Analysis of Depression in News Articles Before and After the COVID-19 Pandemic Based on Unsupervised Learning and Latent Dirichlet Allocation Topic Modeling

Abstract

As of 2023, South Korea maintains the highest suicide rate among OECD countries, accompanied by a notably high prevalence of depression. The onset of the COVID-19 pandemic in 2020 further exacerbated the prevalence of depression, attributed to shifts in lifestyle and societal factors. In this research, differences in depression-related keywords were analyzed using a news big data set, comprising 45,376 news articles from January 1st, 2016 to November 30th, 2019 (pre-COVID-19 pandemic) and 50,311 news articles from December 1st, 2019 to May 5th, 2023 (post-pandemic declaration). Latent Dirichlet Allocation (LDA) topic modeling was utilized to discern topics pertinent to depression. LDA topic modeling outcomes indicated the emergence of topics related to suicide and depression in association with COVID-19 following the pandemic's onset. Exploring strategies to manage such scenarios during future infectious disease outbreaks becomes imperative.