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Description
This study investigates the politeness strategies employed by judges in American Idol 2023 and Vietnamese Idol 2023 through a contrastive pragmatic analysis supported by artificial intelligence (AI) tools. Drawing on Brown and Levinson’s (1987) politeness theory, the study examines how judges manage face-threatening acts when providing evaluations, criticism, and feedback to contestants. Data consist of judges’ interactions selected from audition and live-show episodes of both programs. A mixed-methods approach is adopted, combining qualitative discourse analysis with AI-assisted coding and classification of politeness strategies. The findings are expected to reveal both universal and culture-specific patterns in the use of positive politeness, negative politeness, off-record strategies, and bald-on-record acts. Furthermore, the study demonstrates the potential of AI-assisted analysis to enhance the efficiency, consistency, and scalability of pragmatic research. The paper contributes to cross-cultural pragmatics, media discourse studies, and emerging applications of AI in language research, offering methodological implications for future investigations of politeness in digitally mediated communication.
Keywords: Politeness strategies; Contrastive pragmatics; Artificial intelligence; Media discourse; Cross-cultural communication.
