scispace - formally typeset
Open AccessJournal ArticleDOI

Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery

Qunying Huang, +1 more
- 24 Aug 2015 - 
- Vol. 4, Iss: 3, pp 1549-1568
TLDR
This paper makes an initial effort in coding social media messages into different themes within different disaster phases during a time-critical crisis by manually examining more than 10,000 tweets generated during a natural disaster and referencing the findings from the relevant literature and official government procedures involving different disaster stages.
Abstract
Social media data have emerged as a new source for detecting and monitoring disaster events. A number of recent studies have suggested that social media data streams can be used to mine actionable data for emergency response and relief operation. However, no effort has been made to classify social media data into stages of disaster management (mitigation, preparedness, emergency response, and recovery), which has been used as a common reference for disaster researchers and emergency managers for decades to organize information and streamline priorities and activities during the course of a disaster. This paper makes an initial effort in coding social media messages into different themes within different disaster phases during a time-critical crisis by manually examining more than 10,000 tweets generated during a natural disaster and referencing the findings from the relevant literature and official government procedures involving different disaster stages. Moreover, a classifier based on logistic regression is trained and used for automatically mining and classifying the social media messages into various topic categories during various disaster phases. The classification results are necessary and useful for emergency managers to identify the transition between phases of disaster management, the timing of which is usually unknown and varies across disaster events, so that they can take action quickly and efficiently in the impacted communities. Information generated from the classification can also be used by the social science research communities to study various aspects of preparedness, response, impact and recovery.

read more

Content maybe subject to copyright    Report

Citations
More filters
Journal ArticleDOI

Big data and disaster management: a systematic review and agenda for future research

TL;DR: This study examines big data in DM to present main contributions, gaps, challenges and future research agenda, and shows a classification of publications, an analysis of the trends and the impact of published research in the DM context.
Journal ArticleDOI

Social media analytics for natural disaster management

TL;DR: A schema is proposed to categorize the gathered articles into 15 classes and facilitate the generation of data analysis tasks and suggest research opportunities and challenges in fusing social media data with authoritative datasets, i.e. census data and remote-sensing data.
Journal ArticleDOI

Spatial, temporal, and content analysis of Twitter for wildfire hazards

TL;DR: In this article, the authors analyzed wildfire-related Twitter activities in terms of their attributes pertinent to space, time, content, and network, so as to gain insights into the usefulness of social media data in revealing situational awareness.
Journal ArticleDOI

Using Twitter for tasking remote-sensing data collection and damage assessment: 2013 Boulder flood case study

TL;DR: In this article, a new methodology is introduced that leverages data harvested from social media for tasking the collection of remote-sensing imagery during disasters or emergencies, which is valuable in situations where environmental hazards such as hurricanes or severe weather affect very large areas.
Journal ArticleDOI

Event classification and location prediction from tweets during disasters

TL;DR: The Twitter post in a flood related disaster is investigated and an algorithm to identify victims asking for help is proposed, which is first of its kind, aimed at helping victims during disasters based on their tweets.
References
More filters
Proceedings ArticleDOI

Earthquake shakes Twitter users: real-time event detection by social sensors

TL;DR: This paper investigates the real-time interaction of events such as earthquakes in Twitter and proposes an algorithm to monitor tweets and to detect a target event and produces a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location.
Proceedings ArticleDOI

Microblogging during two natural hazards events: what twitter may contribute to situational awareness

TL;DR: Analysis of microblog posts generated during two recent, concurrent emergency events in North America via Twitter, a popular microblogging service, aims to inform next steps for extracting useful, relevant information during emergencies using information extraction (IE) techniques.
Journal ArticleDOI

The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic

TL;DR: The use of information embedded in the Twitter stream is examined to (1) track rapidly-evolving public sentiment with respect to H1N1 or swine flu, and (2) track and measure actual disease activity.
Journal ArticleDOI

A new model of social class : findings from the BBC's Great British Class Survey Experiment.

TL;DR: The authors used latent class analysis on these variables to derive seven classes of social class in the UK, and demonstrate the existence of an elite class whose wealth separates them from an established middle class, as well as a class of technical experts.
Journal ArticleDOI

Harnessing the Crowdsourcing Power of Social Media for Disaster Relief

TL;DR: The advantages and disadvantages of crowdsourcing applications applied to disaster relief coordination are described and several challenges must be addressed to make crowdsourcing a useful tool that can effectively facilitate the relief progress in coordination, accuracy, and security.
Related Papers (5)