Conference Proceeding Article
Textual information exchanged among users on online social network platforms provides deep understanding into users' interest and behavioral patterns. However, unlike traditional text-dominant settings such as o ine publishing, one distinct feature for online social network is users' rich interactions with the textual content, which, unfortunately, has not yet been well incorporated in the existing topic modeling frameworks. In this paper, we propose an LDA-based behavior-topic model (B-LDA) which jointly models user topic interests and behavioral patterns. We focus the study of the model on online social network settings such as microblogs like Twitter where the textual content is relatively short but user interactions on them are rich. We conduct experiments on real Twitter data to demonstrate that the topics obtained by our model are both informative and insightful. As an application of our B-LDA model, we also propose a Twitter followee recommendation algorithm combining B-LDA and LDA, which we show in a quantitative experiment outperforms LDA with a significant margin.
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
Proceedings of the 2013 SIAM International Conference on Data Mining: 2-4 May 2013, Austin, Texas
City or Country
QIU, Minghui; ZHU, Feida; and JIANG, Jing.
It Is Not Just What We Say, But How We Say Them: LDA-based Behavior-Topic Model. (2013). Proceedings of the 2013 SIAM International Conference on Data Mining: 2-4 May 2013, Austin, Texas. 794-802. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1734
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.