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Exploring COVID-19–Related Stressors: Topic Modeling Study
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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9285672/ https://www.ncbi.nlm.nih.gov/pubmed/35731966 http://dx.doi.org/10.2196/37142 |
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author | Leung, Yue Tong Khalvati, Farzad |
author_facet | Leung, Yue Tong Khalvati, Farzad |
author_sort | Leung, Yue Tong |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9285672 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-92856722022-07-16 Exploring COVID-19–Related Stressors: Topic Modeling Study Leung, Yue Tong Khalvati, Farzad J Med Internet Res Original Paper 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. JMIR Publications 2022-07-13 /pmc/articles/PMC9285672/ /pubmed/35731966 http://dx.doi.org/10.2196/37142 Text en ©Yue Tong Leung, Farzad Khalvati. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 13.07.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Leung, Yue Tong Khalvati, Farzad Exploring COVID-19–Related Stressors: Topic Modeling Study |
title | Exploring COVID-19–Related Stressors: Topic Modeling Study |
title_full | Exploring COVID-19–Related Stressors: Topic Modeling Study |
title_fullStr | Exploring COVID-19–Related Stressors: Topic Modeling Study |
title_full_unstemmed | Exploring COVID-19–Related Stressors: Topic Modeling Study |
title_short | Exploring COVID-19–Related Stressors: Topic Modeling Study |
title_sort | exploring covid-19–related stressors: topic modeling study |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9285672/ https://www.ncbi.nlm.nih.gov/pubmed/35731966 http://dx.doi.org/10.2196/37142 |
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