ArticlePloS one2018
Social sensing of floods in the UK.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- The linguistic and emotional effects of weather on UK social media users.Scientific reports · 2025Article
- Water level prediction using soft computing techniques: A case study in the Malwathu Oya, Sri Lanka.PloS one · 2023Article
- Social Sensing of Heatwaves.Sensors (Basel, Switzerland) · 2021Article
- A social Beaufort scale to detect high winds using language in social media posts.Scientific reports · 2021Article
- Article
- How can social media analytics assist authorities in pandemic-related policy decisions? Insights from Australian states and territories.Health information science and systems · 2020Article
- On detecting urgency in short crisis messages using minimal supervision and transfer learning.Social network analysis and mining · 2020Article
- A global database of historic and real-time flood events based on social media.Scientific data · 2019Article
- Computer Vision and IoT-Based Sensors in Flood Monitoring and Mapping: A Systematic Review.Sensors (Basel, Switzerland) · 2019Review
- A picture tells a thousand…exposures: Opportunities and challenges of deep learning image analyses in exposure science and environmental epidemiology.Environment international · 2019Article
- Scaling laws in geo-located Twitter data.PloS one · 2019Article
- @choo: Tracking Pollen and Hayfever in the UK Using Social Media.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
"Social sensing" is a form of crowd-sourcing that involves systematic analysis of digital communications to detect real-world events. Here we consider the use of social sensing for observing natural hazards. In particular, we present a case study that uses data from a popular social media platform (Twitter) to detect and locate flood events in the UK. In order to improve data quality we apply a number of filters (timezone, simple text filters and a naive Bayes 'relevance' filter) to the data. We then use place names in the user profile and message text to infer the location of the tweets. These two steps remove most of the irrelevant tweets and yield orders of magnitude more located tweets than we have by relying on geo-tagged data. We demonstrate that high resolution social sensing of floods is feasible and we can produce high-quality historical and real-time maps of floods using Twitter.
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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.