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Communication Dans Un Congrès Année : 2013

'Current City' prediction for coarse location based applications on Facebook

Résumé

Location-Based services with social networks improve users' experience and enrich people's social live. However, location information is often inadequate due to privacy and security concerns. We seek to infer users' `Current City' on Facebook for coarse location based applications. We first extract users' multiple explicit and implicit location attributes, and analyze correlations of these attributes from two perspective: user-centric and user-friends. We observe that both user-centric and user-friends location attributes tightly correlate to a user's Current City (e.g., 60% of users stay in their hometown, 60% of users live in the same city as 50% of their friends). Based on extensive analysis and observations on location attributes correlations, we have constructed a Current City Prediction model (CCP) using artificial neural network (ANN) learning frameworks. The experimental results indicate that we achieve accuracy levels of 84% for city-level prediction and 98% for country-level which are increases of 9% and 18%, respectively than what is possible with Tweecalization
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Dates et versions

hal-01297715 , version 1 (04-04-2016)

Identifiants

Citer

Wipada Chanthaweethip, Xiao Han, Noel Crespi, Yuanfang Chen, Reza Farahbakhsh, et al.. 'Current City' prediction for coarse location based applications on Facebook. GLOBECOM 2013 : IEEE Global Communications Conference, Exhibition & Industry Forum, Dec 2013, Atlanta, United States. pp.3188 - 3193, ⟨10.1109/GLOCOM.2013.6831562⟩. ⟨hal-01297715⟩
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