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TopMobile phone has transformed our ways of life, works and connection to the social and physical world. Now, it is an integral part of our social networks and daily life (Chin & Zhang, 2014). Smartphones equipped with multiple sensors have become a useful tool for studying social phenomenon such as social interaction, social networks and organizations (Boonstra et al., 2015). It has also become a useful tool for data collection and sources of data in multiple fields of research. As a source of data for empirical research, the device has gained much attention to the researchers in social sciences, particularly sociology, social psychology, psychology (Raento et al., 2009), urban studies, communication and media studies, public health, epidemiology, computer science and mental health research (Grinter & Eldridge, 2001). The data amassed by using smartphones is goldmine for generating unique insights into human psychology, behavior and interactions (Harari et al., 2016) (Tossell et al., 2012) not only at the personal level but also at social, organizational, and geographical levels. It also allows to have insight into how people maintain social relationships (Eagle et al., 2009), and develop social networks (Wang & He, 2015), the dynamics of human mobility (Deville et al., 2014) (Montoliu et al., 2012) (Palmer et al., 2012) and how disease gets spread (Lajous et al., 2010) (Wesolowski et al., 2014).
Most of the researches related to mobile phones have used massive dataset on call data records (CDRs) to understand multiple dynamics human communication behavior, social network and social relationships. By surveying the results, the researches who used mobile phone datasets (Blondel et al., 2015) found that most of the studies focused on users’ personal mobility, geographical partitioning, urban planning, security, and privacy issues. Analysis of mobile phone data can generate insight into real-time human mobility, density and distribution of population in a certain geographical location leading to the improvement of natural disasters’ impact assessment and emergency response to the disasters (Raento et al., 2009) (Lu et al., 2016) (Gething & Tatem, 2011), epidemic modeling (Salathe et al., 2010) (Wesolowski et al., 2014) and mapping of diffusion of new technologies, new diseases and new pests (Bell et al., 2016), urban planning, public transport design, traffic engineering, human behavior pattern in urban setting (Sagl et al., 2012) (Candia et al., 2008). Boonstra (Tjeerd et al., 2017) investigated the possibility of using a smartphone app to understand the relationship between social connectivity and mental health. They found that Bluetooth sensor data can help to map the social networks of the phone user and can be a source of insights about their mental health. They argued that data collection on the topics by using smartphone apps would be more convenient and less biased that of traditional methods.