Abstract

Social media has played a significant role in disaster management, as it enables the general public to contribute to the monitoring of disasters by reporting incidents related to disaster events. However, the vast volume and wide variety of generated social media data create an obstacle in disaster management by limiting the availability of actionable information from social media. Several approaches have therefore been proposed in the literature to cope with the challenges of social media data for disaster management. To the best of our knowledge, there is no published literature on social media data management and analysis that identifies the research problems and provides a research taxonomy for the classification of the common research issues. In this paper, we provide a survey of how social media data contribute to disaster management and the methodologies for social media data management and analysis in disaster management. This survey includes the methodologies for social media data classification and event detection as well as spatial and temporal information extraction. Furthermore, a taxonomy of the research dimensions of social media data management and analysis for disaster management is also proposed, which is then applied to a survey of existing literature and to discuss the core advantages and disadvantages of the various methodologies.

Highlights

  • During natural disasters, social media can play an essential role in the emergency response and provide a complete picture of situational awareness during and after the disaster

  • We have developed a taxonomy of social media data management and analysis for disaster management

  • We investigate the contribution of social media applications in the context of disaster management strategy, which is a discipline for dealing with disasters or avoid disasters where possible

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Summary

Introduction

Social media can play an essential role in the emergency response and provide a complete picture of situational awareness during and after the disaster. There are several challenges in acquiring and extracting hazard-related information from social media, including volume, unstructured data sources, signal-to-noise ratio, ungrammatical and multilingual data, and fraudulent message identification and removal. The massive amounts and variety of data generated by social media lead to different levels of information being extracted from the social media data. A tweet with attached images could potentially provide more situational awareness. A tweet with photos of a roadblock on a hilly road can help people who are driving on the road nearby to understand the current situation of the roadblock and change to a new route away from the blocked area

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