  The evolution of language has been a hotly debated subject with contradicting
hypotheses and unreliable claims. Drawing from signalling games, dynamic
population mechanics, machine learning and algebraic topology, we present a
method for detecting evolutionary patterns in a sociological model of language
evolution. We develop a minimalistic model that provides a rigorous base for
any generalized evolutionary model for language based on communication between
individuals. We also discuss theoretical guarantees of this model, ranging from
stability of language representations to fast convergence of language by
temporal communication and language drift in an interactive setting. Further we
present empirical results and their interpretations on a real world dataset
from \rdt to identify communities and echo chambers for opinions, thus placing
obstructions to reliable communication among communities.
