Exploring COVID-19–Related Stressors: Topic Modeling Study (Preprint)
Bibliographic record
Abstract
BACKGROUND The COVID-19 pandemic has affected the lives of people globally for over 2 years. Changes in lifestyles due to the pandemic may cause psychosocial stressors for individuals and could lead to mental health problems. To provide high-quality mental health support, health care organizations need to identify COVID-19–specific stressors and monitor the trends in the prevalence of those stressors. OBJECTIVE This study aims to apply natural language processing (NLP) techniques to social media data to identify the psychosocial stressors during the COVID-19 pandemic and to analyze the trend in the prevalence of these stressors at different stages of the pandemic. METHODS We obtained a data set of 9266 Reddit posts from the subreddit \rCOVID19_support, from February 14, 2020, to July 19, 2021. We used the latent Dirichlet allocation (LDA) topic model to identify the topics that were mentioned on the subreddit and analyzed the trends in the prevalence of the topics. Lexicons were created for each of the topics and were used to identify the topics of each post. The prevalences of topics identified by the LDA and lexicon approaches were compared. RESULTS The LDA model identified 6 topics from the data set: (1) “fear of coronavirus,” (2) “problems related to social relationships,” (3) “mental health symptoms,” (4) “family problems,” (5) “educational and occupational problems,” and (6) “uncertainty on the development of pandemic.” According to the results, there was a significant decline in the number of posts about the “fear of coronavirus” after vaccine distribution started. This suggests that the distribution of vaccines may have reduced the perceived risks of coronavirus. The prevalence of discussions on the uncertainty about the pandemic did not decline with the increase in the vaccinated population. In April 2021, when the Delta variant became prevalent in the United States, there was a significant increase in the number of posts about the uncertainty of pandemic development but no obvious effects on the topic of fear of the coronavirus. CONCLUSIONS We created a dashboard to visualize the trend in the prevalence of topics about COVID-19–related stressors being discussed on a social media platform (Reddit). Our results provide insights into the prevalence of pandemic-related stressors during different stages of the COVID-19 pandemic. The NLP techniques leveraged in this study could also be applied to analyze event-specific stressors in the future.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".