An effective filter for screening disaster responses from the crowd
Date Issued
2014
Date
2014
Author(s)
Lin, Wei-Yu
Abstract
The quality of disaster mitigation is directly connected to the efficiency and quality of disaster responses. In current state of practice, most disaster responses require manual process, mainly handled by government officers, to eliminate the incorrectness. They usually collect the information from multiple sources, verify the correctness and announce the verified information. This process ensures the quality of the information but often result in time-delay. Recently, due to the rapid development of Internet and mobile devices, many crowd-based platforms, such as Sahana, Ushahidi crisis map and Typhoon Morakot Crisis Map, have been developed and employed for disaster responses. Although these platforms sometimes play important role in disaster mitigation, they have three obvious drawbacks: correctness, duplication, and inconsistent format. In this research, we aim to develop a computational method, ACI filter, to eliminate the drawbacks. ACI filter integrates both artificial intelligence (AI) filter and the human intelligence using crowd sources. We used AI filter to retrieve the responses that are highly possible to be correct and eliminate the responses that are highly possible to be incorrect. Remaining responses are filtered by crowd sources. We used the crowd for three purposes: to combine the duplicated responses, to eliminate incorrect responses, and to synchronize the formats of the responses. To verify the ACI filter, we used 876 disaster responses collected from a real disaster in Taiwan caused by a serious torrential rain in June 10 2012. We recruited 284 volunteers from the Internet to participate the test. Each participant is asked to answer twenty short questions. Average testing time for each participant is 212 seconds, meaning 10.6 seconds per question. The research results show that ACI filter eliminates 26.25% inaccurate responses. False negative rate (i.e. mistakenly validation) is 0.00%. False positive rate (i.e. mistakenly elimination) is 3.91%. In conclusion, ACI filter, combining the machine and human power, can successfully improve the accuracy of the responses with the crowd. The method can be extended and applied to cope with large-scale disaster responses.
Subjects
災情回報
眾包
篩選器
群眾平台
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-103-R01521611-1.pdf
Size
23.32 KB
Format
Adobe PDF
Checksum
(MD5):d54d4212d8227e3ddc58df6e9fbf517b