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. 2022 Mar 30;5(4):1716.
doi: 10.23889/ijpds.v5i4.1716. eCollection 2020.

Quantifying depression-related language on social media during the COVID-19 pandemic

Brent D Davis  1 , Dawn Estes McKnight  2 , Daniela Teodorescu  2 , Anabel Quan-Haase  3   4 , Rumi Chunara  5   6 , Alona Fyshe  2   7 , Daniel J Lizotte  1   8
Affiliations

Quantifying depression-related language on social media during the COVID-19 pandemic

Brent D Davis et al. Int J Popul Data Sci. .

Abstract

Introduction: The COVID-19 pandemic had clear impacts on mental health. Social media presents an opportunity for assessing mental health at the population level.

Objectives: 1) Identify and describe language used on social media that is associated with discourse about depression. 2) Describe the associations between identified language and COVID-19 incidence over time across several geographies.

Methods: We create a word embedding based on the posts in Reddit's /r/Depression and use this word embedding to train representations of active authors. We contrast these authors against a control group and extract keywords that capture differences between the two groups. We filter these keywords for face validity and to match character limits of an information retrieval system, Elasticsearch. We retrieve all geo-tagged posts on Twitter from April 2019 to June 2021 from Seattle, Sydney, Mumbai, and Toronto. The tweets are scored with BM25 using the keywords. We call this score rDD. We compare changes in average score over time with case counts from the pandemic's beginning through June 2021.

Results: We observe a pattern in rDD across all cities analyzed: There is an increase in rDD near the start of the pandemic which levels off over time. However, in Mumbai we also see an increase aligned with a second wave of cases.

Conclusions: Our results are concordant with other studies which indicate that the impact of the pandemic on mental health was highest initially and was followed by recovery, largely unchanged by subsequent waves. However, in the Mumbai data we observed a substantial rise in rDD with a large second wave. Our results indicate possible un-captured heterogeneity across geographies, and point to a need for a better understanding of this differential impact on mental health.

Keywords: COVID-19; Twitter; depression; information retrieval; machine learning; public health surveillance; social media.

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Conflict of interest statement

Statement on conflicts of interest: The authors have no conflicts of interest to declare.

Figures

Figure 1: rDD scores over time for each city between April 2019 and June 2021
Figure 1: rDD scores over time for each city between April 2019 and June 2021
Figure 2: rDD scores vs. case counts per city. Points are shaded to show progression over time
Figure 2: rDD scores vs. case counts per city. Points are shaded to show progression over time
Figure 3: Region case counts (centered 7-day window mean daily change) in orange and rDD scores (centered 7-day window mean) in blue for the Twitter posts
Figure 3: Region case counts (centered 7-day window mean daily change) in orange and rDD scores (centered 7-day window mean) in blue for the Twitter posts
See this image and copyright information in PMC

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