Story of COVID's mental health impact - a thread

February 03, 2021

Twitter has long provided a short, sharp take on the community's fears, anxieties and experiences. Now, data scientists have analysed 94 million tweets from the first months of the pandemic to track COVID-19's effect on mental health in NSW.

The research team used machine learning to develop a model able to capture data indicating depression, stress, anxiety and suicidal thoughts among users of the social media platform.

The aim was to tap into popular technology to help public health experts identify changes in community levels of depression over time.

The World Health Organisation highlighted early in 2020 that the pandemic would likely have a negative impact on mental health, with the disease affecting many facets of life including work, health and relationships.

Researchers from the University of Technology Sydney (UTS) and the University of Essex, UK, developed their novel classification model to tease out the psychological impact of COVID-19 outbreaks and government policies such as lockdowns.

While Australia has been less affected by COVID-19 than other countries around the globe, the results show the first wave of cases and the resulting lockdown still had a profound impact on mental health in the community.

"Social media provides a real-time snapshot of the thoughts, feelings and activities of people's daily lives. Every tweet signals a user's state of mind and emotional wellbeing at that moment," says co-author Professor Guandong Xu, from the UTS School of Computer Science.

"Aggregation of these digital traces makes it possible to monitor mental health at a large-scale, which has become a new, growing area of interest in public health and health care research.

"Identifying community depression dynamics can help governments and policymakers better understand the psychological impacts of policy decisions, and identify communities that may require increased public health support," he says.

The researchers captured and analysed data from 94 million tweets posted by social media users in 128 local government areas in New South Wales between 1 January and 22 May 2020.

The machine-learning based depression detection model classified the content of tweets according to topic, emotion - including the use of emojis - and recognised symptoms of depression such as fatigue, weight loss, feelings of worthlessness and suicidal ideation.

The model revealed a significant jump in depression levels at the start of the Covid-19 outbreak in New South Wales, around 8 March, reaching a peak on 26 March 2020 that coincided with the highest number of recorded cases.

Government measures such as the state lockdown on 31 March appeared to slightly increase depression levels, although easing lockdown did not reduce depression.

While local government areas measured different levels of depression, these were not closely linked to outbreaks.

The researchers also found that during lockdown more than 40% of Twitter users increased the time they spent on the platform.
-end-
The paper, Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia, is published in IEEE Transactions on Computational Social Systems.

University of Technology Sydney

Related Depression Articles from Brightsurf:

Children with social anxiety, maternal history of depression more likely to develop depression
Although researchers have known for decades that depression runs in families, new research from Binghamton University, State University of New York, suggests that children suffering from social anxiety may be at particular risk for depression in the future.

Depression and use of marijuana among US adults
This study examined the association of depression with cannabis use among US adults and the trends for this association from 2005 to 2016.

Maternal depression increases odds of depression in offspring, study shows
Depression in mothers during and after pregnancy increased the odds of depression in offspring during adolescence and adulthood by 70%.

Targeting depression: Researchers ID symptom-specific targets for treatment of depression
For the first time, physician-scientists at Beth Israel Deaconess Medical Center have identified two clusters of depressive symptoms that responded to two distinct neuroanatomical treatment targets in patients who underwent transcranial magnetic brain stimulation (TMS) for treatment of depression.

A biological mechanism for depression
Researchers report that in depressed individuals there are increased amounts of an unmodified structural protein, called tubulin, in lipid rafts compared with non-depressed individuals.

Depression in adults who are overweight or obese
In an analysis of primary care records of 519,513 UK adults who were overweight or obese between 2000-2016 and followed up until 2019, the incidence of new cases of depression was 92 per 10,000 people per year.

Why stress doesn't always cause depression
Rats susceptible to anhedonia, a core symptom of depression, possess more serotonin neurons after being exposed to chronic stress, but the effect can be reversed through amygdala activation, according to new research in JNeurosci.

Which comes first: Smartphone dependency or depression?
New research suggests a person's reliance on his or her smartphone predicts greater loneliness and depressive symptoms, as opposed to the other way around.

Depression breakthrough
Major depressive disorder -- referred to colloquially as the 'black dog' -- has been identified as a genetic cause for 20 distinct diseases, providing vital information to help detect and manage high rates of physical illnesses in people diagnosed with depression.

CPAP provides relief from depression
Researchers have found that continuous positive airway pressure (CPAP) treatment of obstructive sleep apnea (OSA) can improve depression symptoms in patients suffering from cardiovascular diseases.

Read More: Depression News and Depression Current Events
Brightsurf.com is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to Amazon.com.