GRAPH-THEORETIC AND STATISTICAL MODELS FOR DETECTING AND TRACING DEEPFAKE MEDIA IN INTERCULTURAL COMMUNICATION NETWORKS

Abstract

This study investigates the detection and tracing of deepfake media in intercultural communication networks through the integration of graph-theoretic modeling and statistical analysis. Drawing on empirical data and secondary scholarship, it demonstrates how detection accuracy varies across formats such as movie scripts, press releases, social media posts, and email campaigns. Findings reveal that detection outcomes are shaped not only by algorithmic precision but also by cultural trust structures and network resilience. Western clusters, characterized by decentralized communication patterns, exhibit stronger resistance to synthetic content, while hierarchical clusters show heightened vulnerability. By coupling mathematical modeling with cultural analysis, the study contributes a replicable methodology and offers insights for both academic and practical applications. It argues that effective deepfake detection requires the alignment of technical innovation with culturally responsive strategies.