Abstract
In recent years, Online Social Networks (OSNs) have changed the way people connect and interact with each other. Indeed, most people have registered an account on some popular OSNs (such as Facebook, or Google+) which is used to access the system at different times of the days, depending on their life and habits. In this context, understanding how users connect to the OSNs is of paramount importance for both the protection of their privacy and the OSN’s provider (or third-party applications) that want to exploit this information. In this paper, we study the task of predicting the availability status (online/offline) of the OSNs’ users by exploiting the availability information of their friends. The basic idea is to evaluate how the knowledge about availability status of friends can help in predicting the availability status of the center-users. For this purpose, we exploit several learning algorithms to find interesting relationships between the availability status of the users and those of their friends. The extensive validation of the results, by using a real Facebook dataset, indicates that the availability status of the users’ friends can help in predicting whether the central user is online or offline.
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De Salve, A., Mori, P., Ricci, L. (2017). Evaluating the Impact of Friends in Predicting User’s Availability in Online Social Networks. In: Guidotti, R., Monreale, A., Pedreschi, D., Abiteboul, S. (eds) Personal Analytics and Privacy. An Individual and Collective Perspective. PAP 2017. Lecture Notes in Computer Science(), vol 10708. Springer, Cham. https://doi.org/10.1007/978-3-319-71970-2_6
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DOI: https://doi.org/10.1007/978-3-319-71970-2_6
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