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Graph-based joint pandemic concern and relation extraction on Twitter

Citation

Shi, J and Li, W and Yongchareon, S and Yang, Y and Bai, Q, Graph-based joint pandemic concern and relation extraction on Twitter, Expert Systems With Applications, 195 Article 116538. ISSN 0957-4174 (2022) [Refereed Article]


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Copyright 2022 Elsevier Ltd.

DOI: doi:10.1016/j.eswa.2022.116538

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 Details

Item Type:Refereed Article
Keywords:concern detection, COVID-19, auto concern, extraction, concern graph, graph, convolutional network, knowledge representation, concern detection, deep learning
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Knowledge representation and reasoning
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Application software packages
UTAS Author:Bai, Q (Dr Quan Bai)
ID Code:149084
Year Published:2022
Deposited By:Information and Communication Technology
Deposited On:2022-03-07
Last Modified:2022-05-18
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