One of the persistent challenges in crisis detection is inferring actionable information to support emergency response. Existing methods focus on situational awareness but often lack actionable insights. This study proposes a holistic approach to implementing an actionability extraction system on social media, including requirement gathering, selection of machine learning tasks, data preparation, and integration with existing resources, providing guidance for governments, civil services, emergency workers, and researchers on supplementing existing channels with actionable information from social media. Our solution leverages an actionability schema and domain-adaptive pre-training, improving upon the state-of-the-art model by 5.5 % and 10.1 % in micro and macro F1 scores.

中文翻译：

---

通过可操作性事件图式和域自适应预训练赋能危机信息提取


危机检测中持续存在的挑战之一是推断可操作的信息以支持应急响应。现有方法侧重于态势感知，但通常缺乏可操作的见解。本研究提出了一种在社交媒体上实施可操作性提取系统的整体方法，包括需求收集、机器学习任务的选择、数据准备以及与现有资源的整合，为政府、公务员、应急工作人员和研究人员提供指导，以补充来自社交媒体的可操作信息补充现有渠道。我们的解决方案利用可操作性架构和域自适应预训练，在最先进的模型的基础上，将微观和宏观 F1 分数提高了 5.5% 和 10.1%。