  Classification of crisis events, such as natural disasters, terrorist attacks
and pandemics, is a crucial task to create early signals and inform relevant
parties for spontaneous actions to reduce overall damage. Despite crisis such
as natural disasters can be predicted by professional institutions, certain
events are first signaled by civilians, such as the recent COVID-19 pandemics.
Social media platforms such as Twitter often exposes firsthand signals on such
crises through high volume information exchange over half a billion tweets
posted daily. Prior works proposed various crisis embeddings and classification
using conventional Machine Learning and Neural Network models. However, none of
the works perform crisis embedding and classification using state of the art
attention-based deep neural networks models, such as Transformers and
document-level contextual embeddings. This work proposes CrisisBERT, an
end-to-end transformer-based model for two crisis classification tasks, namely
crisis detection and crisis recognition, which shows promising results across
accuracy and f1 scores. The proposed model also demonstrates superior
robustness over benchmark, as it shows marginal performance compromise while
extending from 6 to 36 events with only 51.4% additional data points. We also
proposed Crisis2Vec, an attention-based, document-level contextual embedding
architecture for crisis embedding, which achieve better performance than
conventional crisis embedding methods such as Word2Vec and GloVe. To the best
of our knowledge, our works are first to propose using transformer-based crisis
classification and document-level contextual crisis embedding in the
literature.
