herring and J. Paolillo. Gender and genre variation in weblogs. In Journal of Sociolinguistics, 2006.
9. T. Joachims. Learning to Classify Text using Support Vector Machines. Kluwer, 2002.
10. W. Labov. The Social Stratification of English in New York City. Center for Applied Linguistics, Washington DC, 1966.
11. R. K. Macaulay. Talk that counts: Age, Gender, and Social Class Differences in Discourse. Oxford University Press, 2005.
12. S. Nowson and J. Oberlander. The identity of bloggers: Openness and gender in personal weblogs. In Computational Approaches to Analyzing Weblogs: Papers from the 2006 AAAI Spring Symposium, 2006.
13. S. Singh. A pilot study on gender differences in conversational speech on lexical richness measures. In Literary and Linguistic Computing, 2001.
14. M. Thomas, B. Pang, and L. Lee. Get out the vote: determining support or opposition from congressional floor-debate transcripts. In EMNLP '06, 2006.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Natural language processing