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For many years, great efforts have been made to use social networks to help build this situational awareness. While there are many models for automatically extracting information from posts, the difficulty remains in detecting and geolocating this information on the fly so that it can be placed on maps. To this end, we first build an appropriate dataset comprised of documents from the French Wikipedia corpus, the dataset from the CAp challenge, and a homemade annotated Twitter dataset extracted during French natural disasters.
We then developed an Entity-Linking pipeline in adequacy with our end-application use case: real-time prediction and peak resiliency. Moreover, the entities geolocated by our model show a strong coherence with the spatiotemporal signature of the natural disasters considered, which suggests that it could usefully contribute to automatic social network analysis for crisis managers. Crisis situations are characterised by a collapse of meaning and by the disorientation of actors.
In addition to traditional actionable channels, which often take time to gather consolidated information from the field, it has become common practice over the past 10 years for crisis practitioners to try to capture the information circulating on social media.
Although Twitter is not the most widely used social media, its user community remains significant: in the case of France, which is considered in this study, the number of active users is estimated at around 10 million i.
