User characterization from geographic topic analysis in online social media

Jiangchuan Zheng, Siyuan Liu, Lionel M. Ni

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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.

Original languageEnglish (US)
Title of host publicationASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
EditorsMartin Ester, Guandong Xu, Xindong Wu, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages464-471
Number of pages8
ISBN (Electronic)9781479958771
DOIs
StatePublished - Oct 10 2014
Event2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014 - Beijing, China
Duration: Aug 17 2014Aug 20 2014

Publication series

NameASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Other

Other2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014
CountryChina
CityBeijing
Period8/17/148/20/14

Fingerprint

Global positioning system
Semantics
Topology

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications

Cite this

Zheng, J., Liu, S., & Ni, L. M. (2014). User characterization from geographic topic analysis in online social media. In M. Ester, G. Xu, X. Wu, & X. Wu (Eds.), ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 464-471). [6921627] (ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM.2014.6921627
Zheng, Jiangchuan ; Liu, Siyuan ; Ni, Lionel M. / User characterization from geographic topic analysis in online social media. ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. editor / Martin Ester ; Guandong Xu ; Xindong Wu ; Xindong Wu. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 464-471 (ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining).
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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.",
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Zheng, J, Liu, S & Ni, LM 2014, User characterization from geographic topic analysis in online social media. in M Ester, G Xu, X Wu & X Wu (eds), ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining., 6921627, ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Institute of Electrical and Electronics Engineers Inc., pp. 464-471, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014, Beijing, China, 8/17/14. https://doi.org/10.1109/ASONAM.2014.6921627

User characterization from geographic topic analysis in online social media. / Zheng, Jiangchuan; Liu, Siyuan; Ni, Lionel M.

ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ed. / Martin Ester; Guandong Xu; Xindong Wu; Xindong Wu. Institute of Electrical and Electronics Engineers Inc., 2014. p. 464-471 6921627 (ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - User characterization from geographic topic analysis in online social media

AU - Zheng, Jiangchuan

AU - Liu, Siyuan

AU - Ni, Lionel M.

PY - 2014/10/10

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N2 - 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.

AB - 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.

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M3 - Conference contribution

T3 - ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

SP - 464

EP - 471

BT - ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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A2 - Wu, Xindong

PB - Institute of Electrical and Electronics Engineers Inc.

ER -

Zheng J, Liu S, Ni LM. User characterization from geographic topic analysis in online social media. In Ester M, Xu G, Wu X, Wu X, editors, ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Institute of Electrical and Electronics Engineers Inc. 2014. p. 464-471. 6921627. (ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining). https://doi.org/10.1109/ASONAM.2014.6921627