Disaster Management From Social Media Using Machine Learning
Disaster Management From Social Media Using Machine Learning
复制标题
使用机器学习从社交媒体进行灾害管理
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
C. Vishnu Hari
中科院分区：
文献类型：
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作者：
S. Sindhu;Dheeraj S Nair;V. S. Maya;M. T. Thanseeha;C. Vishnu Hari
近年来，自然灾害造成了悲惨的人类损失，并对基础设施造成了不可预测的破坏。确定损失和伤亡的过程使第一反应者能够有效地分配资源，尽可能多地挽救生命。灾害管理进程的成功在很大程度上取决于能否获得关于灾害状况、受灾民众和周边地区环境的准确和及时的信息。社交媒体是灾难发生过程中的沟通方式之一。在这类灾难中，旁观者和受影响的人会发布即时更新，包括死亡人数或受伤人员报告、基础设施损坏、对食品、水、住所等急需品的请求、捐赠提议等。社交媒体图像是相关信息的宝贵来源。然而，大量的帖子和不相关帖子的存在使得应急人员无法手动挖掘帖子以获得可信信息。因此，从社交媒体上收集的帖子中自动提取信息的过程对于充分利用这种丰富的数据至关重要。本文主要讨论如何对社交媒体平台上的图像内容进行去噪和分类，以帮助人道主义组织获得态势感知、更新和开展救援行动。
In recent years natural calamities and disasters resulted in tragic loss of mankind and caused unpredictable infrastructural damages. The process of identifying damage and casualties allows first responders to efficiently allocate resources and save as many lives as possible. The success of a disaster management process is largely dependent on getting the accurate and timely information about the disaster status, the affected people and the environment in the surrounding areas. Social media is one of the ways of communication in the course of catastrophes. During such catastrophe, bystanders and affected people post instant updates including death toll or reports of injured people, infrastructure damage, requests for immediate necessities like food, water, shelter, donation offers and so on. Social media images are a valuable source of relevant information. However, the huge number of posts and presence of irrelevant posts makes it impossible for emergency responders to manually mine posts for credible information. Therefore, automating the process of information extraction from the posts which are already collected from social media is essential to fully take advantage of this abundance of data. This paper mainly discusses about denoising, and classifying imagery content from social media platforms to help humanitarian organizations in gaining situational awareness, updates and launching relief operations.