Meme diffusion through mass social media

Online social media are rapidly complementing and even replacing person-to-person social contacts. The broadcast model of traditional mass media as channels for the diffusion of information is equally losing traction. As a result, online social networks and media have become a crucial turf on which public relations, marketing, and political battles are fought. Viral messages and political smear campaigns are increasingly designed specifically for social media. Here we propose to build a computational framework that will enable the study of meme diffusion in large-scale social media by collecting, analyzing, classifying, visualizing, and modeling massive streams of public micro-blogging data. While a number of websites have recently emerged that track popular and trending memes, such end-user services are not designed for scientific purposes. We aim to fill this gap by providing for the first time a large-scale infrastructure to delve deeply into a broad set of questions about how and why information spreads online. To enable this empirical analysis of meme diffusion, we will develop a unified framework that makes it possible to apply the same analytical methods to a broad variety of data feeds, e.g., Twitter, Google Buzz, Yahoo! Meme, and Facebook. In particular, the framework will model streams of social networking data as a series of events that represent interactions between actors and memes. As such it will facilitate comparison of the high-level statistical features across different Web 2.0 communities, the development of general models for the behavior of users, and models for the diffusion of ideas in a social network. The proposed project will thus span both technical and broad scientific challenges that lie at the intersection of computing and the social sciences. On the technical side, the framework will provide an unprecedented level of data interoperability for the real-time analysis of massive social media data streams on the order of millions of posts per day. More broadly, our framework will have tremendous impact on the emerging field of computational social science, greatly facilitating the study of the growing number of social phenomena that are mirrored online. Below are some key goals that our project will achieve.