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

Zhou, J; Zogan, H; Yang, S; Jameel, S; Xu, G; Chen, F

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

Zhou, J

Zogan, H Yang, S

Jameel, S Xu, G Chen, F

Permalink

Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Computational Social Systems, 2021, pp. 1-10
Issue Date:: 2021

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Accepted Manuscript VersionAdobe PDF (3.55 MB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Zhou, J https://orcid.org/0000-0001-6034-644X
dc.contributor.author	Zogan, H
dc.contributor.author	Yang, S https://orcid.org/0000-0002-6772-6805
dc.contributor.author	Jameel, S
dc.contributor.author	Xu, G
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.date.accessioned	2021-01-24T11:38:39Z
dc.date.available	2021-01-24T11:38:39Z
dc.date.issued	2021
dc.identifier.citation	IEEE Transactions on Computational Social Systems, 2021, pp. 1-10
dc.identifier.issn	2373-7476
dc.identifier.issn	2373-7476
dc.identifier.uri	http://hdl.handle.net/10453/145574
dc.description.abstract	The recent COVID-19 pandemic has caused unprecedented impact across the globe. We have also witnessed millions of people with 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. For instance, depression is one of the most common mental health issues according to the findings made by the World Health Organisation (WHO). 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. Such granular level analysis of depression dynamics not only can help authorities such as governmental departments to take corresponding actions more objectively in specific regions if necessary but also allows users to perceive the dynamics of depression over the time.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	http://purl.org/au-research/grants/arc/LP170100891
dc.relation	http://purl.org/au-research/grants/arc/DP200101374
dc.relation	Asian Office of Aerospace R&D
dc.relation.ispartof	IEEE Transactions on Computational Social Systems
dc.relation.isbasedon	10.1109/tcss.2020.3047604
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia
dc.type	Journal Article
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
dc.date.updated	2021-01-24T11:37:28Z
pubs.publication-status	Published

Abstract:

The recent COVID-19 pandemic has caused unprecedented impact across the globe. We have also witnessed millions of people with 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. For instance, depression is one of the most common mental health issues according to the findings made by the World Health Organisation (WHO). 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. Such granular level analysis of depression dynamics not only can help authorities such as governmental departments to take corresponding actions more objectively in specific regions if necessary but also allows users to perceive the dynamics of depression over the time.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/145574