ArticleScientific data2019
A global database of historic and real-time flood events based on social media.
Article in Scientific data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Identifying women with poor experience of post-abortion care: a cross-sectional study in two African hospitals in humanitarian settings.BMC public health · 2025Article
- FloodCastBench: A Large-Scale Dataset and Foundation Models for Flood Modeling and Forecasting.Scientific data · 2025Article
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- A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment.Sensors (Basel, Switzerland) · 2024Review
- Assessing post-abortion care using the WHO quality of care framework for maternal and newborn health: a cross-sectional study in two African hospitals in humanitarian settings.Reproductive health · 2024Article
- Citizen scientists' engagement in flood risk-related data collection: a case study in Bui River Basin, Vietnam.Environmental monitoring and assessment · 2024Article
- Article
- Article
- Flood exposure and poverty in 188 countries.Nature communications · 2022Article
- Article
- A multi-modal approach towards mining social media data during natural disasters - a case study of Hurricane Irma.International journal of disaster risk reduction : IJDRR · 2021Article
- Urban flooding events pose risks of virus spread during the novel coronavirus (COVID-19) pandemic.The Science of the total environment · 2021Article
- Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning.PloS one · 2021Article
- A global database of historic and real-time flood events based on social media.Scientific data · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Early event detection and response can significantly reduce the societal impact of floods. Currently, early warning systems rely on gauges, radar data, models and informal local sources. However, the scope and reliability of these systems are limited. Recently, the use of social media for detecting disasters has shown promising results, especially for earthquakes. Here, we present a new database for detecting floods in real-time on a global scale using Twitter. The method was developed using 88 million tweets, from which we derived over 10,000 flood events (i.e., flooding occurring in a country or first order administrative subdivision) across 176 countries in 11 languages in just over four years. Using strict parameters, validation shows that approximately 90% of the events were correctly detected. In countries where the first official language is included, our algorithm detected 63% of events in NatCatSERVICE disaster database at admin 1 level. Moreover, a large number of flood events not included in NatCatSERVICE were detected. All results are publicly available on www.globalfloodmonitor.org.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.