  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.
