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Studies of human attention dynamics analyses how attention is focused on specific topics, issues or people. In online social media, there are clear signs of exogenous shocks, bursty dynamics, and an exponential or powerlaw lifetime distribution. We here analyse the attention dynamics of traditional media, focussing on co-occurrence of people in newspaper articles. The results are quite different from online social networks and attention. Different regimes seem to be operating at two different time scales. At short time scales we see evidence of bursty dynamics and fast decaying edge lifetimes and attention. This behaviour disappears for longer time scales, and in that regime we find Poissonian dynamics and slower decaying lifetimes. We propose that a cascading Poisson process may take place, with issues arising at a constant rate over a long time scale, and faster dynamics at a shorter time scale.
Although the many forms of modern social media have become major channels for the dissemination of information, they are becoming overloaded because of the rapidly-expanding number of information feeds. We analyze the expanding user-generated content
Recent wide-spread adoption of electronic and pervasive technologies has enabled the study of human behavior at an unprecedented level, uncovering universal patterns underlying human activity, mobility, and inter-personal communication. In the presen
Online traces of human activity offer novel opportunities to study the dynamics of complex knowledge exchange networks, and in particular how the relationship between demand and supply of information is mediated by competition for our limited individ
Daily interactions naturally define social circles. Individuals tend to be friends with the people they spend time with and they choose to spend time with their friends, inextricably entangling physical location and social relationships. As a result,
A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been ado