Hazard risk awareness and disaster management: Extracting the information content of twitter data

Karimiziarani, Mohammadsepehr; Jafarzadegan, Keighobad; Abbaszadeh, Peyman; Shao, Wanyun; Moradkhani, Hamid

doi:10.1016/j.scs.2021.103577

Cited by 66 publications

(20 citation statements)

References 44 publications

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“…Analyzing data from a Harvard University tweet corpus provided for Hurricane Harvey, Karimiziarani et al (2022) were able to understand how risk perceptions were formed. These perceptions could, in turn, be used to enhance adaptation strategies for urban design and planning.…”

Section: Latent Semantic Analysis Text Classificationmentioning

confidence: 99%

“…Therefore, examining patterns in social media data during a disaster can help governments document both effective and ineffective information dissemination strategies, which can be helpful in future responses to best cater to the needs of communities seeking not only physical but also emotional support (Takahashi et al 2015). Karimiziarani et al (2022) analyzed Twitter data posted during Hurricane Harvey, which hit the US in 2017, and found that understanding the risk perceptions during the disaster period can enhance adaptation strategies for urban design and planning as well as deploy emergency responders to tweets containing information on damages, injuries, requests for help, and situational updates. This was also noted by Baro and Palaoag (2019), who evaluated disaster-related tweets in the Philippines to contribute to the government's initiatives in reducing injuries, infrastructure damage, property damage, and loss of life.…”

Section: Introductionmentioning

confidence: 99%

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Mapping the Public Voice for Development—Natural Language Processing of Social Media Text Data: A Special Supplement of Key Indicators for Asia and the Pacific 2022

2022

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This publication explores how natural language processing (NLP) techniques can be applied to social media text data to map public sentiment and inform development research and policy making. The publication introduces the foundations of natural language analyses and showcases studies that have applied NLP techniques to make progress on the Sustainable Development Goals. It also reviews specific NLP techniques and concepts, supported by two case studies. The first case study analyzes public sentiments on the coronavirus disease (COVID-19) in the Philippines while the second case study explores the public debate on climate change in Australia.

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Section: Latent Semantic Analysis Text Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping the Public Voice for Development—Natural Language Processing of Social Media Text Data: A Special Supplement of Key Indicators for Asia and the Pacific 2022

2022

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“…Tropical cyclones (TCs) are among the most devastating natural disasters in the world. Despite the tremendous national governments' protection efforts, they still result in loss of lives and properties ( Karimiziarani et al., 2022; Klotzbach et al., 2018; Munoz et al., 2021; Sebastian et al., 2017; Song et al., 2022; Tennant & Gilmore, 2020). Tropical Cyclones are compound hazards, during which a combination of climate processes and hazards (i.e., torrential rainfall, intense winds, and abnormal surge) lead to significant societal impacts greater than the impacts each can produce in isolation (de Ruiter et al., 2020; Leonard et al., 2014; Zscheischler et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

A Multivariate Scaling System Is Essential to Characterize the Tropical Cyclones' Risk

et al. 2022

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The current Tropical Cyclones (TCs) scaling system, Saffir‐Simpson Hurricane Wind Scale (SSHWS), characterizes the hazardousness of these events solely based on wind speed. This is despite the fact that TCs are classic examples of compound hazards during which multiple hazard drivers that are wind, storm surge, and intense rainfall interact and yield in impacts greater than the sum of individuals. Studies have shown that people's decision to evacuate is highly related to the estimated SSHWS category. Thus, the current SSHWS ‐based classification of TCs yields an underestimation of the hazardousness of TCs and so may misguide the threatened communities. Here, we propose a new scaling system that uses Copulas for categorizing TCs based on the likelihood of a given set of severity for rainfall, surge, and wind speed. We use a variety of data sources to obtain the timing and intensity of wind speed, rainfall along the track, and the associated maximum surge for 102 TCs that have made landfall in the United States' Atlantic and Gulf coasts between 1979 and 2020. Comparing the outputs of our scaling system with official damage reporting for the costliest TCs in the history of the United States, we show that the proposed approach significantly improves TC hazard communication and can be useful for informing decision makers and emergency responders.

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“…Emergency managers or government agencies use social media as a critical channel for risk and crisis communication with the public. Meanwhile, the emergency professionals may also use information from social media platforms for collective intelligence activities such as quick impacts assessment (Fang et al, 2019 ; Wu & Cui, 2018 ), detecting public sentiments (Iglesias-Sánchez et al, 2020 ) or situation awareness (Karimiziarani et al, 2022 ), and such information can help them make critical decisions for emergency response. Moreover, some organizations, especially non-government organizations or self-organized groups involved in disaster response, may use social media for response coordination (Reuter & Kaufhold, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Topical and emotional expressions regarding extreme weather disasters on social media: a comparison of posts from official media and the public

Han

Shen

Liu

et al. 2022

Humanit Soc Sci Commun

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Understanding media frames and the public resonance during disasters is essential for making inclusive climate change and adaptation policies in the context of increasingly extreme weather events. In this study, we use the extreme weather and flood event that occurred in July 2021 in Zhengzhou, China, as a case study to investigate how official media in China reported this event and how the public responded. Moreover, since one accountability investigation report regarding this disaster was released in January 2022, we also compared these posts between the emergency response period and the post-crisis learning period after the report’s release. Topic modeling using the LDA (Latent Dirichlet Allocation) method and emotion analysis were conducted to analyze the posts from Weibo, China’s primary social media platform. The results demonstrated that the posts from official media and the public comments differed in both topics and emotions, with relatively little coherence. During the emergency response period, the media’s posts focused more on the facts, such as the extreme weather event, the places where it occurred, the impacts, and the search and rescue efforts, while the public comments were more about help appeals from the neglected ones in the rural areas, and emotional expressions such as moral support, condolence or encouragement to the victims and their families. After the accountability investigation in January, the media’s posts primarily covered the investigation process, the punishment, the attribution of disaster consequences, and the lessons learned, while the public’s comments were relatively emotional, praised the good, condoled the victims, and condemned the villains. The dominant emotion from the media’s posts was “like” in July 2021, but it became depression in January 2022. Anger was the prevalent emotion from the public during all the stages. This study provided valuable knowledge to the current understanding of the different patterns and dynamics of official media reports and the public’s resonance in disaster management.

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Hazard risk awareness and disaster management: Extracting the information content of twitter data

Cited by 66 publications

References 44 publications

Mapping the Public Voice for Development—Natural Language Processing of Social Media Text Data: A Special Supplement of Key Indicators for Asia and the Pacific 2022

Mapping the Public Voice for Development—Natural Language Processing of Social Media Text Data: A Special Supplement of Key Indicators for Asia and the Pacific 2022

A Multivariate Scaling System Is Essential to Characterize the Tropical Cyclones' Risk

Topical and emotional expressions regarding extreme weather disasters on social media: a comparison of posts from official media and the public

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