Abstract
Nowadays, the significance of monitoring stress levels and recognizing early signs of mental illness cannot be overstated. Automatic stress detection in text can proactively help manage stress and protect mental well-being. In today’s digital era, social media platforms reflect various communities’ psychological well-being and stress levels. This study focuses on detecting and analyzing stress-related posts in Reddit’s academic communities. Due to online education and remote work, these communities have become central for academic discussions and support. We classify text as stressed or not using natural language processing and machine learning classifiers, with Dreaddit as our training dataset containing labeled Reddit data. Next, we collect and analyze posts from various academic subreddits. We identified that the most effective individual feature for stress detection is the Bag of Words, paired with the Logistic Regression classifier, achieving a 77.78% accuracy rate and an F1 score of 0.79 on the pre-labeled DReaddit dataset. To validate our model’s applicability to detect stress in the specific context of academia, we conducted a supplementary experiment by manually annotating 100 posts from academic subreddits, achieving a 72% accuracy rate. Our key findings reveal that the overall stress level in academic texts is 29%. Posts and comments in professors’ Reddit communities are the most stressful compared to other academic levels, including bachelor’s, graduate’s, and Ph.D. students. This research contributes to our understanding of the stress levels within academic communities. It can help academic institutions and online communities effectively develop measures and interventions to address this issue.