  "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.
