Publication Type

Conference Proceeding Article

Version

Postprint

Publication Date

11-2013

Abstract

Predicting users political party in social media has important impacts on many real world applications such as targeted advertising, recommendation and personalization. Several political research studies on it indicate that political parties’ ideological beliefs on sociopolitical issues may influence the users political leaning. In our work, we exploit users’ ideological stances on controversial issues to predict political party of online users. We propose a collaborative filtering approach to solve the data sparsity problem of users stances on ideological topics and apply clustering method to group the users with the same party. We evaluated several state-of-the-art methods for party prediction task on debate.org dataset. The experiments show that using ideological stances with Probabilistic Matrix Factorization (PMF) technique achieves a high accuracy of 88.9% at 22.9% data sparsity rate and 80.5% at 70% data sparsity rate on users’ party prediction task.

Keywords

Collaborative Filtering, Ideological Stances, Memory-based CF, Model-based CF, Probabilistic Matrix Factorization

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Influence and Political Communication | Social Media

Research Areas

Data Management and Analytics

Publication

Social Informatics: 5th International Conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013: Proceedings

Volume

8238

First Page

177

Last Page

191

ISBN

9783319032603

Identifier

10.1007/978-3-319-03260-3_16

Publisher

Springer Verlag

City or Country

Cham

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://dx.doi.org/10.1007/978-3-319-03260-3_16