Statistical physics has been successfully applied to social dynamics, offering a framework to understand collective behaviors emerging from individual interactions. This review explores various aspects of social dynamics, including opinion, cultural, and language dynamics, crowd behavior, hierarchy formation, human dynamics, and social spreading. It highlights the connections between these phenomena and traditional statistical physics concepts, emphasizing the comparison of model results with empirical data. The review begins with an introduction to the general framework of statistical physics in social dynamics, focusing on concepts like order and disorder, topology, dynamical systems, and agent-based modeling. It then delves into opinion dynamics, discussing models such as the voter model, majority rule, social impact theory, Sznajd model, and bounded confidence models. Cultural dynamics are analyzed using the Axelrod model and its variants, while language dynamics are explored through evolutionary approaches and semiotic dynamics. The review also covers crowd behavior, hierarchy formation, human dynamics, and the coevolution of states and topology. It emphasizes the importance of empirical validation and the role of topology in social interactions. The review highlights the challenges in modeling social systems, including the need for realistic microscopic models and the complexity of interactions between agents. Key concepts discussed include the Ising model for understanding order-disorder transitions, the role of network topology in social dynamics, and the use of dynamical systems and agent-based modeling. The review also addresses the importance of bounded confidence in social interactions and the challenges in modeling social systems with realistic parameters. Overall, the review provides a comprehensive overview of the application of statistical physics to social dynamics, emphasizing the need for interdisciplinary approaches and empirical validation to understand complex social phenomena.Statistical physics has been successfully applied to social dynamics, offering a framework to understand collective behaviors emerging from individual interactions. This review explores various aspects of social dynamics, including opinion, cultural, and language dynamics, crowd behavior, hierarchy formation, human dynamics, and social spreading. It highlights the connections between these phenomena and traditional statistical physics concepts, emphasizing the comparison of model results with empirical data. The review begins with an introduction to the general framework of statistical physics in social dynamics, focusing on concepts like order and disorder, topology, dynamical systems, and agent-based modeling. It then delves into opinion dynamics, discussing models such as the voter model, majority rule, social impact theory, Sznajd model, and bounded confidence models. Cultural dynamics are analyzed using the Axelrod model and its variants, while language dynamics are explored through evolutionary approaches and semiotic dynamics. The review also covers crowd behavior, hierarchy formation, human dynamics, and the coevolution of states and topology. It emphasizes the importance of empirical validation and the role of topology in social interactions. The review highlights the challenges in modeling social systems, including the need for realistic microscopic models and the complexity of interactions between agents. Key concepts discussed include the Ising model for understanding order-disorder transitions, the role of network topology in social dynamics, and the use of dynamical systems and agent-based modeling. The review also addresses the importance of bounded confidence in social interactions and the challenges in modeling social systems with realistic parameters. Overall, the review provides a comprehensive overview of the application of statistical physics to social dynamics, emphasizing the need for interdisciplinary approaches and empirical validation to understand complex social phenomena.