TAGGS: Grouping Tweets to Improve Global Geoparsing for Disaster Response
| العنوان: | TAGGS: Grouping Tweets to Improve Global Geoparsing for Disaster Response |
|---|---|
| المؤلفون: | Jurjen Wagemaker, Hans de Moel, Brenden Jongman, Jens de Bruijn, Jeroen C. J. H. Aerts |
| المساهمون: | Water and Climate Risk |
| المصدر: | de Bruijn, J A, de Moel, H, Jongman, B, Wagemaker, J & Aerts, J C J H 2018, ' TAGGS: Grouping Tweets to Improve Global Geoparsing for Disaster Response ', Journal of Geovisualization and Spatial Analysis, vol. 2, no. 2, 2, pp. 1-14 . https://doi.org/10.1007/s41651-017-0010-6 Journal of Geovisualization and Spatial Analysis, 2(2):2, 1-14. Springer |
| بيانات النشر: | Springer, 2018. |
| سنة النشر: | 2018 |
| مصطلحات موضوعية: | Geography, Planning and Development, 02 engineering and technology, geotagging, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Earth and Planetary Sciences (miscellaneous), SDG 13 - Climate Action, Social media, Computers in Earth Sciences, Spatial analysis, Event (computing), Data science, geoparsing, SDG 11 - Sustainable Cities and Communities, Metadata, Geotagging, Geolocation, disaster response, geolocation, geocoding, floods, Geocoding, 020201 artificial intelligence & image processing, twitter, Geoparsing |
| الوصف: | 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. |
| اللغة: | English |
| تدمد: | 2509-8829 |
| DOI: | 10.1007/s41651-017-0010-6 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::581ba2561a5714062781bf1e6bb3f0e4 https://research.vu.nl/en/publications/bce24ed9-6c48-41fe-a116-e15275a7da76 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....581ba2561a5714062781bf1e6bb3f0e4 |
| قاعدة البيانات: | OpenAIRE |
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