Social media platforms have become fundamental tools for sharing information during natural disasters or catastrophic events. This paper presents SEDOM-DD (Sub-Events Detection on sOcial Media During Disasters), a new method that analyzes user posts to discover sub-events that occurred after a disaster (e.g., collapsed buildings, broken gas pipes, floods). SEDOM-DD has been evaluated with datasets of different sizes that contain real posts from social media related to different natural disasters (e.g., earthquakes, floods and hurricanes). Starting from such data, we generated synthetic datasets with different features, such as different percentages of relevant posts and/or geotagged posts. Experiments performed on both real and synthetic datasets showed that SEDOM-DD is able to identify sub-events with high accuracy. For example, with a percentage of relevant posts of 80% and geotagged posts of 15%, our method detects the sub-events and their areas with an accuracy of 85%, revealing the high accuracy and effectiveness of the proposed approach.

社交媒体平台已成为在自然灾害或灾难性事件期间共享信息的基本工具。本文介绍了 SEDOM-DD（灾难期间社交媒体上的子事件检测），这是一种分析用户帖子以发现灾难后发生的子事件（例如，倒塌的建筑物、破裂的煤气管道、洪水）的新方法。SEDOM-DD 已经使用不同大小的数据集进行了评估，这些数据集包含来自社交媒体的与不同自然灾害（例如，地震、洪水和飓风）相关的真实帖子。从这些数据开始，我们生成了具有不同特征的合成数据集，例如相关帖子和/或地理标记帖子的不同百分比。在真实数据集和合成数据集上进行的实验表明，SEDOM-DD 能够以高精度识别子事件。例如，