Public concern detection provides potential guidance to the authorities for crisis management before or during a pandemic outbreak. Detecting people's concerns and attention from online social media platforms has been widely acknowledged as an effective approach to relieve public panic and prevent a social crisis. However, detecting concerns in time from massive information in social media turns out to be a big challenge, especially when sufficient manually labeled data is in the absence of public health emergencies, e.g., COVID-19. In this paper, we propose a novel end-to-end deep learning model to identify people's concerns and the corresponding relations based on Graph Convolutional Network and Bi-directional Long Short Term Memory integrated with Concern Graph. Except for the sequential features from BERT embeddings, the regional features of tweets can be extracted by the Concern Graph module, which not only benefits the concern detection but also enables our model to be high noise-tolerant. Thus, our model can address the issue of insufficient manually labeled data. We conduct extensive experiments to evaluate the proposed model by using both manually labeled tweets and automatically labeled tweets. The experimental results show that our model can outperform the state-of-art models on real-world datasets.

翻译：公众关注的发现为当局在大流行病爆发之前或期间的危机管理提供了潜在的指导。从在线社交媒体平台上发现人们的关切和关注已被广泛视为缓解公众恐慌和预防社会危机的有效办法。然而,从社交媒体的大规模信息中及时发现关注是一个巨大的挑战,特别是当足够的人工标签数据是在没有公共卫生紧急情况(如COVID-19)的情况下出现时。在本文件中,我们提出了一个新的端到端的深层次学习模式,以确定人们的关切和基于图表的动态网络和双向短期内存与关注图相结合的相应关系。除BERT嵌入的相继特征外,Twitter的区域特征可以由关注图模块提取,这不仅有利于关注检测,而且还使我们的模型具有高度的噪音耐受性。因此,我们的模型可以解决手动标签数据不足的问题。我们进行广泛的实验,通过使用人工标签的推文和自动标签的推文来评估拟议模式。实验结果显示,我们的模型可以超越现实数据模型的状态模型。