As one kind of social media, microblogs are widely used for sensing the real-world. The popularity of mi- croblogs is an important measurement for evaluation of the influencial of pieces of information. The models and mod- eling techniques for popularity of microblogs are studied in this paper. A huge data set based on Sina Weibo, one of the most popular microblogging services, is used in the study. First, two different types of popularity, namely number of retweets and number of possible views are defined, while their relationships are discussed. Then, the temporal dynamics, in- cluding lifecycles and tipping-points, of tweets＇ popularity are studied. For modeling the temporal dynamics, a piece- wise sigmoid model is used. Empirical studies show the ef- fectiveness of our modeling methods.
Research papers are of great importance for researchers to record and share their ideas,studies and results. Meanwhile,they are the bases for further research work. Finding related research papers is the preliminary of good research. A new method for ranking related research papers is introduced in this paper. Given a set of interested papers,the method proposed in this paper may rank related papers just based on a citation graph by constructing the minimal Steiner trees. The method can work with the existing keyword based methods. Intensive experiments on a specific research topic show that the proposed method provides a different ranking criterion to existing ones,and can effectively retrieve those highly related papers.
