Name: Arabic Flood Twitter Dataset
Description: It includes 4,037 human-labelled Arabic Twitter messages for four high-risk flood events that occurred in 2018
Provider: Multiple institutions
Paper Title: Crisis Detection from Arabic Tweets
  
Tasks: crisis detection
Venue Name: Workshop on Arabic Corpus Linguistics
Authors: Alaa Alharbi,Mark G. Lee
Abstract: 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