Abstract
During the various catastrophic events of recent years, the use of social media to communicate timely information in crisis periods has become a common practice, allowing affected population to quickly publish a considerable amount of disaster information which can help managers making correct and quick decisions. In this paper, we propose a new real-time alert model for the management of natural or anthropogenic disasters. This model is based on a semi-supervised inductive technique to use unlabeled multi-source data, which are often abundant during a crisis event, with less data previously labeled than previous event. We use two sets of real-world crisis data from Facebook and Twitter manually tagged to launch streaming retrieval of relevant content: it is used for evaluating our proposed approach. Preliminary results are satisfactory.
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