Articles | Volume 21, issue 6
https://doi.org/10.5194/nhess-21-1825-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-21-1825-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Review article: Detection of actionable tweets in crisis events
Technical University of Munich, Data Science in Earth Observation, Munich, Germany
Jens Kersten
German Aerospace Center, Institute of Data Science, Jena, Germany
Friederike Klan
German Aerospace Center, Institute of Data Science, Jena, Germany
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Total article views: 3,659 (including HTML, PDF, and XML)
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Total article views: 987 (including HTML, PDF, and XML)
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- A decision support system for extracting artificial intelligence-driven insights from live twitter feeds on natural disasters F. Sufi 10.1016/j.dajour.2022.100130
- Empowering crisis information extraction through actionability event schemata and domain-adaptive pre-training Y. Zhang et al. 10.1016/j.im.2024.104065
- Leveraging Disruptive Technologies for Faster and More Efficient Disaster Response Management C. Calle Müller et al. 10.3390/su162310730
- MSBKA: A Multi-Strategy Improved Black-Winged Kite Algorithm for Feature Selection of Natural Disaster Tweets Classification G. Mu et al. 10.3390/biomimetics10010041
- Geoinformation Harvesting From Social Media Data: A community remote sensing approach X. Zhu et al. 10.1109/MGRS.2022.3219584
- Vision-Language Models in Remote Sensing: Current progress and future trends X. Li et al. 10.1109/MGRS.2024.3383473
2 citations as recorded by crossref.
Latest update: 14 Aug 2025
Short summary
Messages on social media can be an important source of information during crisis situations. This article reviews approaches for the reliable detection of informative messages in a flood of data. We demonstrate the varying goals of these approaches and present existing data sets. We then compare approaches based (1) on keyword and location filtering, (2) on crowdsourcing, and (3) on machine learning. We also point out challenges and suggest future research.
Messages on social media can be an important source of information during crisis situations....
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