Graph-based joint pandemic concern and relation extraction on Twitter. (1st June 2022)

• Record Type: Journal Article
• Authors: Shi, Jingli; Li, Weihua; Yongchareon, Sira; Yang, Yi; Bai, Quan
• Is Part Of: Expert systems with applications. Volume 195(2022)
• Publication Date: 2022-06-01
• Subjects: Concern detection -- COVID-19 -- Auto concern extraction -- Concern graph -- Graph Convolutional Network; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.116538
• Languages: English
• ISSNs: 0957-4174

Abstract

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. Highlights: The inherent features captured by concern graph for improving the model performance. A novel deep learning model for sequential and regional dependency feature learning. A novel end-to-end model for concerns and relations extraction. Generic and effective concerns extraction evaluated by using real-world data.