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.
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Shi, J. et al. (2021). Automated Concern Exploration in Pandemic Situations - COVID-19 as a Use Case. In: Uehara, H., Yamaguchi, T., Bai, Q. (eds) Knowledge Management and Acquisition for Intelligent Systems. PKAW 2021. Lecture Notes in Computer Science(), vol 12280. Springer, Cham. https://doi.org/10.1007/978-3-030-69886-7_15
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DOI: https://doi.org/10.1007/978-3-030-69886-7_15
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