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Social media plays a fundamental role in the diffusion of information. There are two different ways of information diffusion in social media platforms such as Twitter and Weibo. Users can either re-share messages posted by their friends or recreate messages based on the information acquired from other non-local information sources such as the mass media. By analyzing around 60 million messages from a large micro-blog site, we find that about 69 % of the diffusion volume can be attributed to users' re-sharing behaviors, and the remaining 31 % are caused by user recreating behaviors. The information diffusions caused by the two kinds of behaviors have different characteristics and variation trends, but most existing models of information diffusion do not distinguish them. The recent availability of massive online social streams allows us to study the process of Bin Cui

This paper presents a novel low-resolution phosphene visualization of depth and boundary computed by a two-layer Associative Markov Random Fields. Unlike conventional methods modeling the depth and boundary as an individual MRF respectively, our algorithm proposed a two-layer associative MRFs framework by combining the depth with geometry-based surface boundary estimation, in which both variables are inferred globally and simultaneously. With surface boundary integration, the experiments demonstrates three significant improvements as: 1) eliminating depth ambiguities and increasing the accuracy, 2) providing comprehensive information of depth and boundary for human navigation under low-resolution phosphene vision, 3) when integrating the boundary clues into downsampling process, the foreground obstacle has been clearly enhanced and discriminated from the surrounding background. In order to gain higher efficiency and lower computational cost, the work is initialized on segmentation based depth plane fitting and labeling, and then applying the latest projected graph cut for global optimization. The proposed approach has been tested on both Middlebury and indoor real-scene data set, and achieves a much better performance with significant accuracy than other popular methods in both regular and low resolutions.