Detecting information from Twitter on landslide hazards in Italy using deep learning models

Background: Mass media are a new and important source of information for any natural disaster, mass emergency, pandemic, economic or political event, or extreme weather event affecting one or more communities in a country. Several techniques have been developed for data mining in social media for many natural events, but few of them have been applied to the automatic extraction of landslide events. In this study, Twitter has been investigated to detect data about landslide events in Italian-language. The main aim is to obtain an automatic text classification on the basis of information about natural hazards. The text classification for landslide events in Italian-language has still not been applied to detect this type of natural hazard. Results: Over 13,000 data were extracted within Twitter considering five keywords referring to landslide events. The dataset was classified manually, providing a solid base for applying deep learning. The combination of BERT + CNN has been chosen for te...