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
| 
15 Jun 2021
Review article |
| 15 Jun 2021
| 15 Jun 2021
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
Viewed
Total article views: 7,472 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 10 Jul 2020)
| HTML | XML | Total | BibTeX | EndNote | |
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| 5,701 | 1,633 | 138 | 7,472 | 216 | 196 |
- HTML: 5,701
- PDF: 1,633
- XML: 138
- Total: 7,472
- BibTeX: 216
- EndNote: 196
Total article views: 6,405 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 15 Jun 2021)
| HTML | XML | Total | BibTeX | EndNote | |
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| 5,404 | 880 | 121 | 6,405 | 180 | 163 |
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- PDF: 880
- XML: 121
- Total: 6,405
- BibTeX: 180
- EndNote: 163
Total article views: 1,067 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 10 Jul 2020)
| HTML | XML | Total | BibTeX | EndNote | |
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| 297 | 753 | 17 | 1,067 | 36 | 33 |
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- Total: 1,067
- BibTeX: 36
- EndNote: 33
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Total article views: 7,472 (including HTML, PDF, and XML)
Thereof 7,244 with geography defined
and 228 with unknown origin.
Total article views: 6,405 (including HTML, PDF, and XML)
Thereof 6,179 with geography defined
and 226 with unknown origin.
Total article views: 1,067 (including HTML, PDF, and XML)
Thereof 1,065 with geography defined
and 2 with unknown origin.
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Cited
19 citations as recorded by crossref.
- Blockchain-Based Event Detection and Trust Verification Using Natural Language Processing and Machine Learning Z. Shahbazi & Y. Byun https://doi.org/10.1109/ACCESS.2021.3139586
- Methodology for visual analysis of psychological tension in online discourse on coronavirus vaccination A. Khakimova et al. https://doi.org/10.1108/GKMC-06-2024-0368
- Determining Impact Scores of Tweets in Turkish at Disaster Times O. Ozek & A. Kumluca Topalli https://doi.org/10.1061/NHREFO.NHENG-2734
- From information to action: supply-demand matching strategies for social media-based emergency response Q. Ji et al. https://doi.org/10.1016/j.ssci.2026.107308
- Classification and severity assessment of disaster losses based on multi-modal information in social media W. Zhou et al. https://doi.org/10.1016/j.ipm.2025.104179
- DisTGranD: Granular event/sub-event classification for disaster response A. Adesokan et al. https://doi.org/10.1016/j.osnem.2024.100297
- Emotions-Based Disaster Tweets Classification: Real or Fake M. Alfonse & M. Gawich https://doi.org/10.37394/23209.2023.20.34
- Rapid disaster damage assessment using deep adversarial sliced Wasserstein domain adaptation F. AlNaimi et al. https://doi.org/10.1007/s00521-026-12151-7
- Linguistic patterns in social media content from crisis and non-crisis zones: A case study of Hurricane Ian L. Dinh & S. Walczak https://doi.org/10.1016/j.ipm.2025.104061
- Quantifying Urban Linguistic Diversity Related to Rainfall and Flood across China with Social Media Data J. Qian et al. https://doi.org/10.3390/ijgi13030092
- Tracing online flood conversations across borders: a watershed-level analysis of geo-social media topics during the 2021 European flood S. Dujardin et al. https://doi.org/10.5194/nhess-25-2351-2025
- A decision support system for extracting artificial intelligence-driven insights from live twitter feeds on natural disasters F. Sufi https://doi.org/10.1016/j.dajour.2022.100130
- Empowering crisis information extraction through actionability event schemata and domain-adaptive pre-training Y. Zhang et al. https://doi.org/10.1016/j.im.2024.104065
- Leveraging Disruptive Technologies for Faster and More Efficient Disaster Response Management C. Calle Müller et al. https://doi.org/10.3390/su162310730
- MSBKA: A Multi-Strategy Improved Black-Winged Kite Algorithm for Feature Selection of Natural Disaster Tweets Classification G. Mu et al. https://doi.org/10.3390/biomimetics10010041
- Parameter-efficient fine-tuning of Llama models for crisis-tweet categorization: A Pareto-informed trade-off analysis A. Sasikumar et al. https://doi.org/10.1016/j.rineng.2026.110247
- Geoinformation Harvesting From Social Media Data: A community remote sensing approach X. Zhu et al. https://doi.org/10.1109/MGRS.2022.3219584
- Vision-Language Models in Remote Sensing: Current progress and future trends X. Li et al. https://doi.org/10.1109/MGRS.2024.3383473
- Quantifying the temporal dynamics of environmental awareness through longitudinal social media analysis M. Stojcheva et al. https://doi.org/10.1063/5.0310191
19 citations as recorded by crossref.
