“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.
Methods for detecting and tracking natural hazards continue to increase in coverage, resolution and reliability. However, information on the social impacts of natural hazards is often lacking. Here we test the feasibility of using social media data (Twitter and Instagram) to detect and map an important class of natural hazard: wildfires. We analyse social media posts associated with wildfires over several time periods and compare them with wildfire occurrence data derived from satellite-based remote sensing data and on-the-ground observations. For the whole of the contiguous United States, we find significant temporal correlations between wildfire-related social media activity and wildfire occurrence, but also that there is substantial variation in the strength of this relationship at smaller spatial scales (states and counties). We then explore the utility of social media for location of wildfire events, finding good evidence to support further development of such methods. We conclude by discussing several challenges and opportunities for application of this novel data resource to provide information on impacts of natural hazards.
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