TY - JOUR
T1 - Using community information for natural disaster alerts
AU - Chen, Chun Chieh
AU - Wang, Hei Chia
N1 - Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: The research is based on work supported by the Taiwan Ministry of Science and Technology under Grant No. MOST 107- 2410-H-006 040-MY3 and MOST 108-2511-H-0 06-0 09. We would like to thank the Center of Innovative Fintech Business Models, Taiwan for a research grant to support this research.
Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: The research is based on work supported by the Taiwan Ministry of Science and Technology under Grant No. MOST 107- 2410-H-006 040-MY3 and MOST 108-2511-H-0 06-0 09. We would like to thank the Center of Innovative Fintech Business Models, Taiwan for a research grant to support this research.
Publisher Copyright:
© The Author(s) 2020.
PY - 2022/10
Y1 - 2022/10
N2 - Recently, the ceaseless rise in the global average temperature has led to extreme climates in which natural disasters, such as droughts, hurricanes, earthquakes and floods, are becoming increasingly serious. Recent research has found that social media typically reflects disasters earlier than official communication channels. In this study, the idea of collecting information on flood disasters caused during the periods of typhoons and heavy rains for a city from the plain text messages released by social media by means of a term frequency (TF) and sliding window approach is proposed. The dataset analysed here contains a total of 292 articles and 12,484 tweets. This research determines how to establish a warning mechanism, with an added notification time for flooding disasters, and it shows how to provide relevant disaster relief personnel with references. This article contributes by combining social media data with emergency management information cloud (EMIC) data, especially in the context of having a mechanism for warning about flooding disasters. According to the experimental results, a sliding window of 90 min and a sliding gap of 10 min obtained the best F-measure value (F = 0.315). The event studied was Typhoon Megi (September 2016), which caused major flooding in Tainan. For the Typhoon Megi event, the flood disaster location database had 161 streets available for matching. Based on the experimental results, it is possible to obtain a high-precision (90% or higher) accuracy rate from real-time tweet data by exploiting a social media dataset.
AB - Recently, the ceaseless rise in the global average temperature has led to extreme climates in which natural disasters, such as droughts, hurricanes, earthquakes and floods, are becoming increasingly serious. Recent research has found that social media typically reflects disasters earlier than official communication channels. In this study, the idea of collecting information on flood disasters caused during the periods of typhoons and heavy rains for a city from the plain text messages released by social media by means of a term frequency (TF) and sliding window approach is proposed. The dataset analysed here contains a total of 292 articles and 12,484 tweets. This research determines how to establish a warning mechanism, with an added notification time for flooding disasters, and it shows how to provide relevant disaster relief personnel with references. This article contributes by combining social media data with emergency management information cloud (EMIC) data, especially in the context of having a mechanism for warning about flooding disasters. According to the experimental results, a sliding window of 90 min and a sliding gap of 10 min obtained the best F-measure value (F = 0.315). The event studied was Typhoon Megi (September 2016), which caused major flooding in Tainan. For the Typhoon Megi event, the flood disaster location database had 161 streets available for matching. Based on the experimental results, it is possible to obtain a high-precision (90% or higher) accuracy rate from real-time tweet data by exploiting a social media dataset.
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U2 - 10.1177/0165551520979870
DO - 10.1177/0165551520979870
M3 - Article
AN - SCOPUS:85099517141
VL - 48
SP - 718
EP - 732
JO - Journal of Information Science
JF - Journal of Information Science
SN - 0165-5515
IS - 5
ER -