# Use of Social Media Data in Disaster Management: A Survey
## 9 Conclusions  
In this paper, we reviewed research publications to investigate the contributions of social media data and the techniques for data management and analysis in disaster management. We studied the various dimensions of the contributions based on our proposed taxonomy, which includes data sources, languages, spatial and temporal information, methodologies, and applications. Human-centric approaches (e.g., social media, blogs, and crowdsourcing) have become significant data sources that provide observational data of real-world events and contribute to disaster management. Several publications have proposed the exploitation of social media data for disaster management, with Twitter being one of the most significant social media data sources used for disaster management. The temporal and spatial information extracted from Twitter is critical information for supporting decision-making in disaster management. Geo-location identification and analysis are key research challenges of the spatial perspective in disaster management. Even though several methodologies have been proposed in the literature, these challenges remain unresolved. However, social media content, along with temporal information, including posting time and event time, can be used to facilitate disaster management in several ways. Many research works used such information to detect precursor events or support decision-making during disasters. Furthermore, several approaches for managing, analysing, and evaluating social media data have been proposed in the literature. It is evident that Big Data technology is a key technology for social media data management due to the high volume of generated social media data. Moreover, machine learning and information retrieval algorithms are widely used to collect, classify, and extract essential information from social media. Such information includes temporal and spatial information and disaster events. F-Measure, precision, and recall are common techniques for evaluation of the proposed methods for data collection, classification, and extraction. Finally, the application perspective of this survey has shown evidence that social media plays a significant role in every phase of disaster management, and the generated data have been used extensively in such management.  
Conceptualisation, R.R., T.S., P.J., M.V.R., J.P., N.B.T., Z.W., R.S., D.P., H.T. and G.M.; methodology, R.R., T.S., P.J. and J.P.; data curation, J.P.; writing--original draft preparation, J.P., N.B.T., Z.W., R.S., D.P. and H.T.; writing--review and editing, J.P., T.S., R.R. and P.J.; visualisation, J.P. and T.S.; supervision, R.R., T.S., P.J., G.M. and M.V.R.; funding acquisition, R.R., P.J. and M.V.R. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by two Natural Environment Research Council projects, including LandSlip (NE/P000681/1), FloodPrep (NE/P017134/1), EPSRC Project Super (RES/0550/7498/001), and the Engineering and Physical Sciences Research Council (EPSRC), Sustainable urban power supply through intelligent control and enhanced restoration of AC/DC networks, EPSRC-NSFC Call in Sustainable Power Supply, and EP/T021985/1.  
Not applicable, the study does not report any data.  
This research was partially supported by Natural Environment Research Council projects, including LandSlip (NE/P000681/1), FloodPrep (NE/P017134/1), EPSRC Project Super (RES/0550/7498/001), the Engineering and Physical Sciences Research Council (EPSRC), Sustainable urban power supply through intelligent control and enhanced restoration of AC/DC networks, EPSRC-NSFC Call in Sustainable Power Supply, and EP/T021985/1.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} \(LO\) & Landslip ontology \\ WSN & Wireless sensor network \\ EQ & Earth observation \\ \end{tabular}