Designing Culturally Adaptive Multilingual Chatbots for Global User Engagement in Media Platforms

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Publisher: SMPTE — Pasadena, CA
Doc Type: Conference Paper
Content Type: Original Research
Volume: 00, pp. 1–19
Abstract: As media platforms globalize, organizations prioritize inclusive, personalized, culturally resonant experiences. Most of the current-day chatbots end up generating tone mismatches, poor localization fidelity and insensitive responses, and the main reason is the over-dependence on English-centric training data and superficial translation techniques. Through this work, we introduce a modular framework tailored to media and entertainment platforms that facilitate the design of culturally adaptive, multilingual chatbots. Seven key layers are incorporated into the proposed architecture: real-time language detection, translation and normalization, a cultural intelligence engine, multilingual NLU/NLG modules, sentiment and emotion detection, bias and safety filters, and a human feedback loop for continual learning. A working prototype that supports eight languages, including English, Hindi, Spanish, Korean, Arabic, and others, was built and evaluated across multiple use cases, such as movie recommendations, subscription support, and smart-TV navigation, leveraging open-source LLMs and fine-tuned NLP pipelines. As part of the evaluations, a pilot-scale deployment, compared to a simulated baseline, showed notable performance improvements: a 21% decrease in average handling time (AHT), an improvement in customer satisfaction (CSAT) to 87%, and a 38% increase in premium content purchases during culturally themed campaigns. Evaluation used automated metrics (BLEU, BERTScore) and human assessments of cultural appropriateness, tone, and localization. Results showed that integrating cultural intelligence into conversation AI design enhanced the authenticity and relevance of global user interactions. This paper presents a scalable blueprint for media technologists seeking to incorporate conversational AI into multilingual, multicultural environments.
Publication Date: 2025-10-13
ISBN: [object Object]
Author(s): Satya Karteek Gudipati
Keyword(s): Multilingual Chatbots, Bias Mitigation and Safety, Code-switching, Cultural Adaptation, Large Language Models (LLMs), Latency Budgets and Deployment, Localization, OTT
Copyright: © 2025 SMPTE

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<span class="citation">Satya Karteek Gudipati; <cite>Designing Culturally Adaptive Multilingual Chatbots for Global User Engagement in Media Platforms</cite>, MTS 2025, Article 30 (pp. 1 to 19); SMPTE, 2025, ISBN: [object Object]</span>

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Satya Karteek Gudipati; Designing Culturally Adaptive Multilingual Chatbots for Global User Engagement in Media Platforms, MTS 2025, Article 30 (pp. 1 to 19); SMPTE, 2025, ISBN: [object Object]

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Satya Karteek Gudipati; <cite id="bib-978-1-61482-966-9-30">Designing Culturally Adaptive Multilingual Chatbots for Global User Engagement in Media Platforms</cite>, MTS 2025, Article 30 (pp. 1 to 19); SMPTE, 2025, ISBN: [object Object]
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