Abstract
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.
Highlights
Contemporary environmental hazard warning systems, based on highly evolved and specialised forecasting procedures, have replaced people’s reliance on their personal observations and intuition
The US Geological Survey (USGS) was the first environmental institution to recognise the value of such user generated content (UGC), recognising that analysis of the content and geographic distribution of Twitter postings—i.e., ‘social sensors’– can be a useful supplement to instrument-based estimates from physical sensors of earthquake location and magnitude [3]
These results clearly demonstrate that flood-related tags tend to correlate with hydrologically themed tags, which occur in the metadata of the uploaded content in isolation (e.g., ‘river’, ‘water’) or in combination (‘RW’)
Summary
Contemporary environmental hazard warning systems, based on highly evolved and specialised forecasting procedures, have replaced people’s reliance on their personal observations and intuition. Several types of data can be collected to assist specialists to predict where and when an environmental hazard might occur. The recent proliferation of digital social media platforms have introduced a new and additional source of information to be taken into account when designing warning systems and planning their implementation [2]. The US Geological Survey (USGS) was the first environmental institution to recognise the value of such user generated content (UGC), recognising that analysis of the content and geographic distribution of Twitter postings—i.e., ‘social sensors’– can be a useful supplement to instrument-based estimates from physical sensors of earthquake location and magnitude [3]. A more recent study reported on the value of the Flickr image sharing platform in nowcasting of PLOS ONE | DOI:10.1371/journal.pone.0172870. A more recent study reported on the value of the Flickr image sharing platform in nowcasting of PLOS ONE | DOI:10.1371/journal.pone.0172870 February 24, 2017
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
