利用人工智能从社交媒体获取可靠情报,支持人道主义危机决策

IF 0.7 4区 管理学 Q4 PUBLIC ADMINISTRATION Journal of Homeland Security and Emergency Management Pub Date : 2023-03-23 DOI:10.1515/jhsem-2021-0042
Christopher Garcia, G. Rabadi, Diana Abujaber, M. Seck
{"title":"利用人工智能从社交媒体获取可靠情报,支持人道主义危机决策","authors":"Christopher Garcia, G. Rabadi, Diana Abujaber, M. Seck","doi":"10.1515/jhsem-2021-0042","DOIUrl":null,"url":null,"abstract":"Abstract Recent advances in the field of artificial intelligence bring promising new capabilities that can substantially improve our ability to manage complex and evolving situations in the face of uncertainty. Humanitarian crises exemplify such situations, and the pervasiveness of social media renders it one of the most abundant sources of real-time information available. However, it is quite a difficult task to condense a body of social media posts into useful information quickly. In this paper we consider the challenge of using social media reports to provide a reliable, real-time situational awareness in the management of humanitarian crises. Effectively addressing this challenge requires extracting only the relevant information out of text and images in individual social media posts, fusing this information together into actionable information points for decision makers, and providing an assessment of the trustworthiness of this information. We propose a general solution framework and discuss a system developed in collaboration with NATO which combines state-of-the-art deep learning, natural language processing, computer vision, and information fusion models to provide a reliable, actionable, real-time situational awareness for supporting decision making in humanitarian crisis logistics. In addition to the technical approach, we also discuss important practical aspects of this project including the development and validation process, challenges encountered along the way, and key lessons learned.","PeriodicalId":46847,"journal":{"name":"Journal of Homeland Security and Emergency Management","volume":"1 1","pages":"97 - 131"},"PeriodicalIF":0.7000,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Supporting Humanitarian Crisis Decision Making with Reliable Intelligence Derived from Social Media Using AI\",\"authors\":\"Christopher Garcia, G. Rabadi, Diana Abujaber, M. Seck\",\"doi\":\"10.1515/jhsem-2021-0042\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Recent advances in the field of artificial intelligence bring promising new capabilities that can substantially improve our ability to manage complex and evolving situations in the face of uncertainty. Humanitarian crises exemplify such situations, and the pervasiveness of social media renders it one of the most abundant sources of real-time information available. However, it is quite a difficult task to condense a body of social media posts into useful information quickly. In this paper we consider the challenge of using social media reports to provide a reliable, real-time situational awareness in the management of humanitarian crises. Effectively addressing this challenge requires extracting only the relevant information out of text and images in individual social media posts, fusing this information together into actionable information points for decision makers, and providing an assessment of the trustworthiness of this information. We propose a general solution framework and discuss a system developed in collaboration with NATO which combines state-of-the-art deep learning, natural language processing, computer vision, and information fusion models to provide a reliable, actionable, real-time situational awareness for supporting decision making in humanitarian crisis logistics. In addition to the technical approach, we also discuss important practical aspects of this project including the development and validation process, challenges encountered along the way, and key lessons learned.\",\"PeriodicalId\":46847,\"journal\":{\"name\":\"Journal of Homeland Security and Emergency Management\",\"volume\":\"1 1\",\"pages\":\"97 - 131\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2023-03-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Homeland Security and Emergency Management\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1515/jhsem-2021-0042\",\"RegionNum\":4,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"PUBLIC ADMINISTRATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Homeland Security and Emergency Management","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1515/jhsem-2021-0042","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"PUBLIC ADMINISTRATION","Score":null,"Total":0}
引用次数: 0

摘要

人工智能领域的最新进展带来了有希望的新能力,可以大大提高我们在面对不确定性时管理复杂和不断变化的情况的能力。人道主义危机就是这种情况的例证,而社交媒体的普及使其成为最丰富的实时信息来源之一。然而,将大量的社交媒体帖子快速压缩成有用的信息是一项相当困难的任务。在本文中,我们考虑了使用社交媒体报告在人道主义危机管理中提供可靠的实时态势感知的挑战。有效应对这一挑战需要从个人社交媒体帖子的文本和图像中提取相关信息,将这些信息融合到一起,为决策者提供可操作的信息点,并对这些信息的可信度进行评估。我们提出了一个通用的解决方案框架,并讨论了与北约合作开发的一个系统,该系统结合了最先进的深度学习、自然语言处理、计算机视觉和信息融合模型,为人道主义危机后勤支持决策提供可靠、可操作、实时的态势感知。除了技术方法之外,我们还讨论了这个项目的重要实践方面,包括开发和验证过程、在此过程中遇到的挑战,以及学到的关键经验教训。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Supporting Humanitarian Crisis Decision Making with Reliable Intelligence Derived from Social Media Using AI
Abstract Recent advances in the field of artificial intelligence bring promising new capabilities that can substantially improve our ability to manage complex and evolving situations in the face of uncertainty. Humanitarian crises exemplify such situations, and the pervasiveness of social media renders it one of the most abundant sources of real-time information available. However, it is quite a difficult task to condense a body of social media posts into useful information quickly. In this paper we consider the challenge of using social media reports to provide a reliable, real-time situational awareness in the management of humanitarian crises. Effectively addressing this challenge requires extracting only the relevant information out of text and images in individual social media posts, fusing this information together into actionable information points for decision makers, and providing an assessment of the trustworthiness of this information. We propose a general solution framework and discuss a system developed in collaboration with NATO which combines state-of-the-art deep learning, natural language processing, computer vision, and information fusion models to provide a reliable, actionable, real-time situational awareness for supporting decision making in humanitarian crisis logistics. In addition to the technical approach, we also discuss important practical aspects of this project including the development and validation process, challenges encountered along the way, and key lessons learned.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
8.80
自引率
12.50%
发文量
11
期刊介绍: The Journal of Homeland Security and Emergency Management publishes original, innovative, and timely articles describing research or practice in the fields of homeland security and emergency management. JHSEM publishes not only peer-reviewed articles, but also news and communiqués from researchers and practitioners, and book/media reviews. Content comes from a broad array of authors representing many professions, including emergency management, engineering, political science and policy, decision science, and health and medicine, as well as from emergency management and homeland security practitioners.
期刊最新文献
Group Identity, Self-Concept, and Gender Bias: A Regression Analysis of Female Student Experiences Within Emergency Management-Related Higher Education Programs A National Disaster Medicine Quality Management Tool in an International Context – A Theoretical Study Cross-Border and Transboundary Resilience Between Here and There. The Role of Social Entrepreneurship in Restoring the Supply Chain of Face Masks During the COVID-19 Crisis Standardization Gaps in European Disaster Management
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1