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Cordyceps@LT-EDI: Depression Detection with Reddit and Self-training

arXiv 2310.01418 ↗First posted 24 Sep 2023cs.CL cs.AIcited by 0

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Abstract (arXiv)

Depression is debilitating, and not uncommon. Indeed, studies of excessive social media users show correlations with depression, ADHD, and other mental health concerns. Given that there is a large number of people with excessive social media usage, then there is a significant population of potentially undiagnosed users and posts that they create. In this paper, we propose a depression severity detection system using a semi-supervised learning technique to predict if a post is from a user who is experiencing severe, moderate, or low (non-diagnostic) levels of depression. Namely, we use a trained model to classify a large number of unlabelled social media posts from Reddit, then use these generated labels to train a more powerful classifier. We demonstrate our framework on Detecting Signs of Depression from Social Media Text - LT-EDI@RANLP 2023 shared task, where our framework ranks 3rd overall.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 24 references that are in the map. First posted on arXiv 24 Sep 2023.

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References · 9

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2019Self-training with Noisy Student improves ImageNet classificationcited by 1,304
  4. 2020Rethinking Pre-training and Self-trainingcited by 352
  5. 2021MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcarecited by 159
  6. 2016Forecasting the onset and course of mental illness with Twitter datacited by 25
  7. 2023Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Healthcited by 25
  8. 2017Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Mediacited by 24
  9. 2021Better Self-training for Image Classification through Self-supervisioncited by 9

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