There is Something Beyond the Twitter Network

Abstract

How information spreads through a social network? Can we assume, that the information is spread only through a given social network graph? What is the correct way to compare the models of information flow? These are the basic questions we address in this work. We focus on meticulous comparison of various, well-known models of rumor propagation in the social network. We introduce the model incorporating mass media and effects of absent nodes. In this model the information appears spontaneously in the graph. Using the most conservative metric, we showed that the distribution of cascades sizes generated by this model fits the real data much better than the previously considered models.Comment: 8 pages, 2 figures, Hypertext 201

Why Do Cascade Sizes Follow a Power-Law?

“…In future, theoretical studies on cascades size distribution could explain phenomena in Gossip-based routing [10], rumor virality and influence maximization [14], recurrence of the cascades [7] or assist in forecasting rumors [15]. Currently the research in this area is purely empirical and we need more theoretical models to understand the process of information propagation [20].…”

“…Based on that, we generate a directed graph of the information transmission (same as [27,8]). We use data published in [20,22] and publish our code and experimental results on [21].…”

“…K-S test comparing the power-law distribution and the real cascade size distribution [22] is 0.0145. At [20], the the K-S test comparing the cascade size distribution on the variant of CGM with real cascade distribution is 0.0447. It means, that our model can potentially improve the test value by 3% in comparison to the CGM on the graph of retweets.…”

“…One of the greatest issue in analyzing the cascade size distribution is the lack of a good theoretical background for this process. Recently, researchers [20] has presented new models, designed to fit real distribution of cascade sizes. Moreover, goodness of fit test against CGM model has been conducted.…”

“…It turns out, that CGM works well for small cascades, however it is unreliable for the large ones. The introduction of time dependent parameters significantly improves predictions for the large cascades [20].…”