  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.
