Detecting community depression dynamics due to COVID-19 pandemic in Australia

Jianlong ZHOU, Hamad ZOGAN, Shuiqiao YANG, Shoaib JAMEEL, Guandong XU, Fang CHEN

Research output: Contribution to journalArticlespeer-review

55 Citations (Scopus)

Abstract

The recent Coronavirus Infectious Disease 2019 (COVID-19) pandemic has caused an 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. Depression can cause serious emotional, behavioral, and physical health problems with significant consequences, both personal and social costs included. This article studies community depression dynamics due to the COVID-19 pandemic through user-generated content on Twitter. A new approach based on multimodal features from tweets and term frequency-inverse document frequency (TF-IDF) is proposed to build depression classification models. Multimodal 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 that 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. Copyright © 2021 IEEE.

Original languageEnglish
Pages (from-to)958-967
JournalIEEE Transactions on Computational Social Systems
Volume8
Issue number4
Early online dateJan 2021
DOIs
Publication statusPublished - Aug 2021

Citation

Zhou, J., Zogan, H., Yang, S., Jameel, S., Xu, G., & Chen, F. (2021). Detecting community depression dynamics due to COVID-19 pandemic in Australia. IEEE Transactions on Computational Social Systems, 8(4), 958-967. https://doi.org/10.1109/TCSS.2020.3047604

Keywords

  • Australia
  • Coronavirus infectious disease 2019 (COVID-19)
  • Depression
  • Multimodal features
  • Twitter

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