DocumentCode :
2439771
Title :
Tweeque: Spatio-Temporal Analysis of Social Networks for Location Mining Using Graph Partitioning
Author :
Abrol, S. ; Khan, Latifur ; Thuraisingham, Bhavani
Author_Institution :
Dept. of Comput. Sci., Univ. of Texas at Dallas, Richardson, TX, USA
fYear :
2012
fDate :
14-16 Dec. 2012
Firstpage :
145
Lastpage :
148
Abstract :
Because of privacy and security reasons, most of the people on social networking sites like Twitter are unwilling to specify their locations in the profiles. In this paper, we present a completely novel approach, Tweeque which is a spatio-temporal mining algorithm that predicts the current location of the user purely on the basis of his social network. The algorithm goes beyond the previous approaches by linking geospatial proximity to friendship and understanding the social phenomenon of migration. The algorithm then performs graph partitioning for identifying social groups allowing us to implicitly consider time as a factor for prediction of user´s most current city location. We perform extensive experiments to show the validity of our system in terms of both accuracy and running time.
Keywords :
data mining; data privacy; graph theory; social networking (online); Tweeque; Twitter; city location; geospatial proximity; graph partitioning; location mining; privacy reasons; security reasons; social groups; social networking sites; spatio-temporal analysis; spatio-temporal mining algorithm; Location Mining; Social Computing; Twitter;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Social Informatics (SocialInformatics), 2012 International Conference on
Conference_Location :
Lausanne
Print_ISBN :
978-1-4799-0234-7
Type :
conf
DOI :
10.1109/SocialInformatics.2012.93
Filename :
6542434
Link To Document :
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