Abstract
Social media is an easy-to-access platform providing timely updates about societal trends and events. Discussions regarding epidemic-related events such as infections, symptoms, and social interactions can be crucial for informing policymaking during epidemic outbreaks. In our work, we pioneer exploiting Event Detection (ED) for better preparedness and early warnings of any upcoming epidemic by developing a framework to extract and analyze epidemic-related events from social media posts. To this end, we curate an epidemic event ontology comprising seven disease-agnostic event types and construct a Twitter dataset SPEED with human-annotated events focused on the COVID-19 pandemic. Experimentation reveals how ED models trained on COVID-based SPEED can effectively detect epidemic events for three unseen epidemics of Monkeypox, Zika, and Dengue; while models trained on existing ED datasets fail miserably. Furthermore, we show that reporting sharp increases in the extracted events by our framework can provide warnings 4-9 weeks earlier than the WHO epidemic declaration for Monkeypox. This utility of our framework lays the foundations for better preparedness against emerging epidemics.
Abstract (translated)
社交媒体是一个易于访问的平台，提供关于社会趋势和事件的及时更新。关于传染病相关事件（如感染、症状和社会互动）的讨论对于在疫情爆发期间进行政策制定至关重要。在我们的工作中，我们首创利用事件检测（ED）方法来更好地准备和早期预警即将到来的任何传染病。为此，我们构建了一个由七种疾病无关的事件类型组成的流行事件本体论，并构建了一个人类标注的Twitter数据集SPEED，以关注COVID-19大流行。实验表明，基于COVID的ED模型训练可以有效地检测出三种种类未见过的猴痘、登革热和黄热病等三个新的疫情；而基于现有ED数据集训练的模型则完全无法达到这种效果。此外，我们还证明了通过我们的框架提取的事件报告可以提前4-9周向世界卫生组织（WHO）颁布的疫情声明提供警告。这种基于我们的框架的更好的预防新兴传染病准备工作的基础。
URL
https://arxiv.org/abs/2404.01679
