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Social Media Data Shows Depression-Related Language Doesn’t Increase After First Wave of Pandemic

Summary: Researchers using artificial intelligence to analyze depression-related language on social media during the first wave of the COVID-19 pandemic have found that people are more resilient than previously thought.

Source: University of Alberta

Researchers who analyzed language about depression on social media during the pandemic say the data suggests that people learn to cope as the waves progress.

University of Alberta researcher Alona Fyshe and his collaborators at the University of Western Ontario have hypothesized that language about depression will increase during each COVID-19 wave. But their study shows that this is not the case.

“There was a huge backlash at first, and then people found their new normal,” says Fyshe, an assistant professor of computer science and psychology. “It’s a message of resilience, people are figuring out how to keep going in an epidemic.”

For the study, the researchers turned their attention to online platforms such as Reddit and Twitter. Fyshe explains that social media is a useful tool for assessing mental health at the population level. Alberta Machine Intelligence Institute and the Canadian CIFAR AI chair.

The researchers first identified keywords by analyzing the type of language banners used in discussions on Reddit. Fyshe explains that the self-description found in these subreddits and forums is not copied on many other social media platforms.

β€œIn fact, we trained a machine learning model that can distinguish the language of people who post on a thread about depression from those who don’t,” says Fyshe.

Using this information and the identified keywords, they turned their attention to Twitter. They analyzed data from four cities (Sydney, Mumbai, Seattle, and Toronto) with different waves of COVID-19 so they could determine which changes in language were due to global trends and which were local. They largely restricted the data to areas with tweets in English so they could use the same methodology to analyze all data.

This shows a woman checking social media on the phone
The researchers first identified keywords by analyzing the type of language banners used in discussions on Reddit. image public domain

Fyshe says the results are surprising. In general, the spikes in COVID-19 cases and the various waves experienced throughout the pandemic were not reflected in the data. In fact, the only city with an increase in depression-related language after the first wave was Mumbai that saw a significant second wave.

The machine learning methods used to scrape Reddit subforums to identify keywords and analyze Twitter data can be applied to a wide variety of topics, Fyshe says. For example, when examining data in Seattle, they found strong backlash against the Black Lives Matter movement.

“It was indicative of a huge change in overall mood – what people were talking about and how people were feeling about the world they lived in.”

About this language and depression research news

Author: Ross Neitz
Source: University of Alberta
Communication: Ross Neitz – University of Alberta
Picture: image public domain

Original research: Open Access.
β€œMeasuring Depression-Related Language on Social Media During the COVID-19 PandemicAlona Fyshe et al. International Journal of Population Data Science

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This shows a young man looking worried.

Abstract

Measuring Depression-Related Language on Social Media During the COVID-19 Pandemic

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The COVID-19 pandemic has had clear effects on mental health. Social media provides an opportunity to assess mental health at the population level.

Goals
1) Identify and describe the language used in social media associated with the discourse about depression. 2) Describe the relationships between the language described and the incidence of COVID-19 over time in various geographies.

methods
We create word insertion based on posts on Reddit’s /r/Depression and use this word insertion to train representations of active authors. We compare these authors with a control group and extract keywords that capture the differences between the two groups. We filter these keywords to match the character limits of face validity and Elasticsearch, an information retrieval system. We’re receiving all geotagged posts on Twitter from April 2019 to June 2021 from Seattle, Sydney, Mumbai and Toronto. Tweets are scored with BM25 using keywords. We call this score rDD. We compare the changes in the average score over time with the number of cases from the start of the pandemic to June 2021.

Results
We observe a pattern in rDD in all cities analyzed: There is a decreasing increase in rDD over time near the onset of the epidemic. However, we are seeing a parallel increase in Mumbai as well, with a second wave of cases.

Results
Our results are in line with other studies showing that the impact of the pandemic on mental health is greatest at baseline, followed by recovery, largely unchanged in subsequent waves. However, in the Mumbai data, we observed a significant increase in rDD with a large second wave. Our results point to possible untapped heterogeneity across geographies and the need to better understand this differential impact on mental health.

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