BRGM, Department of Risks and Prevention, BRGM, Orléans, France.
Lingua Custodia, Paris, France.
PLoS One. 2024 Oct 7;19(10):e0307254. doi: 10.1371/journal.pone.0307254. eCollection 2024.
When a fast kinetic natural disaster occurs, it is crucial that crisis managers quickly understand the extent of the situation, especially through the development of "big picture" maps. For many years, great efforts have been made to use social networks to help build this situational awareness. While there are many models for automatically extracting information from posts, the difficulty remains in detecting and geolocating this information on the fly so that it can be placed on maps. Whilst most of the work carried out to date on this subject has been based on data in English, we tackle the problem of detecting and geolocating natural disasters from French messages posted on the Twitter platform (now renamed "X"). To this end, we first build an appropriate dataset comprised of documents from the French Wikipedia corpus, the dataset from the CAp 2017 challenge, and a homemade annotated Twitter dataset extracted during French natural disasters. We then developed an Entity-Linking pipeline in adequacy with our end-application use case: real-time prediction and peak resiliency. We show that despite these two additional constraints, our system's performances are on par with state-of-the-art systems. Moreover, the entities geolocated by our model show a strong coherence with the spatiotemporal signature of the natural disasters considered, which suggests that it could usefully contribute to automatic social network analysis for crisis managers.
当快速动力学自然灾害发生时，危机管理者迅速了解情况的程度至关重要，特别是通过开发“全景图”地图。多年来，人们一直在努力利用社交网络来帮助建立这种态势感知。虽然有许多模型可以自动从帖子中提取信息，但仍然存在检测和实时地理定位此信息的困难，以便可以将其放置在地图上。虽然迄今为止在这个主题上进行的大部分工作都是基于英语数据，但我们解决了从 Twitter 平台（现在更名为“X”）上发布的法语消息中检测和地理定位自然灾害的问题。为此，我们首先构建了一个适当的数据集，该数据集由法语维基百科语料库、CAp 2017 挑战赛数据集和在法国自然灾害期间提取的自制标注 Twitter 数据集组成。然后，我们开发了一个与我们的端应用用例相适应的实体链接管道：实时预测和峰值弹性。我们表明，尽管有这两个额外的限制，我们的系统性能与最先进的系统相当。此外，我们模型地理定位的实体与所考虑的自然灾害的时空特征具有很强的一致性，这表明它可以为危机管理者的自动社交网络分析做出有用的贡献。
