Enhancing Interpretability in Anxiety Detection on Reddit: A Machine Learning Approach with LIME and Topic Modeling
Year: 2025
Type: article
Abstract: In modern society, mental disorders, particularly anxiety, are becoming more and more prevalent concerns. Individuals express their opinions and feelings on social media platforms like Reddit which offers valuable information for understanding mental health. This study applies BERTopic and Local Interpretable Model-agnostic Explanations (LIME) to demonstrate the interpretation of machine learning models in anxiety detection. To analyze and identify the linguistic patterns, a novel dataset has been collected from Reddit communities utilizing multiple subreddits pertaining to anxiety and casual conversations. For topic modeling BERTopic was used to discover key topics in discussions. In addition, TF-IDF features were used to train a Random Forest Classifier, which obtained an accuracy of 88% in classifying the post between anxiety and non-anxiety. Furthermore, to ensure transparency in model decision making process, LIME was used to examine textual features that influence models. This study emphasizes the importance of explainability with regards to AI-assisted mental health solutions while also demonstrating the usefulness of social media data in analyzing how anxiety is articulated, and language is employed differently.
Authors: Ghulam Fatima Memon, Muhammad Shehram Shah Syed, Erum Saba, Shakeel Ahmed Laghari
Language: English
Topic: Mental Health via Writing
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4412655972