The code used in the implementation.

Natural disasters, like pandemics and earthquakes, are some of the main causes of distress and casualties. Governmental crisis management processes are crucial when dealing with these types of problems. Social media platforms are among the main sources of information regarding current events and public opinion. So, they have been used extensively to aid disaster detection and prevention efforts. Therefore, there is always a need for better automatic systems that can detect and classify disaster data of social media. In this work, we propose enhanced Arabic disaster data classification models. The suggested models utilize domain adaptation to provide state-of-the-art accuracy. We used a standard dataset of Arabic disaster data collected from Twitter for testing the proposed models. Experimental results show that the provided models significantly outperform the previous state-of-the-art results.

自然灾害（如大流行病与地震）是导致人员伤亡与受灾苦难的主要诱因之一。应对此类灾害场景时，政府的危机管理流程至关重要。社交媒体平台是获取时事资讯与公众舆论的核心信息渠道之一，因此被广泛用于辅助灾害侦测与防灾工作。因此，亟需性能更优异的自动化系统，用于侦测并分类社交媒体中的灾害相关数据。本研究中，我们提出了改进型阿拉伯语灾害数据分类模型。所提模型采用域自适应（domain adaptation）技术，可实现当前最优（state-of-the-art）的分类准确率。我们使用从推特（Twitter）采集的标准阿拉伯语灾害数据集，对所提模型进行测试。实验结果表明，所提模型的性能显著优于此前的当前最优结果。