Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia
Jianlong Zhou, Hamad Zogan, Shuiqiao Yang, Shoaib Jameel, Guandong Xu,, Fang Chen

TL;DR
This study analyzes how COVID-19 impacted community depression in Australia using Twitter data, proposing a multi-modal classification model to detect depression trends and regional differences during the pandemic.
Contribution
Introduces a novel multi-modal feature-based depression classification model applied to Twitter data, revealing regional and temporal depression dynamics during COVID-19 in Australia.
Findings
Depression levels increased after COVID-19 outbreak.
Government measures like lockdowns heightened depression.
Regional differences in depression levels across LGAs were identified.
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…
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Taxonomy
TopicsMental Health via Writing · Misinformation and Its Impacts · Sentiment Analysis and Opinion Mining
