VictimFinder: Harvesting rescue requests in disaster response from social media with BERT

Zhou, B.; Zou, Lei; Mostafavi, Ali; Lin, Binbin; Yang, Mei; Gharaibeh, Nasir G.; Cai, Heng; Abedin, Joynal; Mandal, Debayan

doi:10.1016/j.compenvurbsys.2022.101824

Cited by 45 publications

(24 citation statements)

References 27 publications

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“…Another study employed BERT, RoBERTa, XLNet, and seven other transformer-based models to find the victims of disasters on Twitter for the purpose of rescue operations (Zhou et al. 2022 ).…”

Section: Event Detection Methods and Techniquesmentioning

confidence: 99%

“…The findings of a study show that XlNet achieves slightly better results for potentially harmful and protective suicide-related content on Twitter (Metzler et al 2022). Another study employed BERT, RoBERTa, XLNet, and seven other transformer-based models to find the victims of disasters on Twitter for the purpose of rescue operations (Zhou et al 2022).…”

Section: Transformer-based Pre-trained Modelsmentioning

confidence: 99%

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Detection of dangerous events on social media: a critical review

Jamil

Pais

Cordeiro

2022

Soc. Netw. Anal. Min.

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The usability of the events information on social media has been widely studied recently. Several surveys have reviewed the specific type of events on social media using various techniques. Most of the existing methods for event detection are segregated as they approach certain situations that limit the overall details of events happening consecutively on social media while ignoring the crucial relationship between the evolution of these events. Numerous events that materialize on the social media sphere every day before our eyes jeopardize people’s safety and are referred to by using a high-level concept of dangerous events. The front of dangerous events is broad, yet no known work exists that fully addresses and approaches this issue. This work introduces the term dangerous events and defines its scope in terms of practicality to establish the origins of the events caused by the previous events and their respective relationship. Furthermore, it divides dangerous events into sentiment, scenario, and action-based dangerous events grouped on their similarities. The existing research and methods related to event detection are surveyed, including some available events datasets and knowledge-base to address the problem. Finally, the survey is concluded with suggestions for future work and possible related challenges.

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“…Another study employed BERT, RoBERTa, XLNet, and seven other transformer-based models to find the victims of disasters on Twitter for the purpose of rescue operations (Zhou et al. 2022 ).…”

Section: Event Detection Methods and Techniquesmentioning

confidence: 99%

Section: Transformer-based Pre-trained Modelsmentioning

confidence: 99%

Detection of dangerous events on social media: a critical review

Jamil

Pais

Cordeiro

2022

Soc. Netw. Anal. Min.

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“…Rapid detection of damage using social media platforms can help rescue people in danger, determine evacuation orders and prepare for the subsequent disaster hit. For example, Zhou et al (2022) developed new algorithms for identifying rescue request tweets during 2017 Hurricane Harvey. Ganz et al (2015) developed an intelligent system that provides situational awareness to support victim searches and rescue operations.…”

Section: The Role Of Twitter In Disaster Managementmentioning

confidence: 99%

A multi-level analytic framework for disaster situational awareness using Twitter data

Zhai

2022

Comput.Urban Sci.

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During a natural disaster, mining messages from social media platforms can facilitate local agencies, rescue teams, humanitarian aid organizations, etc., to track the situational awareness of the public. However, for different stakeholders, the concerns about people’s situational awareness in a natural disaster event are different. Therefore, I developed a Twitter-based analytic framework to take perception-level situational awareness, humanitarian-level situational awareness, and action-level situational awareness into consideration. Specifically, perception-level situational awareness mainly reflects people’s perception of the ongoing natural disaster event (i.e., if people are discussing the disaster event). Decision-makers can rapidly have a big picture of severely impacted regions. Humanitarian-level situational awareness represents tweets that are associated with the humanitarian categories based on the definition from the United Nations Office for the Coordination of Humanitarian Affairs. The detection of humanitarian-level situational awareness can help response teams understand the specific situations and needs of local communities. In terms of the action-level situational awareness, I extracted noun-verb pairs in each tweet to explicitly represent the specific event described in a given tweet, so that the response teams can quickly act on the situation case by case. Moreover, to shed light on disaster resilience and social vulnerability, I further examined the demographic characteristics of three levels of situational awareness. I empirically demonstrated the analytic framework using geo-tagged tweets during 2018 Hurricane Michael.

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“…NLP can support extreme weather events data challenges by analyzing large amounts of weather data for patterns and trends (Kahle et al 2022 ). These data can provide more accurate forecasts and early warning systems for extreme weather events (Kitazawa and Hale 2021 ; Rossi et al 2018 ; Vayansky et al 2019 ; Zhou et al 2022 ). NLP can also monitor social media for information on extreme weather events, allowing for the detection of local events that may not be reported in official channels (Kitazawa and Hale 2021 ; Zhou et al 2022 ).…”

Section: Introductionmentioning

confidence: 99%

“…These data can provide more accurate forecasts and early warning systems for extreme weather events (Kitazawa and Hale 2021 ; Rossi et al 2018 ; Vayansky et al 2019 ; Zhou et al 2022 ). NLP can also monitor social media for information on extreme weather events, allowing for the detection of local events that may not be reported in official channels (Kitazawa and Hale 2021 ; Zhou et al 2022 ). Additionally, NLP can be used to create automated chat bots to provide information to those affected by extreme weather events, such as directions to shelters, medical assistance, and other resources.…”

Section: Introductionmentioning

confidence: 99%

A systematic review of natural language processing applications for hydrometeorological hazards assessment

Tounsi

Temimi

2023

Nat Hazards

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Natural language processing (NLP) is a promising tool for collecting data that are usually hard to obtain during extreme weather, like community response and infrastructure performance. Patterns and trends in abundant data sources such as weather reports, news articles, and social media may provide insights into potential impacts and early warnings of impending disasters. This paper reviews the peer-reviewed studies (journals and conference proceedings) that used NLP to assess extreme weather events, focusing on heavy rainfall events. The methodology searches four databases (ScienceDirect, Web of Science, Scopus, and IEEE Xplore) for articles published in English before June 2022. The preferred reporting items for systematic reviews and meta-analysis reviews and meta-analysis guidelines were followed to select and refine the search. The method led to the identification of thirty-five studies. In this study, hurricanes, typhoons, and flooding were considered. NLP models were implemented in information extraction, topic modeling, clustering, and classification. The findings show that NLP remains underutilized in studying extreme weather events. The review demonstrated that NLP could potentially improve the usefulness of social media platforms, newspapers, and other data sources that could improve weather event assessment. In addition, NLP could generate new information that should complement data from ground-based sensors, reducing monitoring costs. Key outcomes of NLP use include improved accuracy, increased public safety, improved data collection, and enhanced decision-making are identified in the study. On the other hand, researchers must overcome data inadequacy, inaccessibility, nonrepresentative and immature NLP approaches, and computing skill requirements to use NLP properly.

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VictimFinder: Harvesting rescue requests in disaster response from social media with BERT

Cited by 45 publications

References 27 publications

Detection of dangerous events on social media: a critical review

Detection of dangerous events on social media: a critical review

A multi-level analytic framework for disaster situational awareness using Twitter data

A systematic review of natural language processing applications for hydrometeorological hazards assessment

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