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

TLDR

The COVID‑19 pandemic has had unprecedented global impact, leading to widespread mental health problems such as depression, which carries significant personal and social costs. This study investigates community depression dynamics during COVID‑19 in Australia using Twitter user‑generated content. The authors develop a multimodal depression classification model that uses emotion, topic, and domain‑specific features extracted via TF‑IDF from tweets collected in New South Wales. The model reveals that depression levels rose after COVID‑19 began and increased further during government lockdowns.

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.