From Ideas to Social Signals: Spatiotemporal Analysis of Social Media Dynamics

Social media activity analysis can provide an open window to the inception and evolution of ideas. In this paper, we introduce a general model of spatiotemporal evolution of an arbitrary number of ideas in social media. As the main theoretical contribution, we map user messages into a latent hidden field and derive a multidimensional social signal that encapsulates an arbitrary number of ideas. We then analyze the distance (in time and space) of individual ideas when compared to a general stream of ideas, thus allowing the characterization of the spatiotemporal behavior of individual idea trajectories. Finally, using Twitter data, we observe that the spatiotemporal behavior of ideas is contents dependent, that is, different ideas evolve differently in time and space. Consequently, we identify four major patterns of behavior of ideas in space (local vs. global) and time (rare vs. pervasive), which can be used to understand the spatiotemporal nature social media dynamics.