Social sensing of floods in the UK
Arthur, R; Boulton, C; Shotton, H; et al.Williams, HTP
Date: 31 January 2018
Journal
PLoS ONE
Publisher
Public Library of Science (PLoS)
Publisher DOI
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 ...
“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.
Computer Science
Faculty of Environment, Science and Economy
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