The purpose of this paper is to activate latent users posts by modeling user behaviors by a transition of clusters that represent particular posting activities. Twitter has rapidly spread and become an easy and convenient microblog that enables users to exchange instant text messages called tweets. There are so many latent users whose posting activities have decreased.
Under this model, two kinds of time-series analysis methods are proposed to clarify the lifecycles of Twitter users. In the first one, all users belong to a cluster consisting of several features at individual time slots and move among the clusters in a time series. In the second one, the posting activities of Twitter users are analyzed by the amount of tweets that vary with time.
This sophisticated evaluation using a large actual tweet-set demonstrated the proposed methods effectiveness. The authors found a big difference in the state transition diagrams between long- and short-term users. Analysis of short-term users introduces effective knowledge for encouraging continued Twitter use.
An the efficient user behavior model, which describes transitions of posting activities, is proposed. Two kinds of time longitudinal analysis method are evaluated using a large amount of actual tweets.
This work was supported by a Grant-in-Aid for scientific Research No. 25280110.
Yamaguchi, Y., Yamamoto, S. and Satoh, T. (2014), "Behavior analysis methods for Twitter users based on transitions in posting activities", International Journal of Web Information Systems, Vol. 10 No. 4, pp. 363-377. https://doi.org/10.1108/IJWIS-04-2014-0014Download as .RIS
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