Maximum Entropy Frameworks for Online Social Networks 

The study of complex systems requires rigorous statistical foundations to distinguish genuine structural patterns from stochastic noise. This work explores the construction of Maximum Entropy Frameworks for complex networks, providing a principled methodology for their validation across diverse fields. We provide a comprehensive overview of these models, discussing their computational implementation, theoretical strengths, and limitations to ensure statistically validated results. Building on this theoretical foundation, the analysis extends to Computational Social Science, using bipartite network projections to decode the mechanics of digital discourse. A detailed study of polarization and echo chambers on Reddit, a platform often perceived as less susceptible to such dynamics, serves to test the sensitivity of these validation models. Furthermore, we show how user involvement drives linguistic complexity on Twitter, using these statistical tools to map modern social dynamics.