Arabic Flood Twitter Dataset
It includes 4,037 human-labelled Arabic Twitter messages for four high-risk flood events that occurred in 2018
Multiple institutions
Crisis Detection from Arabic Tweets

crisis detection
Workshop on Arabic Corpus Linguistics
Alaa Alharbi,Mark G. Lee
Social media (SM) platforms such as Twitter offer a rich source of real-time information about crises from which useful information can be extracted to support situational
awareness. The task of automatically identifying SM messages related to a specific event
poses many challenges, including processing
large volumes of short, noisy data in real time.
This paper explored the problem of extracting crisis-related messages from Arabic Twitter data. We focused on high-risk floods as
they are one of the main hazards in the Middle East. In this work, we presented a goldstandard Arabic Twitter corpus for four highrisk floods that occurred in 2018. Using the
annotated dataset, we investigated the performance of different classical machine learning
(ML) and deep neural network (DNN) classifiers. The results showed that deep learning is
promising in identifying flood-related posts