The statistical mechanics of Twitter communities

Gavin Hall, William Bialek

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


We build models for the distribution of social states in Twitter communities. States can be defined by the participation versus 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.

Original languageEnglish (US)
Article number093406
JournalJournal of Statistical Mechanics: Theory and Experiment
Issue number9
StatePublished - 2021

All Science Journal Classification (ASJC) codes

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Statistics, Probability and Uncertainty


  • Critical phenomena of socio-economic systems
  • Inference in socioeconomic system
  • Socio-economic networks


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