AI-Driven Analysis of Cross-Cultural Communication Patterns

Abstract

In the present study, the artificial intelligence capabilities are tested in assessing cross-cultural patterns of communication based on a mixed-method experimental design.  The data were collected in a multimodal form and included text transcripts, audio material, and paralinguistic cues of individuals of diverse cultural backgrounds.  Audio analysis and Natural Language Processing were applied on preprocessing pipelines, and to sort communication styles Support Vector Machines, Random Forests, and BiLSTMs were employed.  Statistical tests (ANOVA, correlation matrix) proved that considerable differences occurred between the cultural masses, and the attitude polarity, the use of pronouns, and the variation in intonation were discovered as the main discriminators.  The results reveal that BiLSTM achieved the best classification accuracy unlike SHAP interpretability that confirmed the relevance of the emotional and linguistic features on the prediction of cultural communication tactics.  There were also differences in visualizations of how polite people, how successful a conversation is, and how inter- and intracultural dynamics operate.  These findings were supported by qualitative theme analysis, further enriching culture and giving substantiating insight.  The findings of all the studies show that AI-based algorithms can be successfully used to detect and measure interculture communication strategies, thus both theorizing and practically introducing the field of communication, as well as utilizing such methodologies in education, business and foreign relations across the globe.