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@choo: Tracking Pollen and Hayfever in the UK Using Social Media

1
Computer Science, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QE, UK
2
The Alan Turing Institute, 96 Euston Road, London NW1 2DB, UK
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(12), 4434; https://doi.org/10.3390/s18124434
Received: 26 October 2018 / Revised: 3 December 2018 / Accepted: 10 December 2018 / Published: 14 December 2018
(This article belongs to the Special Issue Social Sensing)
Allergic rhinitis (hayfever) affects a large proportion of the population in the United Kingdom. Although relatively easily treated with medication, symptoms nonetheless have a substantial adverse effect on wellbeing during the summer pollen season. Provision of accurate pollen forecasts can help sufferers to manage their condition and minimise adverse effects. Current pollen forecasts in the UK are based on a sparse network of pollen monitoring stations. Here, we explore the use of “social sensing” (analysis of unsolicited social media content) as an alternative source of pollen and hayfever observations. We use data from the Twitter platform to generate a dynamic spatial map of pollen levels based on user reports of hayfever symptoms. We show that social sensing alone creates a spatiotemporal pollen measurement with remarkable similarity to measurements taken from the established physical pollen monitoring network. This demonstrates that social sensing of pollen can be accurate, relative to current methods, and suggests a variety of future applications of this method to help hayfever sufferers manage their condition. View Full-Text
Keywords: hayfever; social media; pollen; social sensing; crowdsourcing hayfever; social media; pollen; social sensing; crowdsourcing
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MDPI and ACS Style

Cowie, S.; Arthur, R.; Williams, H.T.P. @choo: Tracking Pollen and Hayfever in the UK Using Social Media. Sensors 2018, 18, 4434.

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