Social Media Driven Big Data Analysis for Disaster Situation Awareness: A Tutorial

被引:3
作者
Pal, Amitangshu [1 ]
Wang, Junbo [2 ]
Wu, Yilang [3 ]
Kant, Krishna [4 ]
Liu, Zhi [5 ]
Sato, Kento [6 ]
机构
[1] Indian Inst Technol Kanpur, Comp Sci & Engn, Kanpur 208016, Uttar Pradesh, India
[2] Sun Yat sen Univ, Sch Intelligent Syst Engn, Guangdong Prov Key Lab Intelligent Transportat Sys, Guangzhou, Guangdong, Peoples R China
[3] PKUtech Co Ltd, Tokyo 1010037, Japan
[4] Temple Univ, Comp & Informat Sci, Philadelphia, PA 19122 USA
[5] Univ Electrocommun, Sch Informat & Engn, Tokyo 1828585, Japan
[6] RIKEN Ctr Comp Sci R CCS, Kobe, Hyogo 6500047, Japan
基金
中国国家自然科学基金;
关键词
Spatial big data analytics; crowd big data; disaster management; situation awareness; CLUSTERING-ALGORITHM; SPATIAL-ASSOCIATION; SENTIMENT ANALYSIS; EVENT DETECTION; DENSITY; INFORMATION; PATTERNS; COVID-19; SHAPES; CRISIS;
D O I
10.1109/TBDATA.2022.3158431
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Situational awareness tries to grasp the important events and circumstances in the physical world through sensing, communication, and reasoning. Tracking the evolution of changing situations is an essential part of this awareness and is crucial for providing appropriate resources and help during disasters. Social media, particularly Twitter, is playing an increasing role in this process in recent years. However, extracting intelligence from the available data involves several challenges, including (a) filtering out large amounts of irrelevant data, (b) fusion of heterogeneous data generated by the social media and other sources, and (c) working with partially geo-tagged social media data in order to deduce the needs of the affected people. Spatio-temporal analysis of the data plays a key role in understanding the situation, but is available only sparsely because only a small fraction of people post relevant text and of those very few enable location tracking. In this paper, we provide a comprehensive survey on data analytics to assess situational awareness from social media big data.
引用
收藏
页码:1 / 21
页数:21
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