Islamic Culture and Relations Organization Spektrum Iran 0934-358X 38 2 2025 07 01 Affective asymmetries in AI: Sentiment bias between English and Persian in harmonized LLM pipelines Affective asymmetries in AI: Sentiment bias between English and Persian in harmonized LLM pipelines 143 157 241451 10.22034/spektrum.2026.563602.1052 DE Michael Totaro Department of Computer Science & Informatics, University of Louisiana at Lafayette, USA Leila Gheisi PhD student in Communication, University of Louisiana at Lafayette, USA 0009-0007-6885-9566 Ehsan Shahghasemi Associate Professor, Department of Communication, The University of Tehran, Tehran, Iran 0000-0002-8716-5806 Journal Article 2025 03 05 In the contemporary landscape of crisis management, decision-makers are increasingly overwhelmed by the sheer volume, velocity, and variety of media data generated during emergencies. Traditional manual analytical methods are often insufficient to process this influx effectively, necessitating a paradigm shift toward advanced computational approaches. The primary goal of this study is to bridge the gap between technical data science and practical crisis communication by establishing a clear analytical link between specific machine learning (ML) paradigms and their operational capabilities. This article utilizes a narrative review methodology, underpinned by a theoretical framework grounded in machine learning. The study systematically synthesizes existing literature to categorize and analyze how distinct ML architectures—specifically supervised, unsupervised, and deep learning—are applied within the domain of media data analysis to support decision-making processes during crises. The analysis confirms that artificial intelligence significantly enhances crisis management effectiveness by automating media monitoring and generating actionable real-time insights. The findings delineate specific roles for different algorithms: supervised learning serves as the theoretical foundation for rapid misinformation detection and precise crisis classification. Conversely, unsupervised learning and deep learning are identified as critical tools for detecting data anomalies and recognizing emerging patterns, which are essential for the functionality of proactive early warning systems. While AI offers transformative potential, this study provides a critical reflection on significant implementation challenges. It highlights the “black box” problem—characterized by a lack of algorithmic interpretability—and inherent data biases as major ethical hurdles that can compromise accountability and fairness in crisis response. The present study contributes a structured framework for understanding AI’s role through a theoretical lens. It concludes that future implementation must prioritize explainable AI to balance computational efficiency with ethical responsibility. In the contemporary landscape of crisis management, decision-makers are increasingly overwhelmed by the sheer volume, velocity, and variety of media data generated during emergencies. Traditional manual analytical methods are often insufficient to process this influx effectively, necessitating a paradigm shift toward advanced computational approaches. The primary goal of this study is to bridge the gap between technical data science and practical crisis communication by establishing a clear analytical link between specific machine learning (ML) paradigms and their operational capabilities. This article utilizes a narrative review methodology, underpinned by a theoretical framework grounded in machine learning. The study systematically synthesizes existing literature to categorize and analyze how distinct ML architectures—specifically supervised, unsupervised, and deep learning—are applied within the domain of media data analysis to support decision-making processes during crises. The analysis confirms that artificial intelligence significantly enhances crisis management effectiveness by automating media monitoring and generating actionable real-time insights. The findings delineate specific roles for different algorithms: supervised learning serves as the theoretical foundation for rapid misinformation detection and precise crisis classification. Conversely, unsupervised learning and deep learning are identified as critical tools for detecting data anomalies and recognizing emerging patterns, which are essential for the functionality of proactive early warning systems. While AI offers transformative potential, this study provides a critical reflection on significant implementation challenges. It highlights the “black box” problem—characterized by a lack of algorithmic interpretability—and inherent data biases as major ethical hurdles that can compromise accountability and fairness in crisis response. The present study contributes a structured framework for understanding AI’s role through a theoretical lens. It concludes that future implementation must prioritize explainable AI to balance computational efficiency with ethical responsibility. https://www.spektrumiran.com/article_241451_97c37be7afe5bdce870519d22e10b2fb.pdf