  We build models for the distribution of social states in Twitter communities.
States can be defined by the participation vs silence of individuals in
conversations that surround key words, and we approximate the joint
distribution of these binary variables using the maximum entropy principle,
finding the least structured models that match the mean probability of
individuals tweeting and their pairwise correlations. These models provide very
accurate, quantitative descriptions of higher order structure in these social
networks. The parameters of these models seem poised close to critical surfaces
in the space of possible models, and we observe scaling behavior of the data
under coarse-graining. These results suggest that simple models, grounded in
statistical physics, may provide a useful point of view on the larger data sets
now emerging from complex social systems.
