ABSTRACT

Social networks are important communication channel where individuals and emergency agencies can exchange information during disasters. The ability to detect disaster information or ‘reporting’ tweets would provide many advantages in disaster management during crowded events. This study explores Twitter behaviour during the Mina stampede tragedy in the 2015 Hajj by processing tweets posted over seven days during and after the incident (24–30 September 2015). Statistical features were derived from tweets, such as the number of hashtags, user mentions, and links, to provide an overview of the use of Twitter during this disaster. A classification model was built to filter reporting tweets using two Arabic natural language processing tools: Farasa and MADAMIRA. A support vector machine with a radial basis function kernel generated the best results in both tools (F-score: 88%–89%). The results will be useful to those who manage large, crowded events such as Hajj in Arabic-speaking regions.

摘要

社交网络是个人和应急机构在灾难期间可以交换信息的重要沟通渠道。检测灾难信息或“报告”推文的能力将为拥挤事件期间的灾难管理提供许多优势。本研究通过处理事件发生期间和之后（2015 年 9 月 24 日至 30 日）7 天内发布的推文，探讨了 2015 年朝圣米娜踩踏悲剧期间的 Twitter 行为。统计特征来自推文，例如主题标签的数量、用户提及和链接，以提供在这场灾难中使用 Twitter 的概况。使用两种阿拉伯语自然语言处理工具：Farasa 和 MADAMIRA，构建了一个分类模型来过滤报告推文。具有径向基函数内核的支持向量机在这两种工具中产生了最好的结果（F 分数：88%–89%）。这些结果将对那些在阿拉伯语地区管理大型、拥挤的活动（如朝觐）的人有用。