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Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia

Zhou, Jianlong and Zogan, Hamad and Yang, Shuiqiao and Jameel, Shoaib and Xu, Guandong and Chen, Fang (2020) 'Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia.' IEEE Transactions on Computational Social Systems. ISSN 2329-924X (In Press)

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Abstract

The recent COVID-19 pandemic has caused unprecedented impact across the globe. Many people are going through increased mental health issues, such as depression, stress, worry, fear, disgust, sadness, and anxiety, which have become one of the major public health concerns during this severe health crisis. Depression can cause serious emotional, behavioural and physical health problems with significant consequences, both personal and social costs included. This paper studies community depression dynamics due to COVID-19 pandemic through user-generated content on Twitter. A new approach based on multi-modal features from tweets and Term Frequency-Inverse Document Frequency (TF-IDF) is proposed to build depression classification models. Multi-modal features capture depression cues from emotion, topic and domain-specific perspectives. We study the problem using recently scraped tweets from Twitter users emanating from the state of New South Wales in Australia. Our novel classification model is capable of extracting depression polarities which may be affected by COVID-19 and related events during the COVID-19 period. The results found that people became more depressed after the outbreak of COVID-19. The measures implemented by the government such as the state lockdown also increased depression levels. Further analysis in the Local Government Area (LGA) level found that the community depression level was different across different LGAs.

Item Type: Article
Uncontrolled Keywords: Depression, Multi-modal features, COVID-19, Twitter, Australia
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 02 Feb 2021 14:49
Last Modified: 02 Feb 2021 15:15
URI: http://repository.essex.ac.uk/id/eprint/29674

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