ABSTRACT
C entralized with users being the creators and propagators, social networks tend to be an indispensable part of modern people’s lives, in the
era of Web 2.0. Massive amount of users’ thoughts and friendships are implied in social networks, which becomes a promising source of big data. One of the most significant meanings for data mining is to analyze the underlined relations among data, and use it for the future. In a social network, the limitation of users’ time and attention determines that users will only focus on what they are interested in and what is popular for the time being. Predicting what is popular in time will not only improve the utilization of users’ time and attention, but also benefit social websites to offer better services to their users. In this chapter, we intend to research the popularity prediction of textual content, using big data in social networks. We focus on methods and models of prediction, which are well classified by elements the models consider, such as user behaviors, the life cycles of information, and the social network topology. We also reveal researchers’ work on classifying social networks, evaluating metrics, as well as feature selection, and what remains to be done.
