ArticleJournal of medical Internet research2022
Exploring COVID-19-Related Stressors: Topic Modeling Study.
Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 29 citations in OpenAlex.
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- Interpretable Topic Modeling of Spontaneous Speech in Depression Using Large Language Models: Multilingual Four-Cohort Study.JMIR mental health · 2026Article
- Global Sentiment Toward Health AI at the Dawn of the ChatGPT Era: Empirical Analysis of Twitter (X) Discourse.Journal of medical Internet research · 2026Article
- Current applications and future directions in natural language processing for news media and mental health.Scientific reports · 2025Article
- Analyzing Themes, Sentiments, and Coping Strategies Regarding Online News Coverage of Depression in Hong Kong: Mixed Methods Study.Journal of medical Internet research · 2025Article
- Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions.Frontiers in immunology · 2025Review
- Highly stressful global event affecting health sciences students: A longitudinal qualitative study.PloS one · 2025Article
- Mapping global public perspectives on mRNA vaccines and therapeutics.NPJ vaccines · 2024Article
- Topic modeling and content analysis of people's anxiety-related concerns raised on a computer-mediated health platform.Scientific reports · 2024Article
- A platform for connecting social media data to domain-specific topics using large language models: an application to student mental health.JAMIA open · 2024Article
- Disruptions in the Cystic Fibrosis Community's Experiences and Concerns During the COVID-19 Pandemic: Topic Modeling and Time Series Analysis of Reddit Comments.Journal of medical Internet research · 2023Article
- Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing.International journal of data science and analytics · 2023Article
- Dear Pandemic: A topic modeling analysis of COVID-19 information needs among readers of an online science communication campaign.PloS one · 2023Article
- Investigating Topic Modeling Techniques to Extract Meaningful Insights in Italian Long COVID Narration.Biotech (Basel (Switzerland)) · 2022Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
objectiveThis 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.
methodsWe 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.
resultsThe 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.
conclusionsWe 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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