Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Extracting Reliable Twitter Data for Flood Risk Communication using Manual Assessment and Google Vision API from Text and Images

Xiaohui Liu
* ORCID logo ,
Bandana Kar
,
Francisco Alejandro Montiel Ishino
,
Chaoyang Zhang
,
Faustine Williams
ORCID logo
Version 1 : Received: 20 August 2020 / Approved: 22 August 2020 / Online: 22 August 2020 (02:32:40 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Abstract

While Twitter has been touted to provide up-to-date information about 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 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/street networks. This information will help emergency response coordination efforts and informed allocation of resources when enough tweets contain geocoordinates or locations/venue names. This research will help identify reliable crowdsourced risk information to enable near-real time emergency response through better use of crowdsourced risk communication platforms.

Keywords

Twitter; data reliability; risk communication; data mining; Google Cloud Vision API

Subject

Social Sciences, Geography, Planning and Development

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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