Abstract
Most existing works on categorization of social media users in online social networks (OSNs) consider only the topical interest of users as the basis for user classification. The temporal evolution of user topical interests has not been thoroughly studied to identify their effects on the classification of social users. In this paper, we investigate the problem of discovering/classifying and tracking time-sensitive activity-driven social user classification in OSNs. The users in a particular class have the tendency to be temporally similar in terms of their temporal degree of topical interests. Our main idea is based on the observation that the degree of users’ topical interests often degrades or upgrades widely over a period of time. The temporal tendency of user activities is modeled as the freshness of recent activities by tracking the social streams with a fading time window.
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Hossen, M., Afrose, T., Ghosh, A.M., Anwar, M.M. (2021). Classification of Social Media Users Based on Temporal Behaviors and Interests. In: Sharma, H., Gupta, M.K., Tomar, G.S., Lipo, W. (eds) Communication and Intelligent Systems. Lecture Notes in Networks and Systems, vol 204. Springer, Singapore. https://doi.org/10.1007/978-981-16-1089-9_72
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DOI: https://doi.org/10.1007/978-981-16-1089-9_72
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