Online social network (OSN) is an important part of cyber physical system (CPS). In OSN, micro-blogging has grown rapidly to a popular online social network recently and provides a large number of real-time tweets for users. With the popularity of micro-blogging and the increase of active users, many users are faced with an information overload problem, especially for those with many followees and thousands of tweets arriving every day. In this paper, we aim to investigate the problem of recommending valuable tweets that users are really interested in personally, so as to reduce their efforts to find useful information. We consider three major aspects in our proposed ranking model, including the popularity of a tweet itself, the intimacy between the user and the tweet publisher, and the interest fields of the user. The detailed indicators for each aspect are introduced by analyzing users' behaviors and their meanings on micro-blogs. The experimental results show that the proposed model can help improve the ranking performance in precision and greatly outperform several baseline methods.

Recently, microblog services accelerate the information propagation among peoples, leaving the traditional media like newspaper, TV, forum, blogs, and web portals far behind. Various messages are spread quickly and widely by retweeting in microblogs. In this paper, we take Sina microblog as an example, aiming to predict the possible number of retweets of an original tweet in one month according to the time series distribution of its topnretweets. In order to address the problem, we propose the concept of a tweet’s lifecycle, which is mainly decided by three factors, namely, the response time, the importance of content, and the interval time distribution, and then the given time series distribution curve of its topnretweets is fitted by a two-phase function, so as to predict the number of its retweets in one month. The phases in the function are divided by the lifecycle of the original tweet and different functions are used in the two phases. Experiment results show that our solution can address the problem of predicting the times of retweeting in microblogs with a satisfying precision.

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