Mathematical AI-Driven Insights into Societal Dynamics and Resilience
Keywords: Societal behavior, resilience, multimedia content analysis, explainable AI, interpretable AI, Hindi cinema, community data, cultural studies, public policy
TL;DR: Mathematical AI-Driven Insights into Societal Dynamics
Abstract: The behavioral patterns of individuals within a society can serve as a reflection of its overall conditions and status. By examining these patterns, we can identify critical societal issues such as poverty, hunger, crime, sadness, underdevelopment, premature mortality, and social structure. Our research aims to understand the factors associated with these problems by leveraging societal data analytics. We explore societal behavior and resilience by analyzing Hindi cinema from 1951 onwards and comparing it with contemporary community data from developed countries. This is achieved through integrated interpretive and mathematical artificial intelligence (MAI) techniques, alongside natural language processing (NLP). The primary goal is to uncover and analyze the underlying societal norms, resilience patterns, and behavioral dynamics depicted in Hindi movies, and to validate these findings against real-world community data. The methodology begins with language processing techniques, such as term frequency analysis and contextual thematic analysis, to extract and quantify thematic variables from song lyrics. These variables capture fundamental societal issues like wealth disparity, human suffering, and societal despair. Additionally, Fourier and Laplace transforms are used for time-series analysis of audio signals and thematic continuity in video sequences. Game theory models are also applied to study decision-making processes and social interactions portrayed in the films. To ensure the transparency and interpretability of the MAI-driven insights, we employ explainable AI (XAI) approaches, including counterfactual explanations, feature visualization, and concept activation vectors (CAVs).From both the outcomes of MAI and XAI, we have noticed that there are some key features showing from both the data set and these are hunger, crimes, underdevelopment, poverty, and social structure. By treating multimedia content as a reflection of societal views and comparing it with empirical data, this research provides a core analysis of societal dynamics across different cultural contexts. Our proposed methods demonstrate higher accuracy compared to state-of-the-art approaches which ensure our findings more explainable and insightful.
Submission Number: 7
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