Automated Concern Exploration in Pandemic Situations - COVID-19 as a Use Case
| Autor: | Jian Yu, Quan Bai, Sira Yongchareon, Yi Yang, Weihua Li, Jingli Shi, Naimeng Yao |
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| Rok vydání: | 2021 |
| Předmět: |
Coronavirus disease 2019 (COVID-19)
Social network business.industry Computer science Social crisis Deep learning 030231 tropical medicine Internet privacy Sentiment analysis 02 engineering and technology 03 medical and health sciences 0302 clinical medicine 020204 information systems Pandemic 0202 electrical engineering electronic engineering information engineering Key (cryptography) Social media Artificial intelligence business |
| Zdroj: | Knowledge Management and Acquisition for Intelligent Systems ISBN: 9783030698850 PKAW |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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