ABSTRACT
Algorithmic sociology integrates machine learning, computational simulations, and extensive digital trace data to bridge microlevel social interactions with macro-level societal outcomes. By combining computational power with sociological theory, this approach enables researchers to identify patterns in large-scale social behavior while grounding findings in mechanisms, meaning, and context. The chapter highlights how machine learning classifications can prompt refinement of theoretical models, how agent-based simulations allow systematic testing of mechanism-driven explanations, and how critical perspectives enhance understandings of power, inequality, and algorithmic influence within digital infrastructures. A dedicated methods-and-ethics section addresses the challenges of ensuring that algorithmic approaches remain interpretable, replicable, and socially responsible. Key practices include designing explainable models that link predictive outputs to theory, conducting bias audits across diverse subpopulations, maintaining replicability even with restricted datasets, and implementing privacy-preserving workflows. The chapter also presents illustrative case sketches, covering predictive tasks such as information diffusion, voter turnout, and patterns of social inequality, where features informed by sociological theory enhance both model accuracy and interpretability. By combining computational analytics with reflexive, theoretically grounded interpretation, algorithmic sociology offers a pathway to study complex social systems at scale while preserving analytical rigor, ethical responsibility, and insight into the mechanisms driving social life.
