The infection times of individuals in online information spread such as the inter-arrival time of Twitter messages or the propagation time of news stories on a social media site can be explained through a convolution of lognormally distributed observation and reaction times of the individual participants. Experimental measurements support the lognormal shape of the individual contributing processes, and have resemblance to previously reported lognormal distributions of human behavior and contagious processes.

The key feature of online social networks (OSN) is the ability of users to become ac>ve, make friends and interact via comments, videos or messages with those around them. This social interac>on is typically perceived as cri>cal to the proper func>oning of these plaSorms; therefore, a significant share of OSN research in the recent past has inves>gated the characteris>cs and importance of these social links, studying the networks' friendship rela>ons through their topological proper>es, the structure of the resul>ng communi>es and iden>fying the role and importance of individual members within these networks.In this paper, we present results from a mul>--year study of the online social network Digg.com, indica>ng that the importance of friends and the friend network in the propaga>on of informa>on is less than originally perceived. While we do note that users form and maintain a social structure along which informa>on is exchanged, the importance of these links and their contribu>on is very low: Users with even a nearly iden>cal overlap in interests react on average only with a probability of 2% to informa>on propagated and received from friends. Furthermore, in only about 50% of stories that became popular from the en>re body of 10 million news we find evidence that the social >es among users were a cri>cal ingredient to the successful spread. Our findings indicate the presence of previously unconsidered factors, the temporal alignment between user ac>vi>es and the existence of addi>onal logical rela>onships beyond the topology of the social graph, that are able to drive and steer the dynamics of such OSNs.

While enabling new research questions and methodologies, the massive size of social media platforms also poses a significant issue for the analysis of these networks. In order to deal with this data volume, researchers typically turn to samples of these graph structures to conduct their analysis. This however raises the question about the representativeness of such limited crawls, and the amount of data necessary to come to stable predictions about the underlying systems. This paper analyzes the convergence of six commonly used topological metrics as a function of the crawling method and sample size used. We find that graph crawling methods drastically over-and underestimate network metrics, and that a non-trivial amount of data is needed to arrive at a stable estimate of the underlying network.

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Abstract. We analyse the number of votes, called the digg value, which measures the impact or popularity of submitted information in the Online Social Network Digg. Experiments over five years indicate that the digg value of a story on the first frontpage follows closely a lognormal distribution. While the law of proportionate effect explains lognormal behavior, the proportionality factor a in that law is assumed to have a constant mean, whereas experiments show that a decreases linearly with time. Our hypothesis, the probability that a user diggs (votes) on a story given that he observes a certain digg value m equals a × m, can explain observations, provided that the population of users that can digg on that story is close to a Gaussian.