Human and Social Analytics

Abstract

The rise of data analytics has transformed our understanding of human and social behavior by utilizing data from digital interactions, social platforms, and various other sources. This study explored the value of analytics techniques—sentiment analysis, network analysis, and predictive modeling—in capturing individual and collective behaviors. Such insights enable decision making in fields such as marketing, public health, social policy, and urban planning. However, challenges such as data bias, ethical considerations, and complexity of human behavior underscore the need for advanced methods and human oversight. To address these complexities, the proposed framework integrates multimodal sentiment analysis, context-aware network models, and adaptive predictive modeling. This comprehensive approach supports nuanced analysis that aids in real-time decision-making and promotes fair and transparent use of analytics in human and social contexts.