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Abstract

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 volumes of information in social media turns out to be a big challenge, especially when sufficient manually labelled data is in the absence during 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 Networks and Bi-directional Long Short Term Memory integrated with Concern Graphs. 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 labelled data. We conduct extensive experiments to evaluate the proposed model by using both manually labelled tweets and automatically labelled tweets. The experimental results show that our model can outperform the state-of-the-art models on real-world datasets.

Item Type:	Article
Authors/Creators:	Shi, J and Li, W and Yongchareon, S and Yang, Y and Bai, Q
Keywords:	concern detection, COVID-19, auto concern, extraction, concern graph, graph, convolutional network, knowledge representation, concern detection, deep learning
Journal or Publication Title:	Expert Systems With Applications
Publisher:	Pergamon-Elsevier Science Ltd
ISSN:	0957-4174
DOI / ID Number:	https://doi.org/10.1016/j.eswa.2022.116538
Copyright Information:	Copyright 2022 Elsevier Ltd.
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