Abstract
Recent developments in Online Social Network (OSN) technologies and services, added with availability of wide range of applications has paved the way towards popularity of several social network platforms. These OSNs have evolved as a major communication and interaction platform for millions of users worldwide. The users interact with their social contacts by using various types of available services like messaging, sharing pictures /videos, and many more. However, a major drawback of these platforms is that these activities might reveal certain private information about the users unintentionally. Whenever a user shares any information on OSN with his friends, the information is prone to leakage to other users. The probability of leakage increases with the visibility of the user himself (i.e. the number of users who would be interested on the information of the user) as well as the visibility of his/her friends. Therefore, it is important to measure the visibility of a user in the OSN community. This paper proposes a measure for the visibility of a user, by considering the connectivity properties of the users present in the network. The characteristics of the proposed measure is studied on a real Twitter network as well as a generated Erdős-Rényi network, where we observe the relation between visibility and certain topological parameters of the network. The results show that visibility of a user is determined by his/her direct social contacts, i.e. the number of followers in case of Twitter. However, evaluating the visibility of an user is practically difficult considering the immensely large size of the OSN’s. These findings help us to generate simple mechanisms to estimate the visibility of a user using only its local connectivity properties.
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Rathore, N.C., Tripathy, S., Chandra, J. (2015). Predicting User Visibility in Online Social Networks Using Local Connectivity Properties. In: Natarajan, R., Barua, G., Patra, M.R. (eds) Distributed Computing and Internet Technology. ICDCIT 2015. Lecture Notes in Computer Science, vol 8956. Springer, Cham. https://doi.org/10.1007/978-3-319-14977-6_46
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DOI: https://doi.org/10.1007/978-3-319-14977-6_46
Publisher Name: Springer, Cham
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