Situational awareness extraction: a comprehensive review of social media data classification during natural hazards
Situational awareness extraction: a comprehensive review of social media data classification during natural hazards
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DOI：
10.1080/19475683.2020.1817146
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发表时间：
2020-10
期刊：
影响因子：
5
通讯作者：
Jirapa Vongkusolkit;Qunying Huang
中科院分区：
文献类型：
--
作者：
Jirapa Vongkusolkit;Qunying Huang
摘要社交媒体（例如，Twitter和Facebook）可被视为灾害期间改善态势感知和灾害管理的重要信息来源，因为它们在发生灾害时迅速传播信息方面发挥着重要作用。由于数据量远远超出人工检查的能力，现有的工作利用基于关键字的自然语言处理方法，或者依赖于从文本和其他元数据（例如，用户简档）来提取对SA有贡献的社交媒体数据并将它们自动分类到相关类别（例如，捐赠和捐赠）。然而，分类模式和相关信息提取方法的设计远不那么简单，并且高度依赖于：（1）事件类型，（2）研究或分析目的，以及（3）所使用的社交媒体平台。为此，本文回顾了文献提取社会媒体数据，并提供了一个概述的分类模式，已被用来评估SA在涉及自然灾害的事件，从五个不同的分析角度（内容，时间，用户，情绪和时空）通过讨论流行的主题类别，灾害事件类型，研究的目的，并利用每个模式的平台。最后，本文总结了每种灾害事件类型最常用的分类方法和平台，并概述了研究议程，为未来的创新提出了建议。
ABSTRACT Social media (e.g., Twitter and Facebook) can be regarded as vital sources of information during disasters to improve situational awareness (SA) and disaster management since they play a significant role in the rapid spread of information in the event of a disaster. Due to the volume of data is far beyond the capabilities of manual examination, existing works utilize natural language processing methods based on keywords, or classification models relying on features derived from text and other metadata (e.g., user profiles) to extract social media data contributing to SA and automatically categorize them into the relevant classes (e.g., damage and donation). However, the design of the classification schema and the associated information extraction methods are far less than straightforward and highly depend on: (1) the event type, (2) the study or analysis purpose, and (3) the social media platform used. To this end, this paper reviews the literature for extracting social media data and provides an overview of classification schemas that have been used to assess SA in events involving natural hazards from five different analytical perspectives (content, temporal, user, sentiment, and spatiotemporal) by discussing the prevalent topic categories, disaster event types, purpose of studies, and platforms utilized from each schema. Finally, this paper summarizes classification methods, and platforms that are most commonly used for each disaster event type, and outlines a research agenda with recommendations for future innovations.