Title

Modeling User Arguments, Interactions and Attributes for Stance Prediction in Online Debate Forums

Publication Type

Conference Proceeding Article

Publication Date

5-2015

Abstract

Online debate forums are important social media for people to voice their opinions and debate with each other. Mining user stances or viewpoints from these forums has been a popular research topic. However, most current work does not address an important problem: for a specific issue, there may not be many users participating and expressing their opinions. Despite the sparsity of user stances, users may provide rich side information; for example, users may write arguments to back up their stances, interact with each other, and provide biographical information. In this work, we propose an integrated model to leverage side information. Our proposed method is a regression-based latent factor model which jointly models user arguments, interactions, and attributes. Our method can perform stance prediction for both warm-start and cold-start users. We demonstrate in experiments that our method has promising results on both micro-level and macro-level stance prediction.

Discipline

Computer Sciences | Databases and Information Systems

Research Areas

Data Management and Analytics

Publication

Proceedings of the 2015 SIAM International Conference on Data Mining: April 30 - May 2, Vancouver, Canada

First Page

855

Last Page

863

ISBN

9781611974010

Identifier

10.1137/1.9781611974010.96

Publisher

SIAM

City or Country

Philadelphia, PA

Additional URL

http://dx.doi.org/10.1137/1.9781611974010.96

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