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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/nhess-2017-203
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/nhess-2017-203
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Submitted as: research article 13 Jun 2017

Submitted as: research article | 13 Jun 2017

Review status
This discussion paper is a preprint. It has been under review for the journal Natural Hazards and Earth System Sciences (NHESS). The revised manuscript was not accepted.

TAGGS: Grouping Tweets to Improve Global Geotagging for Disaster Response

Jens de Bruijn1, Hans de Moel1, Brenden Jongman1,2, Jurjen Wagemaker3, and Jeroen C. J. H. Aerts1 Jens de Bruijn et al.
  • 1Institute for Environmental Studies, VU University, Amsterdam, 1081HV, The Netherlands
  • 2Global Facility for Disaster Reduction and Recovery, World Bank Group, Washington D.C., 20433, USA
  • 3FloodTags, The Hague, 2511 BE, The Netherlands

Abstract. The availability of timely and accurate information about ongoing events is important for relief organizations seeking to effectively respond to disasters. Recently, social media platforms, and in particular Twitter, have gained traction as a novel source of information on disaster events. Unfortunately, geographical information is rarely attached to tweets, which hinders the use of Twitter for geographical applications. As a solution, analyses of a tweet’s text, combined with an evaluation of its metadata, can help to increase the number of geo-located tweets. This paper describes a new algorithm (TAGGS), that georeferences tweets by using the spatial information of groups of tweets mentioning the same location. This technique results in a roughly twofold increase in the number of geo-located tweets as compared to existing methods. We applied this approach to 35.1 million flood-related tweets in 12 languages, collected over 2.5 years. In the dataset, we found 11.6 million tweets mentioning one or more flood locations, which can be towns (6.9 million), provinces (3.3 million), or countries (2.2 million). Validation demonstrated that TAGGS correctly located about 65–75 % of the tweets. As a future application, TAGGS could form the basis for a global event detection and monitoring system.

Jens de Bruijn et al.
Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Jens de Bruijn et al.
Model code and software

Toponym-based Algorithm for Grouped Geotagging of Social media J. de Bruijn https://doi.org/10.5281/zenodo.802959

Jens de Bruijn et al.
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Short summary
In this work we present TAGSS, an algorithm that extracts and geolocates tweets using locations mentioned in the text of a tweet. We have applied TAGGS to flood events. However, TAGGS has enormous potential for application in the broad field of geosciences and natural hazards of any kind in particular, where availability of timely and accurate information about the impacts of an ongoing event can assist relief organizations in enhancing their disaster response activities.
In this work we present TAGSS, an algorithm that extracts and geolocates tweets using locations...
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