Determinants of Meme Popularity

Online social media have greatly affected the way in which we communicate with each other. However, little is known about what are the fundamental mechanisms driving dynamical information flow in online social systems. Here, we introduce a generative model for online sharing behavior and analytically show, using techniques from mathematical population genetics, that competition between memes for the limited resource of user attention leads to a type of self-organized criticality, with heavy-tailed distributions of meme popularity: a few memes “go viral” but the majority become only moderately popular. The time-dependent solutions of the model are shown to fit empirical micro-blogging data on hashtag usage, and to predict novel scaling features of the data. The presented framework, 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. Recent advances in communication technologies and the emergence of social media have made it possible to communicate rapidly on a global scale. However, since we receive pieces of information from multiple sources, this has also made the information ecosystem highly competitive: in fact, users’ influence and visibility are highly heterogeneous and memes or topics strive for users’ attention in online social systems. Although several studies have described the dynamics of information flow in popular communication media [1–5], the main factors determining the observed patterns have not been identified and there is no theoretical framework that addresses this challenge. Indeed, given the potential for applications—e.g., having more efficient systems to spread information for safety and preparedness in the face of threats—a better understanding of how memes (ideas, hashtags, etc.) emerge and compete in online social networks is critical. To address this problem, we develop a theoretical framework that describes how users choose among multiple sources of incoming information and affect the spreading of memes on a directed social network, like Twitter [1–3]. Our probabilistic model, in contrast to other studies [3–7] that