The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Social media analytics for natural disaster management
Abstract
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.
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Cites
Poverty and Disasters in the United States: A Review of Recent Sociological Findings
Fothergill, A., & Peek, L. A. (2004) · Natural Hazards · doi:10.1023/b:nhaz.0000026792.76181.d9 · Cited by 1,153
Processing Social Media Messages in Mass Emergency: A Survey
Imran, M., Castillo, C., Diaz, F., & Vieweg, S. (2015) · ACM Computing Surveys · doi:10.1145/2771588 · Cited by 804
Gupta, A., Lamba, H., Kumaraguru, P., & Joshi, A. (2013) · Proceedings of the 22nd International Conference on World Wide Web Companion · doi:10.1145/2487788.2488033 · Cited by 573 · cited 3×
“In most cases, this is done by simply plotting social media messages with geo-coordinates on a map (see Gupta et al .”
BackgroundInfluentialMapping the data shadows of Hurricane Sandy: Uncovering the sociospatial dimensions of ‘big data’
Shelton, T., Poorthuis, A., Graham, M., & Zook, M. (2014) · Geoforum · doi:10.1016/j.geoforum.2014.01.006 · Cited by 256
Privacy, reconsidered: New representations, data practices, and the geoweb
Elwood, S., & Leszczynski, A. (2011) · Geoforum · doi:10.1016/j.geoforum.2010.08.003 · Cited by 226
Cited by
Using Social Media to Mine and Analyze Public Opinion Related to COVID-19 in China
Han, X., Wang, J., Zhang, M., & Wang, X. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17082788 · Cited by 361
Khan, A., Gupta, S., & Gupta, S. K. (2020) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2020.101642 · Cited by 357
Martínez-Rojas, M., Pardo-Ferreira, M. D. C., & Rubio-Romero, J. C. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.07.008 · Cited by 213
Application of social media analytics in tourism crisis communication
Park, D., Kim, W. G., & Choi, S. (2019) · Current Issues in Tourism · doi:10.1080/13683500.2018.1504900 · Cited by 98
Metadata
- Authors
- Wang, Z., & Ye, X.
- Venue
- International Journal of Geographical Information Science
- Publisher
- Informa UK Limited
- Published
- 2017-08-22
- Volume
- 32
- Issue
- 1
- Pages
- 49-72
- ISSN
- 0269-3798 (ISSN-L)
- 1365-8816
- 1362-3087
- OpenAlex
- W2750351539
- Cited by
- 314
- References
- 5
- FWCI
- 52.41
- Type
- journal article
- Language
- en
- Open access
- closed
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