Social media analytics for natural disaster management

被引:158
|
作者
Wang, Zheye [1 ]
Ye, Xinyue [1 ]
机构
[1] Kent State Univ, Dept Geog, Kent, OH 44242 USA
基金
美国国家科学基金会;
关键词
Social media; dimensions; natural disasters; census data; remote sensing; VOLUNTEERED GEOGRAPHIC INFORMATION; REMOTE-SENSING DATA; BIG DATA; TWITTER; FLOOD; SATELLITE; EARTHQUAKE; CONVERGENCE; CREDIBILITY; FRAMEWORK;
D O I
10.1080/13658816.2017.1367003
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Social media analytics has become prominent in natural disaster management. In spite of a large variety of metadata fields in social media data, four dimensions (i.e. space, time, content and network) have been given particular attention for mining useful information to gain situational awareness and improve disaster response. In this article, we review how existing studies analyze these four dimensions, summarize common techniques for mining these dimensions, and then suggest some methods accordingly. We then propose a schema to categorize the gathered articles into 15 classes and facilitate the generation of data analysis tasks. We find that (1) a large part of studies involve multiple dimensions of social media data in their analyses, (2) there are both separate analyses for each dimension and simultaneous analyses for multiple dimensions and (3) there are fewer simultaneous analyses as dimensions increase. Finally, we suggest research opportunities and challenges in fusing social media data with authoritative datasets, i.e. census data and remote-sensing data.
引用
收藏
页码:49 / 72
页数:24
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