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Article

Assessing the Reliability of Relevant Tweets and Validation Using Manual and Automatic Approaches for Flood Risk Communication

1
National Institute on Minority and Health Disparities, National Institutes of Health, Bethesda, MD 20814, USA
2
National Security Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
3
School of Computing, University of Southern Mississippi, 118 College Drive, Hattiesburg, MS 39406, USA
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(9), 532; https://doi.org/10.3390/ijgi9090532
Received: 20 August 2020 / Revised: 31 August 2020 / Accepted: 3 September 2020 / Published: 5 September 2020
(This article belongs to the Special Issue Scaling, Spatio-Temporal Modeling, and Crisis Informatics)
While Twitter has been touted as a preeminent source of up-to-date information on hazard events, the reliability of tweets is still a concern. Our previous publication extracted relevant tweets containing information about the 2013 Colorado flood event and its impacts. Using the relevant tweets, this research further examined the reliability (accuracy and trueness) of the tweets by examining the text and image content and comparing them to other publicly available data sources. Both manual identification of text information and automated (Google Cloud Vision, application programming interface (API)) extraction of images were implemented to balance accurate information verification and efficient processing time. The results showed that both the text and images contained useful information about damaged/flooded roads/streets. This information will help emergency response coordination efforts and informed allocation of resources when enough tweets contain geocoordinates or location/venue names. This research will identify reliable crowdsourced risk information to facilitate near real-time emergency response through better use of crowdsourced risk communication platforms. View Full-Text
Keywords: Twitter; data reliability; risk communication; data mining; Google Cloud Vision API Twitter; data reliability; risk communication; data mining; Google Cloud Vision API
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MDPI and ACS Style

Liu, X.; Kar, B.; Montiel Ishino, F.A.; Zhang, C.; Williams, F. Assessing the Reliability of Relevant Tweets and Validation Using Manual and Automatic Approaches for Flood Risk Communication. ISPRS Int. J. Geo-Inf. 2020, 9, 532. https://doi.org/10.3390/ijgi9090532

AMA Style

Liu X, Kar B, Montiel Ishino FA, Zhang C, Williams F. Assessing the Reliability of Relevant Tweets and Validation Using Manual and Automatic Approaches for Flood Risk Communication. ISPRS International Journal of Geo-Information. 2020; 9(9):532. https://doi.org/10.3390/ijgi9090532

Chicago/Turabian Style

Liu, Xiaohui; Kar, Bandana; Montiel Ishino, Francisco A.; Zhang, Chaoyang; Williams, Faustine. 2020. "Assessing the Reliability of Relevant Tweets and Validation Using Manual and Automatic Approaches for Flood Risk Communication" ISPRS Int. J. Geo-Inf. 9, no. 9: 532. https://doi.org/10.3390/ijgi9090532

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