Abstract
Human activities can be captured in real time using sensors. The rapid growth in sensing technology and its integration with smartphones has instigated a new paradigm of connecting sensors with social networks. These days, users actively migrate their real-life activities on online social networks (OSNs), which turns OSNs into a soft sensory resource of users' face-to-face events. In this work, we exploit OSN face-to-face (F2F) events and geographical profile information to develop an algorithm, DST, that estimates number of days spent together by a given pair of users. The algorithm learns from popular tour packages to reduce the uncertainty in the individual face-to-face event duration. To the best of our knowledge, we are the first work to estimate the amount of time people spent together, face-to-face interacting. The experimental results show that with the proposed method we get days-spent-together values close to the corresponding true values provided by the users.
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