Social media applications and emergency management: A literature review and research agenda

Luna, Sergio; Pennock, Michael J.

doi:10.1016/j.ijdrr.2018.01.006

Cited by 125 publications

(105 citation statements)

References 56 publications

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“…Twitter has played a relevant role in the hours and days immediately after catastrophic events since its advent and has been rapidly becoming the first point of contact for people wishing to seek or provide information about those situations (e.g., [5][6][7][8]). Luna and Pennock [9] conducted a review and analysis of the literature regarding the application of social media to emergency management. Twitter, in this context, can fill what South [10] described as "the lack of immediate flow of information from people at the scene towards authorities or those who can provide help.…”

Section: Related Workmentioning

confidence: 99%

“…On the same issue, Martínez-Rojas et al ( [18], p. 202) added the following: "Regarding the propagation of false information, it is still a challenge the identification of malicious content spread during emergencies." Luna and Pennock [9] mentioned six different scenarios where outdated, inaccurate, or false information has been disseminated through social media applications, slowing down the recovery efforts. As asserted, for instance, by Karlova and Fisher [20] "if the information spread is outdated, inaccurate or false, participants could take counterproductive actions that might exacerbate the negative impacts of disasters."…”

Section: Related Workmentioning

confidence: 99%

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A Citizen-Sensing-Based Digital Service for the Analysis of On-Site Post-Earthquake Messages

Felice

Iessi

2019

IJGI

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The effectiveness of disaster response depends on the correctness and timeliness of data regarding the location and the impact of the event. These two issues are critical when the data come from citizens’ tweets, since the automatic classification of disaster-related tweets suffers from many shortcomings. In this paper, we explore an approach based on participatory sensing (i.e., a subset of mobile crowdsourcing that emphasizes the active and intentional participation of citizens to collect data from the place where they live or work). We operate with the hypothesis of a “friendly world”, that is by assuming that after a calamitous event, in the survivors prevails the feeling of helping those who suffer. The extraction, from the Twitter repository, of the few tweets relevant to the event of interest has a long processing time. With the aggravating circumstance in the phase that follows a severe earthquake, the elaboration of tweets clashes with the need to act promptly. Our proposal allows a huge reduction of the processing time. This goal is reached by introducing a service and a mobile app, the latter is an intermediate tool between Twitter and the citizens, suitable to assist them to write structured messages that act as surrogates of tweets. The article describes the architecture of the software service and the steps involved in the retrieval, from the Twitter server, of the messages coming from citizens living in the places hit by the earthquake; moreover, it details the storage of those messages into a geographical database and their processing using SQL.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Citizen-Sensing-Based Digital Service for the Analysis of On-Site Post-Earthquake Messages

Felice

Iessi

2019

IJGI

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“…It also makes possible a faster information transfer, facilitates interaction amongst team members [58]. And it was considered as providing timely, widespread data collection and dissemination as well as providing a reliable communication channel with teams [59]. Thus, organizations can utilize social media to facilitate the task of the teams and collaborate with them instead of face-toface meetings [60].…”

Section: Social Media (Sm)mentioning

confidence: 99%

Team Performance in Flood Emergency Response: A Conceptual Model and Scale Development

Saeed¹,

Zakaria²,

Sutoyo³

2018

IJIE

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Based on findings from the existing literature regarding the used knowledge integration, social media, and emergency management, this paper presents a conceptual model for Emergency Management Team Performance (EMTP) and provides a rigorous approach in scale development and validation. The model is meant to improve organizations volunteer and staff per on flood field. Thus, this study conceptualizes the construct of EM team performance and generates an initial 39 item on EM task scale. Based on the sample data, we provided an empirical validation of the Emergency Management Team Performance (EMTP) constructs and its underlining dimensionality and developments a generic Emergency Management Team (EMT) scale with desirable psychometric properties, which includes face validity, content validity and pilot testing. In the validation process, a 30-item EM task scale with 6 constructs (task characteristic, task-technology, task-technology fit, social media usage, knowledge integration and EM team performance) were used. This study is a pioneering effort to develop and validate EMTP scale and will contribute to the development of knowledge integration while adding to the repository of rigid research instruments for researcher's utilization.

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“…Several examples were seen when the news was first reported on Twitter, such as an airplane crash over the Hudson River in New York in the year 2009 (Sakaki et al, 2013), the death of former British Prime Minister Margaret Thatcher in April 2013 1 , and the explosions at the Boston Marathon 2013 1 . In recent years, Twitter has been used extensively in the course of natural and human-made disasters such as earthquakes, floods, fire, terrorist attacks, civil unrest, and so on (Alexander, 2014;Landwehr et al, 2016;Laylavi et al, 2017Laylavi et al, , 2016Luna & Pennock, 2018;Mejri et al, 2017;Mendoza et al, 2010;Sakaki et al, 2013;Singh et al, 2017;Yuan & Liu, 2018). The government and non-government agencies use Twitter in case of crisis so that different rescue operations can leap into action, disseminate information to the wider audience, and recognize floor reality (Imran et al, 2014a(Imran et al, , 2015Landwehr et al, 2016;Laylavi et al, 2017Laylavi et al, , 2016Rossi et al, 2018;Sakaki et al, 2013;Zhou et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Location reference identification from tweets during emergencies: A deep learning approach

Kumar

Singh

2019

International Journal of Disaster Risk Reduction

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Twitter is recently being used during crises to communicate with officials and provide rescue and relief operation in real time. The geographical location information of the event, as well as users, are vitally important in such scenarios.The identification of geographic location is one of the challenging tasks as the location information fields, such as user location and place name of tweets are not reliable. The extraction of location information from tweet text is difficult as it contains a lot of non-standard English, grammatical errors, spelling mistakes, non-standard abbreviations, and so on. This research aims to extract location words used in the tweet using a Convolutional Neural Network (CNN) based model. We achieved the exact matching score of 0.929, Hamming loss of 0.002, and F 1 -score of 0.96 for the tweets related to the earthquake. Our model was able to extract even three-to four-word long location references which is also evident from the exact matching score of over 92%. The findings of this paper can help in early event localization, emergency situations, real-time road traffic management, localized advertisement, and in various location-based services.

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Social media applications and emergency management: A literature review and research agenda

Cited by 125 publications

References 56 publications

A Citizen-Sensing-Based Digital Service for the Analysis of On-Site Post-Earthquake Messages

A Citizen-Sensing-Based Digital Service for the Analysis of On-Site Post-Earthquake Messages

Team Performance in Flood Emergency Response: A Conceptual Model and Scale Development

Location reference identification from tweets during emergencies: A deep learning approach

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