Exploring COVID-19 Related Stressors Using Topic Modeling

TL;DR

This study uses NLP and topic modeling on Reddit data to identify and analyze trends in COVID-19 related psychosocial stressors, providing insights for mental health support during different pandemic stages.

Contribution

It applies LDA and lexicon methods to social media data to track COVID-19 stressors over time, offering a novel visualization dashboard for trend analysis.

Findings

Identified key COVID-19 related stressors discussed on Reddit.

Visualized the prevalence trends of stressors during the pandemic.

Demonstrated NLP techniques' applicability to event-specific stressor analysis.

Abstract

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. 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. We obtained dataset of 9266 Reddit posts from subreddit \rCOVID19_support, from 14th Feb ,2020 to 19th July 2021. We used Latent Dirichlet Allocation (LDA) topic model and lexicon methods to identify the topics that were mentioned on the subreddit. 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.