mlsanityData to 14 Oct 2026

Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks

arXiv 2306.16891 ↗First posted 29 Jun 2023cs.IR cs.AI cs.HCcited by 3

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Abstract (arXiv)

Early diagnosis of mental disorders and intervention can facilitate the prevention of severe injuries and the improvement of treatment results. Using social media and pre-trained language models, this study explores how user-generated data can be used to predict mental disorder symptoms. Our study compares four different BERT models of Hugging Face with standard machine learning techniques used in automatic depression diagnosis in recent literature. The results show that new models outperform the previous approach with an accuracy rate of up to 97%. Analyzing the results while complementing past findings, we find that even tiny amounts of data (like users' bio descriptions) have the potential to predict mental disorders. We conclude that social media data is an excellent source of mental health screening, and pre-trained models can effectively automate this critical task.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 12 of its 49 references that are in the map. First posted on arXiv 29 Jun 2023.

Builds on

12 of 49 references.

References · 12

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  5. 2021MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcarecited by 161
  6. 2020Comparing BERT against traditional machine learning text classificationcited by 46
  7. 2021Early Risk Detection of Pathological Gambling, Self-Harm and Depression Using BERTcited by 16
  8. 2022DEPTWEET: A Typology for Social Media Texts to Detect Depression Severitiescited by 12
  9. 2023Calibration of Transformer-based Models for Identifying Stress and Depression in Social Mediacited by 12
  10. 2020Adapting Deep Learning Methods for Mental Health Prediction on Social Mediacited by 10
  11. 2023Predicting mental health using social media: A roadmap for future developmentcited by 1
  12. 2023Depression detection in social media posts using affective and social norm featurescited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practicecited by 11
  2. 2026MentalMARBERT: Domain-Adaptive Pre-training and Two-Stage Fine-Tuning for Arabic Mental Health Disorders Detectioncited by 0
  3. 2026Evaluating Document-Tuned Transformer Representations for Person-level Mental Health Assessmentcited by 0