用大模型分析社交媒体，发现用户认为预警及时就是准。
Leveraging LLMs and Social Media to Understand User Perception of Smartphone-Based Earthquake Early Warnings
- 用LLM分析500+条推文，提取42个用户感知属性
- 发现预警及时性与用户信任度强相关，超1分钟预警有效
- 提醒设计要兼顾工程精度与用户心理认知
2025年4月23日，土耳其马尔马拉埃尔格利西发生矩震级6.2级地震，是该地区25年来最强地震。安卓地震警报（AEA）系统在此次事件中向数百万用户成功发送了高精度早期预警，提前超过一分钟发出警报，为城市地区提供宝贵应对时间。本研究利用大语言模型（LLMs）分析来自X平台的500多条公开社交帖子，提取出42个与用户体验和行为相关的属性。统计分析显示，用户信任度与预警及时性存在显著正相关关系。研究揭示了工程定义的系统准确率与用户中心视角下的准确率之间的差异：在用户心中，及时即准确。研究结果为优化警报设计、开展公众教育以及未来行为研究提供了可操作建议，有助于提升地震活跃区智能手机预警系统的实际效果。
原文摘要 · Abstract (English)
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.
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