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Detection and Analysis of Stress-Related Posts in Reddit Acamedic Communities

Nazzere Oryngozha, Pakizar Shamoi, Ayan Igali

TL;DR

The key findings reveal that the overall stress level in academic texts is 29%.

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 the psychological well-being and stress levels within various communities. This study focuses on detecting and analyzing stress-related posts in Reddit 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, which contains labeled data from Reddit. 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 DReaddit dataset. This combination also performs best in stress detection on human-annotated datasets, with a 72% accuracy rate. Our key findings reveal that posts and comments in professors Reddit communities are the most stressful, compared to other academic levels, including bachelor, graduate, 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 develop measures and interventions to address this issue effectively.

Detection and Analysis of Stress-Related Posts in Reddit Acamedic Communities

TL;DR

The key findings reveal that the overall stress level in academic texts is 29%.

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 the psychological well-being and stress levels within various communities. This study focuses on detecting and analyzing stress-related posts in Reddit 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, which contains labeled data from Reddit. 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 DReaddit dataset. This combination also performs best in stress detection on human-annotated datasets, with a 72% accuracy rate. Our key findings reveal that posts and comments in professors Reddit communities are the most stressful, compared to other academic levels, including bachelor, graduate, 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 develop measures and interventions to address this issue effectively.
Paper Structure (26 sections, 7 equations, 17 figures, 12 tables)

This paper contains 26 sections, 7 equations, 17 figures, 12 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Methods
  4. Data Collection
  5. DReaddit
  6. Test Datasets - Reddit Academic Communities
  7. Data Description
  8. Stress Detection
  9. Pre-processing
  10. Feature extraction
  11. Bag of Words
  12. TF-IDF
  13. Word Embedding
  14. Classification
  15. SVM
  16. ...and 11 more sections

Figures (17)

  • Figure 1: Main techniques to detect stress.
  • Figure 2: Schematic representation of the proposed approach. As a training dataset, we use the Dreaddit pre-labeled dataset Turcan2019 .
  • Figure 3: Word clouds.
  • Figure 4: Illustration of preprocessing steps.
  • Figure 5: After pre-processing.
  • ...and 12 more figures