Social sensing of floods in the UK

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 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.

Using social media to measure impacts of named storm events in the United Kingdom and Ireland

“…This study uses a hybrid approach of methods from previous studies which successfully collected and found useful meaning from Twitter data relating to weather events or natural hazards (Lachlan et al ., ; Arthur et al ., ; Cowie et al ., ; Halse et al ., ). Social media data were collected, filtered for relevance and geo‐located.…”

“…Using the methods outlined by Arthur et al . (), tweets relating to named storms and storm‐associated weather conditions were collected using the Twitter Streaming API (via a Python script using the Twython package [McGrath, ]). This API returns all tweets up to a limit of 1% of the total volume of tweets at any point in time.…”

“…Tweets in the Storm Names collection were particularly in need of additional filtering, since there are many celebrities or other individuals who share the same names as the storms studied here. To achieve this, the methods used successfully in previous studies (Arthur et al ., ; Cowie et al ., ) were employed.…”

“…When grouped together by topic or location, large numbers of social media posts can therefore be used to develop an understanding of a range of issues. Social sensing of this nature has already been successfully used to detect natural hazards such as earthquakes (Sakaki et al ., ), wildfires (Boulton et al ., ) and floods (Brouwer et al ., ; Tkachenko et al ., ; Arthur et al ., ; Rossi et al ., ). A number of studies have used social media to understand impacts of hurricanes in the United States (Guan and Chen, ; Cervone et al ., ; Kryvasheyeu et al ., ; Morss et al ., ; Kim and Hastak, ; Wu and Cui, ).…”

A graph‐based method for social sensing of infrastructure disruptions in disasters

“…Recent studies (Arthur, Boulton, Shotton, & Williams, 2018;Li, Wang, Emrich, & Guo, 2018;Long, Wang, Chen, Jin, & Yu, 2011) have highlighted the importance and applications of social sensing in detecting relevant events and understanding the situation for disaster response and recovery. Social sensing is a technique for collecting and analyzing social posts that deliver online users' observations and sentiments regarding their physical environment.…”