JMIR Publications

ABSTRACT

Background:

The COVID-19 pandemic has affected lives of people from different countries for almost two years. The changes on lifestyles due to the pandemic may cause psychosocial stressors for individuals, and have a potential to lead to mental health problems. To provide high quality mental health supports, healthcare organization need to identify the COVID-19 specific stressors, and notice the trends of prevalence of those stressors.

Objective:

This study aims to apply natural language processing (NLP) on social media data to identify the psychosocial stressors during COVID-19 pandemic, and to analyze the trend on prevalence of stressors at different stages of the pandemic.

Methods:

We obtained dataset of 9266 Reddit posts from subreddit \rCOVID19_support, from 14th Feb 2020 to 19th July 2021. First, we used Latent Dirichlet Allocation (LDA) topic model to identify the topics that were mentioned on the subreddit. Second, analyze the trends on the prevalence of the topics. Third, create lexicons for each of the topics, and identify topics of posts by using lexicon. Then compare the trends on prevalence of topics that identified by LDA and lexicon approaches.

Results:

LDA model has identified six topics from the dataset. According to the result, there was a significant decline on the number of COVID-19 stressors related posts after the vaccine distribution started. This suggest that the distribution of vaccines may reduce the perceived risks of coronavirus. With the progress of vaccination, the result shows an increasing trend on the proportion of posts mentioning the uncertainty about the pandemic. This suggests people may worry whether the pandemic period could be ended due to vaccines, or would there will be new waves of pandemic and lockdown due to new variants.

Conclusions:

Our result presented a dashboard to visualize the trend of prevalence of topics about covid-19 related stressors being discussed on social media platform. The result could provide insights about the prevalence of pandemic related stressors during different stages of COVID-19. The NLP techniques leveraged in this study could also be applied to analyze event specific stressors in the future.