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Erschienen in:
2021 | OriginalPaper | Buchkapitel
Automated Concern Exploration in Pandemic Situations - COVID-19 as a Use Case
verfasst von : Jingli Shi, Weihua Li, Yi Yang, Naimeng Yao, Quan Bai, Sira Yongchareon, Jian Yu
Erschienen in: Knowledge Management and Acquisition for Intelligent Systems
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Abstract
The recent outbreak of the coronavirus disease (COVID-19) rapidly spreads across most of the countries. To alleviate the panics and prevent any potential social crisis, it is essential to effectively detect public concerns through social media. Twitter, a popular online social network, allows people to share their thoughts, views and opinions towards the latest events and news. In this study, we propose a deep learning-based framework to explore public concerns for COVID-19 automatically, where Twitter has been utilised as the key source of information. We extract and analyse public concerns towards the pandemic. Furthermore, as part of the proposed framework, a knowledge graph of the extracted public concern has been constructed to investigate the interconnections.