Effects of Network Structure, Competition and Memory Time on Social Spreading Phenomena

Abstract

Online social media has greatly affected the way in which we communicate with each other. However, little is known about what fundamental mechanisms drive dynamical information flow in online social systems. Here, we introduce a generative model for online sharing behavior that is analytically tractable and that can reproduce several characteristics of empirical micro-blogging data on hashtag usage, such as (time-dependent) heavy-tailed distributions of meme popularity. The presented framework constitutes a null model for social spreading phenomena that, in contrast to purely empirical studies or simulation-based models, clearly distinguishes the roles of two distinct factors affecting meme popularity: the memory time of users and the connectivity structure of the social network.

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Plain Language Summary

In the era of big data, online social networks offer unprecedented opportunities for studying collective human behavior. One important question pertains to the characteristics of human interactions that lead to some items of information (“memes”) becoming massively popular via online sharing. The standard approach to such a question involves large-scale longitudinal data analysis, which has yielded many important clues about underlying mechanisms. Here, we present the first modeling approach that provides insight into the distinct roles of network connectivity structure (who connects to whom) and the memory time of users (i.e., how far back users look in their Twitter streams).

The attention of users is a valuable commodity in both cyberspace and the real world, and competition between memes for attention leads to characteristic signatures in popularity distributions. We focus on nearly one million Twitter user IDs and the popularity of hashtags related to a protest movement that occurred in 2011 in Spain. We assume that all memes—which can be thought of as ideas or hashtags—are attractive to the same degree. We show that the resulting meme popularity distributions are fat tailed, limiting to power laws. Our analytically tractable model incorporates long memory times of users, which is an improvement over previous models. Our probabilistic model yields formulas that enable the model to be rapidly fitted to large-scale data from social networks.

We expect that our findings will provide insights into the fundamental drivers of popularity on social networks.