Articles | Volume 21, issue 8
Nat. Hazards Earth Syst. Sci., 21, 2407–2425, 2021
https://doi.org/10.5194/nhess-21-2407-2021
Nat. Hazards Earth Syst. Sci., 21, 2407–2425, 2021
https://doi.org/10.5194/nhess-21-2407-2021

Research article 17 Aug 2021

Research article | 17 Aug 2021

Social sensing of high-impact rainfall events worldwide: a benchmark comparison against manually curated impact observations

Michelle D. Spruce et al.

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Cited articles

Aisha, T. S., Wok, S., Manaf, A. M. A., and Ismail, R.: Exploring the Use of Social Media During the 2014 Flood in Malaysia, Procedia - Soc. Behav. Sci., 211, 931–937, https://doi.org/10.1016/J.SBSPRO.2015.11.123, 2015. 
Arthur, R., Boulton, C. A., Shotton, H., and Williams, H. T. P.: Social sensing of floods in the UK, available at: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0189327 (last access: 17 December 2020), 2018. 
Bossu, R., Fallou, L., Landès, M., Roussel, F., Julien-Laferrière, S., Roch, J., and Steed, R.: Rapid Public Information and Situational Awareness After the November 26, 2019, Albania Earthquake: Lessons Learned From the LastQuake System, Front. Earth Sci., 8, 235, https://doi.org/10.3389/feart.2020.00235, 2020. 
Boulton, C. A., Shotton, H., and Williams, H. T. P.: Using social media to detect and locate wildfires, in Tenth International AAAI Conference on Web and Social Media, AAAI, available at: https://www.aaai.org/ocs/index.php/ICWSM/ICWSM16/paper/view/13204 (last access: 14 October 2019), 2016. 
Brouwer, T., Eilander, D., van Loenen, A., Booij, M. J., Wijnberg, K. M., Verkade, J. S., and Wagemaker, J.: Probabilistic flood extent estimates from social media flood observations, Nat. Hazards Earth Syst. Sci., 17, 735–747, https://doi.org/10.5194/nhess-17-735-2017, 2017. 
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Short summary
Despite increased use of impact-based weather warnings, the social impacts of extreme weather events lie beyond the reach of conventional meteorological observations and remain difficult to quantify. This study compares data collected from the social media platform Twitter with a manually curated database of high-impact rainfall events across the globe between January–June 2017. Twitter is found to be a good detector of impactful rainfall events and, therefore, a useful source of impact data.
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