- Blockchain-Based Event Detection and Trust Verification Using Natural Language Processing and Machine Learning Z. Shahbazi & Y. Byun https://doi.org/10.1109/ACCESS.2021.3139586
- Methodology for visual analysis of psychological tension in online discourse on coronavirus vaccination A. Khakimova et al. https://doi.org/10.1108/GKMC-06-2024-0368
- Determining Impact Scores of Tweets in Turkish at Disaster Times O. Ozek & A. Kumluca Topalli https://doi.org/10.1061/NHREFO.NHENG-2734
- From information to action: supply-demand matching strategies for social media-based emergency response Q. Ji et al. https://doi.org/10.1016/j.ssci.2026.107308
- Classification and severity assessment of disaster losses based on multi-modal information in social media W. Zhou et al. https://doi.org/10.1016/j.ipm.2025.104179
- DisTGranD: Granular event/sub-event classification for disaster response A. Adesokan et al. https://doi.org/10.1016/j.osnem.2024.100297
- Emotions-Based Disaster Tweets Classification: Real or Fake M. Alfonse & M. Gawich https://doi.org/10.37394/23209.2023.20.34
- Rapid disaster damage assessment using deep adversarial sliced Wasserstein domain adaptation F. AlNaimi et al. https://doi.org/10.1007/s00521-026-12151-7
- Linguistic patterns in social media content from crisis and non-crisis zones: A case study of Hurricane Ian L. Dinh & S. Walczak https://doi.org/10.1016/j.ipm.2025.104061
- Quantifying Urban Linguistic Diversity Related to Rainfall and Flood across China with Social Media Data J. Qian et al. https://doi.org/10.3390/ijgi13030092
- Tracing online flood conversations across borders: a watershed-level analysis of geo-social media topics during the 2021 European flood S. Dujardin et al. https://doi.org/10.5194/nhess-25-2351-2025
- A decision support system for extracting artificial intelligence-driven insights from live twitter feeds on natural disasters F. Sufi https://doi.org/10.1016/j.dajour.2022.100130
- Empowering crisis information extraction through actionability event schemata and domain-adaptive pre-training Y. Zhang et al. https://doi.org/10.1016/j.im.2024.104065
- Leveraging Disruptive Technologies for Faster and More Efficient Disaster Response Management C. Calle Müller et al. https://doi.org/10.3390/su162310730
- MSBKA: A Multi-Strategy Improved Black-Winged Kite Algorithm for Feature Selection of Natural Disaster Tweets Classification G. Mu et al. https://doi.org/10.3390/biomimetics10010041
- Parameter-efficient fine-tuning of Llama models for crisis-tweet categorization: A Pareto-informed trade-off analysis A. Sasikumar et al. https://doi.org/10.1016/j.rineng.2026.110247
- Geoinformation Harvesting From Social Media Data: A community remote sensing approach X. Zhu et al. https://doi.org/10.1109/MGRS.2022.3219584
- Vision-Language Models in Remote Sensing: Current progress and future trends X. Li et al. https://doi.org/10.1109/MGRS.2024.3383473
- Quantifying the temporal dynamics of environmental awareness through longitudinal social media analysis M. Stojcheva et al. https://doi.org/10.1063/5.0310191
Saved (final revised paper)
Latest update: 23 Jun 2026
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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