Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

6-2013

Abstract

Threaded discussion forums provide an important social media platform. Its rich user generated content has served as an important source of public feedback. To automatically discover the viewpoints or stances on hot issues from forum threads is an important and useful task. In this paper, we propose a novel latent variable model for viewpoint discovery from threaded forum posts. Our model is a principled generative latent variable model which captures three important factors: viewpoint specific topic preference, user identity and user interactions. Evaluation results show that our model clearly outperforms a number of baseline models in terms of both clustering posts based on viewpoints and clustering users with different viewpoints.

Keywords

discussion forum posts, user identity, user interaction, user viewpoints, model

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media

Research Areas

Data Management and Analytics

Publication

Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics-Human Language Technologies 2013, 9-14 June, Atlanta

First Page

1031

Last Page

1040

Publisher

Association for Computational Linguistics

City or Country

Stroudsburg, PA

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://aclweb.org/anthology/N13-1123

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