Recently, during natural disasters, the use of social media as a source of actionable information has increased significantly. Finding disaster-related tweets and analyzing their textual content and images can help government agencies and rescue organizations make better decisions. The main challenge of disaster-related message detection on social networking sites is how to identify posts related to emerging disaster events. Since most existing methods extract disaster-specific features that cannot be shared between different disaster events, they are difficult to deal with this challenge. In this article, we propose a novel multimodal adversarial neural network (MANN) to handle the above challenge. MANN consists of three modules: a feature extraction module, a tweet detection module, and a disaster discrimination module. The feature extraction module uses BERT and VGG-19 to learn textual and visual feature representations from posts. Based on multimodal features, the tweet detection module is responsible for identifying posts related to disasters. MANN exploits the disaster discrimination module and adversarial training to captures disaster-invariant features for unseen disaster events. Experimental results on real-world datasets show the proposed model outperforms baseline methods in terms of precision, recall, and F1-measure.

中文翻译：

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通过多模式对抗神经网络检测与灾害相关的推文

最近，在自然灾害期间，使用社交媒体作为可作为行动依据的信息来源的使用显着增加。查找与灾难有关的推文并分析其文本内容和图像可以帮助政府机构和救援组织做出更好的决策。社交网站上与灾难相关的消息检测的主要挑战是如何识别与新出现的灾难事件相关的帖子。由于大多数现有方法提取的特定于灾难的功能无法在不同灾难事件之间共享，因此它们很难应对这一挑战。在本文中，我们提出了一种新颖的多峰对抗神经网络（MANN）来应对上述挑战。MANN由三个模块组成：特征提取模块，推文检测模块和灾难鉴别模块。特征提取模块使用BERT和VGG-19从帖子中学习文本和视觉特征表示。基于多模式功能，推文检测模块负责标识与灾难相关的帖子。MANN利用灾难识别模块和对抗性培训来捕获针对未见灾难事件的灾难不变特征。在实际数据集上的实验结果表明，在精度，召回率和F1度量方面，所提出的模型优于基线方法。MANN利用灾难识别模块和对抗性培训来捕获针对未见灾难事件的灾难不变特征。在真实数据集上的实验结果表明，该模型在精度，召回率和F1度量方面优于基线方法。MANN利用灾难识别模块和对抗性培训来捕获针对未见灾难事件的灾难不变特征。在实际数据集上的实验结果表明，在精度，召回率和F1度量方面，所提出的模型优于基线方法。