Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

10-2013

Abstract

Online discussion forums are popular social media platforms for users to express their opinions and discuss controversial issues with each other. To automatically identify the sides/stances of posts or users from textual content in forums is an important task to help mine online opinions. To tackle the task, it is important to exploit user posts that implicitly contain support and dispute (interaction) information. The challenge we face is how to mine such interaction information from the content of posts and how to use them to help identify stances. This paper proposes a two-stage solution based on latent variable models: an interaction feature identification stage to mine interaction features from structured debate posts with known sides and reply intentions; and a clustering stage to incorporate interaction features and model the interplay between interactions and sides for debate side clustering. Empirical evaluation shows that the learned interaction features provide good insights into user interactions and that with these features our debate side model shows significant improvement over other baseline methods.

Keywords

social media, discussion posts, viewpoint identification, data mining, user interaction

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Publication

CIKM '13 Proceedings of the 22nd ACM international conference on Information & Knowledge Management

First Page

873

Last Page

878

ISBN

9781450322638

Identifier

10.1145/2505515.2505634

Publisher

ACM

City or Country

San Francisco, USA

Additional URL

http://dx.doi.org/10.1145/2505515.2505634

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