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Exploring COVID-19 Related Stressors Using Topic Modeling.
In: Journal of Medical Internet Research, Jg. 24 (2022-07-01), Heft 7, S. N.PAG
Online
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Zugriff:
Background: The COVID-19 pandemic has affected the lives of people globally for over two 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, healthcare organizations need to identify COVID-19-specific stressors and monitor the trends of 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 of the prevalence of these stressors at different stages of the pandemic. Methods: We obtained a dataset of 9266 Reddit posts from the subreddit rCOVID19_support, from 14th Feb 2020 to 19th July 2021. We used Latent Dirichlet Allocation (LDA) topic model to identify the topics that were mentioned on the subreddit, and analyzed the trends of the prevalence of the topics. Lexicons were created for each of the topics, and were used to identify the topics of each post. The prevalence of topics identified by the LDA and lexicon approaches were compared. Results: LDA model identified six topics from the dataset: 1) "fear of coronavirus", 2) "problems related to social relationships", 3) "mental health symptoms", 4) "family problems", 5) "educational and occupational problem", 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 the 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 US, there was a significant increase in the number of posts about the uncertainty of pandemic development but no obvious effects on the topic of the fear of coronavirus. Conclusions: We have created a dashboard to visualize the trend of prevalence of topics about COVID-19-related stressors being discussed on the 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. Clinicaltrial: [ABSTRACT FROM AUTHOR]
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Titel: |
Exploring COVID-19 Related Stressors Using Topic Modeling.
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Autor/in / Beteiligte Person: | Leung, Yue Tong ; Khalvati, Farzad |
Zeitschrift: | Journal of Medical Internet Research, Jg. 24 (2022-07-01), Heft 7, S. N.PAG |
Veröffentlichung: | 2022 |
Medientyp: | academicJournal |
ISSN: | 1439-4456 (print) |
DOI: | 10.2196/37142 |
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