TY - GEN
T1 - User characterization from geographic topic analysis in online social media
AU - Zheng, Jiangchuan
AU - Liu, Siyuan
AU - Ni, Lionel M.
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/10/10
Y1 - 2014/10/10
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.
UR - https://www.scopus.com/pages/publications/84911191611
UR - https://www.scopus.com/pages/publications/84911191611#tab=citedBy
U2 - 10.1109/ASONAM.2014.6921627
DO - 10.1109/ASONAM.2014.6921627
M3 - Conference contribution
AN - SCOPUS:84911191611
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
A2 - Ester, Martin
A2 - Xu, Guandong
A2 - Wu, Xindong
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014
Y2 - 17 August 2014 through 20 August 2014
ER -