The rapid growth of big data and the advancement of machine learning algorithms in recent years have paved the way for a significant conceptual and methodological revolution in sociology. This study introduces and implements an innovative framework based on transfer learning and multi-environmental data collection, comprehensively examining the potential of modern algorithms in analyzing social issues across diverse cultural and economic contexts. Unlike previous studies, which were mostly limited to a single society or specific dataset, the proposed approach leverages a combination of supervised and transfer learning models, enabling the generalization of findings from one society to another.
Data was gathered from diverse cross-cultural sources and, after adaptive preprocessing, algorithms such as Random Forest, Neural Networks, Support Vector Machines, and Logistic Regression were applied. The results not only demonstrate the superior performance of these algorithms in heterogeneous social environments but also highlight the relationship between cultural, economic variables, and the interpretability of predictive models.
The key innovations of this research lie in the use of transfer learning, cross-societal validation, and cultural sensitivity analysis of algorithms, which can open new avenues for data-driven studies in sociology and serve as a foundation for data-informed policymaking and social justice.