Abstract
Online Social Network (OSN) is one of the hottest innovations in the past years. For OSN, users’ behavior is one of the important factors to study. This demonstration proposal presents Harbinger, an analyzing and predicting system for OSN users’ behavior. In Harbinger, we focus on tweets’ timestamps (when users post or share messages), visualize users’ post behavior as well as message retweet number and build adjustable models to predict users’ behavior. Predictions of users’ behavior can be performed with the established behavior models and the results can be applied to many applications such as tweet crawlers and advertisements.
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Guo, R., Wang, H., Zhong, L., Li, J., Gao, H. (2014). Harbinger: An Analyzing and Predicting System for Online Social Network Users’ Behavior. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds) Database Systems for Advanced Applications. DASFAA 2014. Lecture Notes in Computer Science, vol 8422. Springer, Cham. https://doi.org/10.1007/978-3-319-05813-9_38
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DOI: https://doi.org/10.1007/978-3-319-05813-9_38
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05812-2
Online ISBN: 978-3-319-05813-9
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