Inferring User Demographics and Social Strategies in Mobile Social Networks Yuxiao Dongy, Yang Yangz, Jie Tangz, Yang Yangy, Nitesh V. Chawlay yDepartment of Computer Science and Engineering, University of Notre Dame yInterdisciplinary Center for Network Science and Applications, University of Notre Dame zDepartment of Computer Science and Technology, Tsinghua University
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[email protected] ABSTRACT 1. INTRODUCTION Demographics are widely used in marketing to characterize differ- From a recent report by the International Telecommunications ent types of customers. However, in practice, demographic infor- Union (ITU) at the 2013 Mobile World Congress, the number of mation such as age, gender, and location is usually unavailable due mobile-phone subscriptions reached 6.8 billion, corresponding to to privacy and other reasons. In this paper, we aim to harness the a global penetration of 96%. As of 2014, the number of mobile power of big data to automatically infer users’ demographics based subscriptions across the globe has exceeded the world population. on their daily mobile communication patterns. These mobile devices record huge amounts of user behavioral data, Our study is based on a real-world large mobile network of in particular users’ daily communications with others. This pro- more than 7,000,000 users and over 1,000,000,000 communication vides us with an unprecedented opportunity to study how people records (CALL and SMS). We discover several interesting social behave differently and form different groups. strategies that mobile users frequently use to maintain their so- Previous work on mobile social networks mainly focuses on cial connections.