Data Science Applications in Social Behavioural Studies

Abstract

The rapid expansion of digital platforms, social networks, and sensor-driven technologies has resulted in an unprecedented volume of social data describing human behavior at individual, group, and societal levels. Data science, which integrates statistical analysis, machine learning, data mining, and computational modeling, has emerged as a transformative paradigm for understanding, predicting, and influencing social behavior. This paper presents a comprehensive investigation into the applications of data science in social behavioural studies, emphasizing theoretical foundations, methodological frameworks, and empirical insights. The study explores how structured and unstructured data—such as survey data, social media content, mobility traces, and transactional logs—are leveraged to analyze social interactions, behavioral patterns, opinion dynamics, and collective decision-making. A detailed literature survey highlights the evolution of computational social science and identifies key analytical approaches including predictive modeling, network analysis, sentiment analysis, and causal inference. The proposed methodology outlines a systematic pipeline for data acquisition, preprocessing, feature engineering, modeling, and validation, tailored to social behavioural contexts. Experimental results demonstrate the effectiveness of machine learning models in capturing behavioral trends, while the discussion addresses interpretability, ethical considerations, and societal implications. The paper concludes by identifying future research directions, emphasizing explainable artificial intelligence, ethical governance, and interdisciplinary collaboration as essential to advancing data-driven social behavioural research.