Leveraging LLMs and Social Media to Understand User Perception of Smartphone-Based Earthquake Early Warnings

2026-03-24

AI Summary

This study investigates public perception and behavioral responses to smartphone-based earthquake early warning (EEW) systems during a major seismic event, using the 2025 Mw 6.2 Turkey earthquake as a case study. For the first time, large language models (LLMs) are employed to analyze user perceptions, leveraging over 500 publicly available posts from the X platform to extract 42 perceptual attributes. Through text mining and statistical correlation analysis, the research uncovers a user-centered cognitive paradigm—“timeliness equals accuracy”—demonstrating a strong positive correlation between warning timeliness and user trust. These findings challenge the conventional engineering-centric evaluation framework that prioritizes technical precision, offering empirical insights to inform the optimization of alert design, public education strategies, and behavioral interventions in EEW systems.

Technology Category

Data Mining & Knowledge Management: Conversational Systems for Recommendation & RetrievalHumans and AI: Human-Aware Planning and Behavior PredictionCognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationSearch and Retrieval-Augmented AI: Web evaluation methodologies and metricsSemantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Android's Earthquake Alert (AEA) system provided timely early warnings to millions during the Mw 6.2 Marmara Ereglisi, Türkiye earthquake on April 23, 2025. This event, the largest in the region in 25 years, served as a critical real-world test for smartphone-based Earthquake Early Warning (EEW) systems. The AEA system successfully delivered alerts to users with high precision, offering over a minute of warning before the strongest shaking reached urban areas. This study leveraged Large Language Models (LLMs) to analyze more than 500 public social media posts from the X platform, extracting 42 distinct attributes related to user experience and behavior. Statistical analyses revealed significant relationships, notably a strong correlation between user trust and alert timeliness. Our results indicate a distinction between engineering and the user-centric definition of system accuracy. We found that timeliness is accuracy in the user's mind. Overall, this study provides actionable insights for optimizing alert design, public education campaigns, and future behavioral research to improve the effectiveness of such systems in seismically active regions.

Problem

• Earthquake Early Warning
• User Perception
• Smartphone-based Alert
• Social Media Analysis
• Alert Timeliness

Innovation

• Large Language Models
• Earthquake Early Warning
• User Perception
• Social Media Analysis
• Alert Timeliness