Abstract
Matching profiles can be defined as the process of determining whether two profiles in the same or different social networks are instances of the same user in real life or not. Because of the considerable increase in the number of created accounts in social networks, matching profiles across social networks has become a popular focus in a myriad of research works. Current methods in this field require accurate profile analysis to obtain a high user identification quality. However, such studies are restrictive since they do not consider the profile in its globality. In this work, we target the problem of user identification task based on their created profiles in social networks. Specifically, we conduct two main steps. First, we introduce a supervised model which employs the similarity of features for predicting matched profiles. Second, we propose Tuser3 algorithm to search for the correct matched target profile of a source user on three heterogeneous social networks based on several user profiles features. Our experiments show the effectiveness of our method in matching profiles across the three target social networks.
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short write up /‘bio’ /‘about me’ which the user provides about himself.
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Mbarek, A., Jamoussi, S., BenHamadou, A. (2020). Tuser3: A Profile Matching Based Algorithm Across Three Heterogeneous Social Networks. In: Yang, X., Wang, CD., Islam, M.S., Zhang, Z. (eds) Advanced Data Mining and Applications. ADMA 2020. Lecture Notes in Computer Science(), vol 12447. Springer, Cham. https://doi.org/10.1007/978-3-030-65390-3_16
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