Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2014

Abstract

Discovering user demographic attributes from social media is a problem of considerable interest. The problem setting can be generalized to include three components — users, topics and behaviors. In recent studies on this problem, however, the behavior between users and topics are not effectively incorporated. In our work, we proposed an integrated unsupervised model which takes into consideration all the three components integral to the task. Furthermore, our model incorporates collaborative filtering with probabilistic matrix factorization to solve the data sparsity problem, a computational challenge common to all such tasks. We evaluated our method on a case study of user political affiliation identification, and compared against state-of-the-art baselines. Our model achieved an accuracy of 70.1% for user party detection task.

Keywords

Unsupervised Integrated Model, Social/feedback networks, Probabilistic Matrix Factorization, Collaborative filtering

Discipline

Computer Sciences | Databases and Information Systems

Publication

Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part I

First Page

434

Last Page

446

ISBN

9783319066073

Identifier

10.1007/978-3-319-06608-0_36

Publisher

Springer Verlag

City or Country

Cham

Copyright Owner and License

LARC

Additional URL

http://dx.doi.org/10.1007/978-3-319-06608-0_36

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