Emőke-Ágnes Horvát (Northwestern University): Diffusion of Scientific Articles across Online Platforms
Abstract | Most scholars now use online platforms such as social media sites, electronic news outlets, blogs, and wikis for sharing their findings. They have also become the primary source of information about scientific advances for the wider public. As the online dissemination of scientific content increasingly influences personal decision-making and government action, there is a growing interest in studying how people share research findings online. In this talk, I report results on analyses of the diffusion of scientific articles across major online platforms based on 63 million mentions of 7.2 million research articles over seven years. First, I show commonalities between people sharing science and other content such as news articles and memes. Second, I explore specifics of sharing science. We reconstruct the likely underlying structure of information diffusion and investigate the transfer of information about scientific articles within and across different platforms. In particular, we study the role of different users in the dissemination of information to understand better who are the prime sharers of knowledge. Then, we explore the propagation of articles between platforms. Finally, we analyze the structural virality of individual information cascades to place science sharing on the spectrum between pure broadcasting and actual peer-to-peer diffusion. This work provides the broadest study to date about the sharing of science online and builds the basis for an informed model of the dynamics of research coverage across platforms.
Bio | Emőke-Ágnes Horvát is an Assistant Professor in the Department of Communication Studies at Northwestern University, the Computer Science Department of the McCormick School of Engineering, and the Department of Management and Organizations of the Kellogg School of Management. Her research at the intersection of computational social science and social computing develops network and big data methods to understand and support collective intelligence in Web-based systems.