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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jiangchuan Zheng Siyuan Liu Ni, L.M. |
| Copyright Year | 2014 |
| Description | Author affiliation: Heinz Coll., Carnegie Mellon Univ., Pittsburgh, PA, USA (Siyuan Liu) || Dept. of Comp. Sci. & Eng., Hong Kong Univ. of Sci. & Technol., Guangzhou, China (Ni, L.M.) || Dept. of Comp. Sci. & Eng., Hong Kong Univ. of Sci. & Technol., Hong Kong, China (Jiangchuan Zheng) |
| Abstract | Far beyond relationship topology, today's online social networks are also characterized by semantically rich text messages exchanged among users as well as GPS locations associated with those messages, as evidenced by Twitter's geotagged tweets. Textual contents help characterize users' personal interests, while geographical features help link users' behaviors in the online world to those in the physical world such as their mobility patterns. In this paper, instead of studying each aspect separately, as done by most previous works, we combine textual contents and spatial features in a joint way using Bayesian latent topic model in order to construct better algorithms for user characterization and social network study. Specifically, the integration of contents and spatial features in a user-centered environment can not only discover geographic topics but also enable the characterization of users' latent interests with geographic semantics. Such a novel characterization can be leveraged to benefit many interesting studies regarding social network heterogeneity and relationships between online networks and physical world. Using a large-scale twitter data set with broad geographical coverage, we systematically evaluate our framework in several typical inference tasks surrounding user, content and location, as well as carry out empirical studies in real world scenarios. Experimental results demonstrate the advantages of our joint modeling approach, as well as its potentials to facilitate user understanding, both in online world and physical world. |
| Starting Page | 464 |
| Ending Page | 471 |
| File Size | 215202 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781479958771 |
| DOI | 10.1109/ASONAM.2014.6921627 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-08-17 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Conferences Semantics Communities Media Twitter Data models |
| Content Type | Text |
| Resource Type | Article |
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