Assessing the social impacts of extreme weather events using social media

Natural hazards cause major disruption to the UK economy, but their impacts are hard to forecast and observe. This project will develop methods for using social media data to accurately map natural hazards and their impacts. "Social sensing" can be defined as observation of real-world events using unsolicited content from digital communications (e.g. mobile phone call records, social media, web searches, and other online data). The challenge of social sensing is to extract high-quality observations from large numbers of unstructured, patchy, and possibly inaccurate user utterances. If this can be achieved then there are significant opportunities to use this data in areas where observations are not currently available.
One key application area is assessing the impact of natural hazards, where forecasts are routinely produced based on meteorological models, but for which little impact observation data is available for model validation.
Previous work by the investigators has shown that social media can be used successfully as a source of data with which to detect and locate wildfires, floods and extreme rainfall events. However, the methods are at an early stage of development. This project will establish robust methods for two key aspects of the social sensing pipeline for natural hazards:
(1) Content classification. The first part of the project will use machine learning to create text-based classifiers that can automatically categorise social media posts based on their content.
(2) Location inference. Identifying the geographical origin of social media content is essential for hazard impact evaluations, but only a small fraction of social media posts include accurate geotags.
The second part of the project will develop effective machine learning methods for inferring the locations of un-geotagged social media posts.