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Tkachenko, Nataliya; Jarvis, Stephen; Procter, Rob (2017)
Publisher: Public Library of Science
Journal: PLoS ONE
Languages: English
Types: Article
Subjects: Bodies of Water, Applied Mathematics, Algorithms, Linguistics, Research Article, Earth Sciences, Freshwater Environments, HM, Mathematics, Sociology, Ecology and Environmental Sciences, Mathematical and Statistical Techniques, Semantics, Simulation and Modeling, Marine and Aquatic Sciences, Communications, Twitter, Physical Sciences, GB, ZA4450, Aquatic Environments, Social Media, Rivers, Statistics (Mathematics), Hydrology, Computer and Information Sciences, Social Networks, Forecasting, Social Communication, Research and Analysis Methods, Medicine, Flooding, Surface Water, Q, R, Social Sciences, Science, Network Analysis, Statistical Methods
Increasingly, user generated content (UGC) in social media postings and their associated metadata such as time and location stamps are being used to provide useful operational information during natural hazard events such as hurricanes, storms and floods. The main advantage of these new sources of data are twofold. First, in a purely additive sense, they can provide much denser geographical coverage of the hazard as compared to traditional sensor networks. Second, they provide what physical sensors are not able to do: By documenting personal observations and experiences, they directly record the impact of a hazard on the human environment. For this reason interpretation of the content (e.g., hashtags, images, text, emojis, etc) and metadata (e.g., keywords, tags, geolocation) have been a focus of much research into social media analytics. However, as choices of semantic tags in the current methods are usually reduced to the exact name or type of the event (e.g., hashtags '#Sandy' or '#flooding'), the main limitation of such approaches remains their mere nowcasting capacity. In this study we make use of polysemous tags of images posted during several recent flood events and demonstrate how such volunteered geographic data can be used to provide early warning of an event before its outbreak.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    The results below are discovered through our pilot algorithms. Let us know how we are doing!

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