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

Citation: de Bruijn, J., de Moel, H., Jongman, B., Wagemaker, J., and Aerts, J. C. J. H.: TAGGS: Grouping Tweets to Improve Global Geotagging for Disaster Response, Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2017-203, 2017.
Jens de Bruijn et al.
Interactive discussionStatus: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version      Supplement - Supplement
 
RC1: 'TAGGS review', Anonymous Referee #1, 19 Jun 2017 Printer-friendly Version 
AC1: 'Response to reviewer #1', Jens de Bruijn, 30 Aug 2017 Printer-friendly Version Supplement 
 
RC2: 'paper review', Anonymous Referee #2, 20 Jun 2017 Printer-friendly Version 
AC2: 'Response to reviewer #2', Jens de Bruijn, 30 Aug 2017 Printer-friendly Version 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.

Viewed

Total article views: 402 (including HTML, PDF, and XML)

HTML PDF XML Total BibTeX EndNote
310 86 6 402 2 5

Views and downloads (calculated since 13 Jun 2017)

Cumulative views and downloads (calculated since 13 Jun 2017)

Viewed (geographical distribution)

Total article views: 402 (including HTML, PDF, and XML)

Thereof 400 with geography defined and 2 with unknown origin.

Country # Views %
  • 1

Saved

Discussed

Latest update: 18 Nov 2017
Publications Copernicus
Download
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...
Share