Effects of the covid-19 pandemic on anxiety disorders: A topic modelling study

Abstract

Psychological well-being was drastically affected due to changes in the social environment brought on by the COVID-19 pandemic. This study investigates how the COVID-19 pandemic affected individuals with anxiety disorders using narratives published on social media. Mid-pandemic and pre-pandemic datasets are collected and compiled from post titles published in the “r/Anxiety” subreddit, an internet community for people suffering from anxiety disorders. Then, a topic modelling pipeline based on clustering sentence embeddings in combination with sentiment analysis is applied to uncover trends in user narratives with the associated sentiment. In this study, three clustering algorithms, namely the Gaussian mixture model, spectral clustering, and k-means, were evaluated for their performance in clustering sentence embeddings using an internal evaluation method. The clusters formed reflected symptoms and types of anxiety disorders, demonstrating that unsupervised machine learning techniques, in particular topic modelling, can be used to detect mental health issues in social media data.