Publication Type

Journal Article

Version

acceptedVersion

Publication Date

4-2017

Abstract

Microblogging encompasses both user-generated content and behavior. When modeling microblogging data, one has to consider personal and background topics, as well as how these topics generate the observed content and behavior. In this article, we propose the Generalized Behavior-Topic (GBT) model for simultaneously modeling background topics and users' topical interest in microblogging data. GBT considers multiple topical communities (or realms) with different background topical interests while learning the personal topics of each user and the user's dependence on realms to generate both content and behavior. This differentiates GBT from other previous works that consider either one realm only or content data only. By associating user behavior with the latent background and personal topics, GBT helps to model user behavior by the two types of topics. GBT also distinguishes itself from other earlier works by modeling multiple types of behavior together. Our experiments on two Twitter datasets show that GBT can effectively mine the representative topics for each realm. We also demonstrate that GBT significantly outperforms other state-of-The-Art models in modeling content topics and user profiling.

Keywords

Social media, Microblogging, User behavior, Behavior mining, Topic, Modeling, Probabilistic graphic model

Discipline

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

Research Areas

Data Science and Engineering

Publication

ACM Transactions on Intelligent Systems and Technology

Volume

8

Issue

3

First Page

44: 1

Last Page

37

ISSN

2157-6904

Identifier

10.1145/2990507

Publisher

Association for Computing Machinery (ACM)

Copyright Owner and License

LARC and Authors

Additional URL

https://doi.org/10.1145/2990507

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