Relationship between Emotion and Diffusion of Disaster Information on Social Media: Case Study on 2011 Tohoku Earthquake

AbstractIntroductionSocial networks play an important role in disaster situations as they have become a new form of social convergence that provides collective information. The effect of social media on people who experienced disaster should be assessed.HypothesisIn this study, Twitter communication during the Great East Japan Earthquake of March 11, 2011 was assessed. The hypothesis of this study was that usage of Twitter had psychological effects on victims of the disaster.MethodsA cross-sectional questionnaire survey was carried out in cooperation with a major Japanese newspaper three months after the disaster, and 1,144 volunteer participants responded. They were asked about their health, area of residence, property damage they had experienced, information sources they used at the time of the disaster, and their usage of Twitter. Further, the Twitter users were divided into two groups—with and without disaster experience. Their psychological effects relating to feelings of relief, stress or anxiety that they experienced in using Twitter were compared between two groups, and Twitter's psychological risk of disaster experience was estimated as an odds ratio.ResultsTwitter users in this study tended to reside in disaster-affected areas and thought Twitter was a credible information source during the time of the disaster. The psychological effect of Twitter differed based on participants’ disaster experience and gender. Females with disaster experience reported more feelings of relief and stress as a result of using Twitter compared to females who did not experience the disaster. On the other hand, males with disaster experience only reported more stress experiences as a result of using Twitter compared to those without disaster experience.ConclusionTwitter users with disaster experience had a higher usage of Twitter than those without disaster experience. Social media might have had a material psychological influence on people who experienced disaster, and the effect differed by gender. Regardless of gender, negative feelings were transmitted easily among people who experienced the disaster. It was anticipated that the application of Twitter in a disaster situation will be expanded further by taking these findings into consideration.UmiharaJ, NishikitaniM. Emergent use of Twitter in the 2011 Tohoku earthquake. Prehosp Disaster Med.2013;28(5):1-7.

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Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami

Remote Sensing ◽

10.3390/rs10101626 ◽

2018 ◽

Vol 10 (10) ◽

pp. 1626 ◽

Cited By ~ 25

Author(s):

Yanbing Bai ◽

Erick Mas ◽

Shunichi Koshimura

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Disaster Response ◽

Tohoku Earthquake ◽

2011 Tohoku Earthquake ◽

Mapping Technique ◽

Convolutional Network ◽

Response Practice ◽

Damage Mapping

The satellite remote-sensing-based damage-mapping technique has played an indispensable role in rapid disaster response practice, whereas the current disaster response practice remains subject to the low damage assessment accuracy and lag in timeliness, which dramatically reduces the significance and feasibility of extending the present method to practical operational applications. Therefore, a highly efficient and intelligent remote-sensing image-processing framework is urgently required to mitigate these challenges. In this article, a deep learning algorithm for the semantic segmentation of high-resolution remote-sensing images using the U-net convolutional network was proposed to map the damage rapidly. The algorithm was implemented within a Microsoft Cognitive Toolkit framework in the GeoAI platform provided by Microsoft. The study takes the 2011 Tohoku Earthquake-Tsunami as a case study, for which the pre- and post-disaster high-resolution WorldView-2 image is used. The performance of the proposed U-net model is compared with that of deep residual U-net. The comparison highlights the superiority U-net for tsunami damage mapping in this work. Our proposed method achieves the overall accuracy of 70.9% in classifying the damage into “washed away,” “collapsed,” and “survived” at the pixel level. In future disaster scenarios, our proposed model can generate the damage map in approximately 2–15 min when the preprocessed remote-sensing datasets are available. Our proposed damage-mapping framework has significantly improved the application value in operational disaster response practice by substantially reducing the manual operation steps required in the actual disaster response. Besides, the proposed framework is highly flexible to extend to other scenarios and various disaster types, which can accelerate operational disaster response practice.

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