TAGGS: Grouping Tweets to Improve Global Geoparsing for Disaster Response

J.A. de Bruijn, H. de Moel, B. Jongman, Jurjen Wagemaker, J.C.J.H. Aerts

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Timely and accurate information about ongoing events are crucial for relief organizations seeking to effectively respond to disasters. Recently, social media platforms, especially 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, geoparsing algorithms extract and can locate geographical locations referenced in a tweet’s text. This paper describes TAGGS, a new algorithm that enhances location disambiguation by employing both metadata and the contextual spatial information of groups of tweets referencing the same location regarding a specific disaster type. Validation demonstrated that TAGGS approximately attains a recall of 0.82 and precision of 0.91. Without lowering precision, this roughly doubles the number of correctly found administrative subdivisions and cities, towns, and villages as compared to individual geoparsing. We applied TAGGS to 55.1 million flood-related tweets in 12 languages, collected over 3 years. We found 19.2 million tweets mentioning one or more flood locations, which can be towns (11.2 million), administrative subdivisions (5.1 million), or countries (4.6 million). In the future, TAGGS could form the basis for a global event detection system.
Original languageEnglish
Article number2
Pages (from-to)1-14
Number of pages14
JournalJournal of Geovisualization and Spatial Analysis
Volume2
Issue number2
Early online date26 Dec 2017
DOIs
Publication statusPublished - Jun 2018

Funding

The research leading to these results has received funding from Netherlands Organization for Scientific Research (NWO) VICI (grant number 453-14-006) and the European Community’s Seventh Framework Programme (FP7) ENHANCE (grant number 308438).

FundersFunder number
Seventh Framework Programme
FP7 Environment308438
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Stichting voor de Technische Wetenschappen453-14-006

    Keywords

    • geoparsing
    • geocoding
    • geotagging
    • floods
    • twitter
    • geolocation
    • disaster response

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