2016/03/10 by Rahmtin Rotabi, Jon Kleinberg, Rotabi, Rahmtin +1
Computer Science · Physics and Astronomy · #Computers and Society (cs.CY) #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI) #cs.CY #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1603.03303
arxiv created 2016/03/10 · arxiv updated 2016/03/11
An active line of research has studied the detection and representation of trends in social media content. There is still relatively little understanding, however, of methods to characterize the early adopters of these trends: who picks up on these trends at different points in time, and what is their role in the system? We develop a framework for analyzing the population of users who participate in trending topics over the course of these topics' lifecycles. Central to our analysis is the notion of a "status gradient", describing how users of different activity levels adopt a trend at different points in time. Across multiple datasets, we find that this methodology reveals key differences in the nature of the early adopters in different domains.