University Of Tasmania
149084 - Graph based joint pandemic concern and relation extraction on Twitter.pdf (505.16 kB)

Graph-based joint pandemic concern and relation extraction on Twitter

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journal contribution
posted on 2023-05-21, 06:12 authored by Shi, J, Li, W, Yongchareon, S, Yang, Y, Quan BaiQuan Bai
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.


Publication title

Expert Systems With Applications



Article number









School of Information and Communication Technology


Pergamon-Elsevier Science Ltd

Place of publication

The Boulevard, Langford Lane, Kidlington, Oxford, England, Ox5 1Gb

Rights statement

Copyright 2022 Elsevier Ltd.

Repository Status

  • Open

Socio-economic Objectives

Application software packages