Event relatedness assessment of Twitter messages for emergency response

Laylavi, Farhad; Rajabifard, Abbas; Kalantari, Mohsen

doi:10.1016/j.ipm.2016.09.002

Cited by 79 publications

(47 citation statements)

References 21 publications

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“…On the other hand, studies such as Li et al (2012), Imran et al (2013), and Laylavi et al (2016a) focused on ranking and classification techniques to identify tweets on a priority basis. Li et al (2012) proposed a system that used tweets to detect and analyze crime and disaster related events, such as shootings, car accidents, tornadoes etc.…”

Section: Related Workmentioning

confidence: 99%

“…However, very less attention was given to assessment and classification of Twitter messages based on the level of informativeness and relatedness to a specific type of event. Laylavi et al (2016a) proposed a method for detecting eventspecific informative tweets related to a storm event. They used the term frequency analysis and relationship scoring function to define event-related term classes.…”

Section: Related Workmentioning

confidence: 99%

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Event classification and location prediction from tweets during disasters

et al. 2017

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Social media is a platform to express one's view in real time. This real time nature of social media makes it an attractive tool for disaster management, as both victims and officials can put their problems and solutions at the same place in real time. We investigate the Twitter post in a flood related disaster and propose an algorithm to identify victims asking for help. The developed system takes tweets as inputs and categorizes them into high or low priority tweets. User location of high priority tweets with no location information is predicted based on historical locations of the users using the Markov model. The system is working well, with its classification accuracy of 81%, and location prediction accuracy of 87%. The present system can be extended for use in other natural disaster situations, such as earthquake, tsunami, etc., as well as man-made disasters such as riots, terrorist attacks etc. The present system is first of its kind, aimed at helping victims during disasters based on their tweets.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Event classification and location prediction from tweets during disasters

et al. 2017

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“…• Number of accidents: This aspect, which refers to the demand for emergency medical services, is the principal factor considered in the covering model [7]. [10]. We evaluate all 5 of these risk factors in our model, and we describe how we assess risk level in Section 2.3.…”

Section: Determining Risk Factors To Account For In Determining Ambulmentioning

confidence: 99%

Integrating Spatial-Temporal Risk Factors for an Ambulance Allocation Strategy: A Case Study in Bangkok

et al. 2018

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Dedicated emergency medical services (EMS) are important to patients’ chances of survival. In particular, the quicker such services arrive at the scene of an incident, the higher the survival rate. Therefore, the management of ambulance bases is an essential aspect of emergency medical services. Further, the locations of ambulance bases are determined based on patient demand. However, in practice, many elements should be taken into account in a risk assessment of given areas within a locale. Specifically, each area should be assessed for the number and severity of accidents that ordinarily take place there, the number and size of the public events it hosts, its population density, and the number of elderly people resident. In this study, we use a spatial-temporal approach to integrate those factors into a risk assessment of areas relative to each other in a locale. Based on this risk assessment, we determine the optimal locations for ambulance bases in order to minimize response time. We validate our approach using Bangkok as a case study.

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“…This study manually set a collection of keywords related to CDE as seeds, and then applied an iteratively refined algorithm to extract new related keywords [4]. Laylavi et al (2017) assessed the degree of relatedness of Twitter messages to a specific event of interest [18]. Wang et al (2012) used a semantic role labeling approach to target crime-related tweets [19].…”

Section: Related Workmentioning

confidence: 99%

Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data

Zhou

Chen

2017

IJGI

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Social media data provide a great opportunity to investigate event flow in cities. Despite the advantages of social media data in these investigations, the data heterogeneity and big data size pose challenges to researchers seeking to identify useful information about events from the raw data. In addition, few studies have used social media posts to capture how events develop in space and time. This paper demonstrates an efficient approach based on machine learning and geovisualization to identify events and trace the development of these events in real-time. We conducted an empirical study to delineate the temporal and spatial evolution of a natural event (heavy precipitation) and a social event (Pope Francis' visit to the US) in the New York City-Washington, DC regions. By investigating multiple features of Twitter data (message, author, time, and geographic location information), this paper demonstrates how voluntary local knowledge from tweets can be used to depict city dynamics, discover spatiotemporal characteristics of events, and convey real-time information.

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Event relatedness assessment of Twitter messages for emergency response

Cited by 79 publications

References 21 publications

Event classification and location prediction from tweets during disasters

Event classification and location prediction from tweets during disasters

Integrating Spatial-Temporal Risk Factors for an Ambulance Allocation Strategy: A Case Study in Bangkok

Tracing the Spatial-Temporal Evolution of Events Based on Social Media Data

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