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.

翻译：安卓地震警报（AEA）系统在2025年4月23日土耳其马尔马拉埃雷利西6.2级地震期间为数百万用户提供了及时预警。此次地震是当地25年来最强震级，成为智能手机地震预警（EEW）系统的一次关键性实战检验。AEA系统以高精度向用户发送警报，在最强震动抵达城区前提供了超过一分钟的预警时间。本研究利用大语言模型（LLMs）分析了X平台上500余条公共社交媒体帖子，提取了42个与用户体验和行为相关的独特属性。统计分析揭示了显著关联性，特别是用户信任与预警及时性之间的强相关性。结果表明：工程学定义的系统精度与以用户为中心的精度定义存在差异——在用户认知中，"及时性"即为"准确性"。总体而言，本研究为优化警报设计、公众教育宣传及未来行为研究提供了可操作见解，有助于提升此类系统在地震活跃区域的效能。