Title

Shell Miner: Mining Organizational Phrases in Argumentative Texts in Social Media

Publication Type

Conference Proceeding Article

Publication Date

12-2014

Abstract

Threaded debate forums have become one of the major social media platforms. Usually people argue with one another using not only claims and evidences about the topic under discussion but also language used to organize them, which we refer to as shell. In this paper, we study how to separate shell from topical contents using unsupervised methods. Along this line, we develop a latent variable model named Shell Topic Model (STM) to jointly model both topics and shell. Experiments on real online debate data show that our model can find both meaningful shell and topics. The results also show the effectiveness of our model by comparing it with several baselines in shell phrases extraction and document modeling.

Discipline

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

Research Areas

Data Management and Analytics

Publication

2014 IEEE International Conference on Data Mining (ICDM): Proceedings: 14-17 December 2014, Shenzhen

First Page

797

Last Page

802

ISBN

9781479943036

Identifier

10.1109/ICDM.2014.98

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

http://dx.doi.org/10.1109/ICDM.2014.98