Certain words used on social media platforms like Flickr can warn us about extreme weather events before they happen —…
Flickr (PHOTO: GETTY IMAGES)
Certain words used on social media platforms like Flickr can warn us about extreme weather events before they happen — such as hurricanes, storms and floods, suggests new research.
Photographs and key words posted online can signal weather risks developing in specific locations and times — for example, posts about water levels rising can alert the authorities to a potential flood, said the study published in the journal PLOS One.
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"Our analysis demonstrates that metadata in social media image postings enables them to be used as 'social sensors', which can serve as a valuable supplement to instrument-based systems for predicting and monitoring floods, and other kinds of natural hazards," said one of the researchers, Nataliya Tkachenko, from Warwick Institute for the Science of Cities, University of Warwick in Britain.
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The researchers found that tracking certain words used in social media posts around the time of an extreme weather event — such as water and river when there is a flood risk — allows information to be collated to accurately predict which areas will be affected, and how big the impact will be to the infrastructure and human life.
"The opportunities represented by these new data sources are truly exciting as they can help to protect homes, save lives and design more resilient cities," Tkachenko said.
The researchers tracked photos and videos with tags such as river, water and landscape on the social media platform Flickr between 2004 and 2014.
Whilst these words can be used to generally describe natural scenery, the researchers found that in certain time periods before the peak of extreme weather events — and in the locations where they occurred — these words took on a distinct meaning of forecast and warning, showing the weather worsening.
These risk-signalling words can act as 'social sensors', which when used alongside physical meteorological sensors can help to improve the prediction and monitoring of the behaviour and severity of an evolving weather event in multiple areas.
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