The Engineering Company for the Development of Digital Systems, Giza, Egypt.
Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt.
PLoS One. 2024 Apr 4;19(4):e0301255. doi: 10.1371/journal.pone.0301255. eCollection 2024.
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，如大流行病和地震，是造成痛苦和伤亡的主要原因之一。政府的危机管理流程在处理这些类型的问题时至关重要。社交媒体平台是有关当前事件和公众意见的主要信息来源之一。因此，它们被广泛用于帮助灾难检测和预防工作。因此，总是需要更好的自动系统来检测和分类社交媒体上的灾难数据。在这项工作中，我们提出了增强的阿拉伯语灾难数据分类模型。所提出的模型利用领域自适应来提供最先进的准确性。我们使用从 Twitter 收集的阿拉伯语灾难数据的标准数据集来测试所提出的模型。实验结果表明，所提供的模型显著优于以前的最先进结果。
