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A Methodology for Automatic Acquisition of Flood‐event Management Information From Social Media: the Flood in Messinia, South Greece, 2016

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  • Stathis G. Arapostathis

    (Harokopio University)

Abstract

Social network data, utilised as a VGI source, was analysed using the September 2016 flood event in Messinia, South Greece. The flood event led to damage in the urban and rural environment in the general area, and to human deaths. An innovative methodology is based on applying machine learning to classify Twitter content. Tweets were classified into the following ten categories: (i) flood identification, (ii) rain identification, (iii) consequences of the flood, (iv) expressed emotions, (v) ironic attitude to local disaster management authorities, (vi) disaster management information, (vii); volunteer actions, (viii); situation overview, (ix); social effects, and (x); weather information. Some of the categories were divided further, to quantify significant information. The classified output was sequentially geo-referenced by identifying geographic entities within the text of each post (geo-parsing) and replicating each post according to the number of geolocations. The data processing involved various geo-validations and performance metrics. The final output was used to create maps and graphs of different time periods, that provide useful insights into the flood event for disaster management purposes. The applied methodology is an evolution of previous research published by the author, this time providing complete results, based on the analysis of 100 % of the data available, with maps and graphs which demonstrate how the flood event unfolded in different time periods. The methodology is fully automated in terms of data processing, and can be applied using a script developed by the author in the R programming language. This research is a step towards the real-time delivery of advanced information for all disaster management stakeholders, from official authorities and rescue teams, to volunteers and locals who may be situated within the area of a disastrous flood occurrence.

Suggested Citation

  • Stathis G. Arapostathis, 2021. "A Methodology for Automatic Acquisition of Flood‐event Management Information From Social Media: the Flood in Messinia, South Greece, 2016," Information Systems Frontiers, Springer, vol. 23(5), pages 1127-1144, September.
    • Handle: RePEc:spr:infosf:v:23:y:2021:i:5:d:10.1007_s10796-021-10105-z
    DOI: 10.1007/s10796-021-10105-z
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    References listed on IDEAS

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    1. Robert I. Ogie & Hugh Forehead & Rodney J. Clarke & Pascal Perez, 2018. "Participation Patterns and Reliability of Human Sensing in Crowd-Sourced Disaster Management," Information Systems Frontiers, Springer, vol. 20(4), pages 713-728, August.
    2. Grolemund, Garrett & Wickham, Hadley, 2011. "Dates and Times Made Easy with lubridate," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i03).
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    Cited by:

    1. Jingyi Gao & Osamu Murao & Xuanda Pei & Yitong Dong, 2022. "Identifying Evacuation Needs and Resources Based on Volunteered Geographic Information: A Case of the Rainstorm in July 2021, Zhengzhou, China," IJERPH, MDPI, vol. 19(23), pages 1-21, November.
    2. Yoshiki B. Kurata & Ardvin Kester S. Ong & Ranice Ysabelle B. Ang & John Karol F. Angeles & Bianca Danielle C. Bornilla & Justine Lian P. Fabia, 2023. "Factors Affecting Flood Disaster Preparedness and Mitigation in Flood-Prone Areas in the Philippines: An Integration of Protection Motivation Theory and Theory of Planned Behavior," Sustainability, MDPI, vol. 15(8), pages 1-24, April.
    3. Yuko Murayama & Hans Jochen Scholl & Dimiter Velev, 2021. "Information Technology in Disaster Risk Reduction," Information Systems Frontiers, Springer, vol. 23(5), pages 1077-1081, September.
    4. Christian Meske & Enrico Bunde, 2023. "Design Principles for User Interfaces in AI-Based Decision Support Systems: The Case of Explainable Hate Speech Detection," Information Systems Frontiers, Springer, vol. 25(2), pages 743-773, April.

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