Advance in computing and communication technologies are revolutionizing our ability to understand and predict human social dynamics, both at the individual and population levels. Understanding the emergence of collective actions, however, is challenging because of the likely complex endogeneity of relationships of variables of interest. In this talk, I present a study on how people adopt emergent keywords (hashtags) in social media, based on a research framework utilizing exogenous shocks.
We examine the growth, survival, and context of novel hashtags during the 2012 U.S. presidential debates. Our analysis reveals the trajectories of hashtag use fall into two distinct classes: "winners" that emerge more quickly and are sustained for longer periods of time than other "also-rans" hashtags. We propose a theoretical framework to capture dynamics of hashtags based on their fitness, interactivity, diversity, and prominence. Statistical analyses of the growth and persistence of hashtags reveal novel relationships between features of this framework and the relative success of hashtags. Specifically, fitness always contributes to faster hashtag adoption, interactivity extends the life of "winners" while having no effect on "also-rans." Additional interactivity and diversity support the persistence of "winner" and "also-ran" hashtags, respectively. This is the first study on the lifecycle of hashtag adoption and use in response to purely exogenous shocks. We draw on theories of uses and gratification, organizational ecology, and language evolution to discuss these findings and their implications for understanding social influence and collective action in social media more generally.
Yu-Ru Lin is currently an Assistant Professor at the School of Information Sciences, University of Pittsburgh. She received her Ph.D. in Computer Science with a concentration in Arts, Media and Engineering from Arizona State University, where she received the 2011 Outstanding Ph.D. Student Award in Computer Science. She was a postdoctoral research fellow at Institute for Quantitative Social Science, Harvard University and College of Computer and Information Science, Northeastern University. Her research interests include human mobility, social and political network dynamics, and computational social science. She has developed computational approaches for mining and visualizing large-scale, time-varying, heterogeneous, multi-relational, and semi-structured data. Her current research focuses on the emergence of collective behavior and civic engagement through social media and mobile devices. Her work has appeared in prestigious scientific venues including WWW, SIGKDD, InfoVis, ACM TKDD, ACM TOMCCAP, IEEEP and PLoS ONE.