Estimating the effect of COVID-19 on mental health: Linguistic indicators of depression during a global pandemic (2020)

Abstract: This preliminary analysis uses a deep LSTM neural network with fastText embeddings to predict population rates of depression on Reddit in order to estimate the effect of COVID-19 on mental health. We find that year over year, depression rates on Reddit are up 50% , suggesting a 15-million person increase in the number of depressed Americans and a $7.5 billion increase in depression related spending. This finding suggests that utility in NLP approaches to longitudinal public-health surveillance.

## introduction the covid-19 pandemic has already plagued the people of the world's physical health, and while the impact of the novel coronavirus on our mental health is less well understood, it is expected to be negative @xcite . even those who never get sick during a pandemic can experience a multitude of psychological stressors during a disease outbreak and those stressors can persist well past the end of the outbreak @xcite . the popular media is aware of the necessity for otherwise healthy people to emphasize "self care"-small acts intended to maintain one's mental health or relieve stress-during these uncertain times. this analysis attempts to quantify the impact that covid-19 has had to date on the population rate of depression through the use of state-of-science depression prediction models and data from the popular social media site reddit. this paper continues an established line of research in the application of natural language processing techniques to the disease of depression @xcite , and mixes it with the rapidly emerging field of @xcite . research in the former area is centered around the notion that language use reflects the thought processes of the speaker and that by assessing the words people use, we can gain insight into their thought processes @xcite . from this, it follows that text classification approaches such as the use of long short-term memory networks @xcite and word embeddings @xcite , such as fasttext @xcite , can be used to classify people's mental health status based on their speech. depression has been studied widely in this way due to its grave impact on those it afflicts @xcite . indeed, even subclinical levels of depression have been shown to reduce quality of life in meaningful and measurable ways @xcite . ## method in this paper, we use data from reddit to measure the potential impact of covid-19 on depression. we do this in two parts: first, we extend the approach from @xcite , using an lstm model to improve the accuracy of depression prediction on social media; then, with this model, we analyze a new dataset-composed of the reddit comments of 20,000 users across the first six months of 2018, 2019, and 2020 foot_0 -in order to estimate the population rate of depression during the covid-19 pandemic. ## preliminary results in this section, we review the preliminary results. we find that an lstm with fasttext embeddings outperforms the baseline approach in wolohan et al. additionally, the lstm indicates that the population rate of depression may be up by 50% in the first four months of 2020 when compared to the first four months of 2019 and 2018. ## discussion with the global covid-19 pandemic wrecking havoc on medical systems and economies worldwide, 2020 will be a year in which many people go through significant hardships. if the analysis herein is to believed, then the fear that many have about a month 2018 2019 2020 ∆ january .32 .29 .52 +79% february .34 .32 .51 +69% march .31 .32 .49 +53% april .33 .34 .42 +24% may .38 .32 --june .34 .34 --average .34 .32 .49 +53% ## ethical considerations as with all works related to public health and mental health, it is important that we consider the ethical implications of our research and make explicit the ethical justification for the work. in particular, work of this kind-namely, public health surveillance-requires special attention because, while disease surveillance is foundational to good public health practice @xcite , it also forgoes the notion of informed consent. when discussing a health issue rife with stigma such as depression, the concern about a researcher mismanaging data and revealing public health information of individuals without their consent is amplified. @xcite enumerate eight broad categories of ethical arguments by which researchers justify forgoing the traditional informed consent requirement for conducting public health surveillance. of those, we argue that our work satisfies the effectiveness, necessity, proportionality, and least infringement requirements. first, with respect to the effectiveness, this study is the first-to my knowledge-quantitative estimate of the impact of covid-19 on population rate of depression. that makes this data valuable to the public health and mental health communities. second, with respect to necessity, it is noteworthy that this approach is minimally intrusive, requiring no contact with the individuals whose comments are used and no interruption of their usage of the reddit service. for proportionality, third, it is important to note that no individual user-level data was shared at any time during this research, and that the identities of reddit users are hidden behind pseudonyms, offering them an additional layer of protection. fourth and finally, the work considered carefully the notion of least infringement, collecting only data that would be necessary for the analysis herein. additionally, in a departure from traditional practices in the nlp community, the data underlying this work will only be shared with researchers who both (1) provide a research design or other publichealth justification for the use of the data and (2) agree to take the necessary efforts to secure the data. ultimately, we view this work as ethically justified based on the precautions noted above and the potentially large increase in population-level depression against which this research warns. if depression is, as we predict, to impact 15 million more americans through 2020 than in previous years, advanced warning is valuable to the american mental-health system. ## conclusion in this paper we show the effectiveness of an lstm text classifier using fasttext word embeddings at predicting user-level depression and use that classifier to estimate the population rate of depression in april 2020 in the midst of the covid-19 pandemic. we estimate that through the first six months of 2020, population rate of depression is up ≈ 50%, corresponding to a 15 million more depressed americans. this analysis suffers from a lack of data and will strengthen as more data becomes available. additional research is needed to confirm or contradict the results presented here, and will be especially valuable when the adjusted population rates of depression are known.