AHFE Open AccessHuman-Centered AI for Exploring Global Framing Patterns in Climate Disaster News
Abstract
Climate disaster news shapes public understanding of global events, yet coverage of the same disaster often varies across regions and media outlets. Differences in framing (e.g., tone, narrative emphasis, source selection) influence how responsibility, urgency, and impact are perceived. At the same time, the growing volume of digital news makes systematic comparison difficult. While natural language processing enables automated analysis, results are typically presented as abstract labels that are difficult for non-experts to interpret. This paper presents an interactive, human-centered AI system for exploring global framing patterns in climate disaster news. The system integrates automated article retrieval, content extraction, AI-based analysis, event-level grouping, and interactive visualizations that support cross-source and cross-regional comparison. A central contribution is the design of AI-generated explanations using progressive disclosure, providing optional interpretive support while maintaining a concise interface. This approach aims to reduce cognitive load and avoid overreliance on AI outputs. An empirical user study investigates how explanations support understanding and reasoning processes, as well as the identification of cross-regional framing patterns. Positioned at the intersection of HCI, explainable AI, and media literacy, this work contributes design insights for supporting reflective engagement with AI-driven analysis.
Keywords: Human-Centered Design, Ux, Explainable Ai, Interactive Visualization, Comparative Analysis, Media Literacy
DOI: 10.54941/ahfe1008167
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