Integrating Computational Techniques with Social Sciences: A Data-Driven Approach to  Human Behavior Analysis

Author(s)	Yogesh Chandra
Country	India
Abstract	The integration of computational techniques into the social sciences has opened a new frontier in the study of human behavior, societal dynamics, and cultural patterns. Traditionally, social sciences relied on qualitative approaches and small-scale surveys, which provided deep but limited insights. With the advent of big data, machine learning, natural language processing (NLP), and computational modeling, researchers now have tools to analyze human interactions, decision-making, and social phenomena at unprecedented scales. This paper explores how computational techniques enhance social science research by enabling predictive modeling, large-scale behavioral analysis, and simulations of complex systems. Case studies highlight applications in political science, psychology, and economics, while discussions address challenges such as ethical concerns, algorithmic bias, and data privacy. Ultimately, this integration signifies a paradigm shift, offering richer, evidence-based insights into human behavior while demanding responsible governance of computational tools in social contexts.
Keywords	Computational Social Science, Human Behavior Analysis, Big Data, Machine Learning, Natural Language Processing, Predictive Modeling, Social Dynamics
Field	Engineering
Published In	Volume 1, Issue 1, August 2025
Published On	2025-08-04

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American Journal of Computational and Interdisciplinary Research