社交媒体数据在自然灾害应急管理中的应用研究综述

社交媒体是灾害应急管理的一个新兴但尚未充分利用的大数据源。然而,社交媒体数据在灾害应急管理中能发挥什么作用,目前国内的研究较少,而国外的研究亦较为分散,相关应用受限。论文对国内外利用社交媒体数据进行灾害管理相关研究进行了综述,归纳出社交媒体数据在灾害应急管理中应用的潜力、优势和问题。结果发现：① 社交媒体数据含有丰富的灾害信息,能够依靠态势感知和信息共享支持灾害预警、实时监测、损失和救助需求评估、协助快速应急响应以及预测可能的灾害风险等灾害管理工作;② 社交媒体数据的优势在于能保证灾害信息的实时性和连续性,进而提供致灾强度图、损失预测和舆情分析等结果服务于灾害应急管理;③ 地理位置信息有限、专业语料库缺乏、信息噪声处理的复杂性等原因使社交媒体数据分析结果精度受限。如何发挥社交媒体数据社会感知的优势,以弥补传统观测调查手段的不足,更加科学应对灾害应急,仍需要在社交媒体数据处理及多源数据融合分析技术方面取得突破。

A review on the application of social media data in natural disaster emergency management

Social media is a data source that is emerging but underutilized for disaster emergency management. There are few existing studies on the role of social media data in disaster emergency management in China, and international studies are also relatively scattered, which make relevant applications limited. This article reviewed the research on disaster management with social media data. We summarized the potential, advantages, and problems of applying social media data in disaster emergency management. The results show that: 1) Social media data are full of disaster information. They can support disaster early warning, real-time monitoring, and loss and rescue needs assessment, assist rapid emergency response, and predict possible disaster risks that relies on situational awareness and timely information. 2) The advantage of social media data is that it can ensure the timeliness and continuity of disaster information aquisition, which can provide disaster intensity maps, loss prediction, and public opinion analysis for disaster emergency management. 3) Some problems limit the accuracy of social media data analysis results, including limited geographical location information, lack of professional corpus, and complexity of information noise processing, among others. In order to make full use of the advantages of social media data to overcome the shortcomings of traditional observation and investigation methods and respond to disasters more scientifically, breakthroughs in social media data processing and multi-source data fusion analysis technology are needed.