Modeling dynamics of meta-populations with a probabilistic approach: global diffusion in social media

Increasingly, diverse online social networks are locally and globally interconnected by sharing information in the Web ecosystem. Accordingly, emergent macro-level phenomena have been observed, such as global spread of news across different types of social media. Such real-world diffusion is hard to define with a single social platform alone since dynamic influences between heterogeneous social networks are not negligible. Also, the underlying structural property of networks is important, as it drives the diffusion process in a stochastic way. In this paper, we propose a macro-level diffusion model with a probabilistic approach by combining both heterogeneity and structural connectivity of social networks. As real-world phenomena, we take cases from news diffusion across News, social networking sites (SNS), and Blog media using the ICWSM'11 Spinn3r dataset which contains over 386 million Web documents covering a one-month period in early 2011. We find that influence between different media types is varied by context of information. News media are the most influential in the Arts and Economy categories, while SNS and Blog media are in the Politics and Culture categories, respectively. Also, controversial topics such as political protests and multiculturalism failure tend to spread concurrently across social media, while entertainment topics such as film releases and celebrities are likely driven by internal interactions within single social platforms. We expect that the proposed model applies to a wider class of diffusion phenomena in diverse fields including the social sciences, marketing, and neuroscience, and that it provides a way of interpreting dynamics of meta-populations in terms of strength and directionality of influences among them.