mlsanityData to 7 Oct 2026

Early Risk Detection of Pathological Gambling, Self-Harm and Depression Using BERT

arXiv 2106.16175 ↗First posted 30 Jun 2021cs.CLcited by 16

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Abstract (arXiv)

Early risk detection of mental illnesses has a massive positive impact upon the well-being of people. The eRisk workshop has been at the forefront of enabling interdisciplinary research in developing computational methods to automatically estimate early risk factors for mental issues such as depression, self-harm, anorexia and pathological gambling. In this paper, we present the contributions of the BLUE team in the 2021 edition of the workshop, in which we tackle the problems of early detection of gambling addiction, self-harm and estimating depression severity from social media posts. We employ pre-trained BERT transformers and data crawled automatically from mental health subreddits and obtain reasonable results on all three tasks.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 7 of its 34 references that are in the map. First posted on arXiv 30 Jun 2021.

Builds on

7 of 34 references.

References · 7

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  3. 2020The Pushshift Reddit Datasetcited by 289
  4. 2017Depression and Self-Harm Risk Assessment in Online Forumscited by 81
  5. 2018Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequencescited by 36
  6. 2021An Exploratory Analysis of the Relation Between Offensive Language and Mental Healthcited by 14
  7. 2020Detecting Early Onset of Depression from Social Media Text using Learned Confidence Scorescited by 4

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Cited by year

Citing papers per year: 2021: 3; 2022: 3; 2023: 5; 2024: 2; 2025: 3.

16 papers.

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  1. 2021MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcarecited by 159
  2. 2023It's Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformerscited by 18
  3. 2023Emotion fusion for mental illness detection from social media: A surveycited by 13
  4. 2021Sequence-to-Sequence Lexical Normalization with Multilingual Transformerscited by 7
  5. 2024On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlookcited by 6
  6. 2022BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformercited by 5
  7. 2023Utilizing ChatGPT Generated Data to Retrieve Depression Symptoms from Social Mediacited by 5
  8. 2021A Psychologically Informed Part-of-Speech Analysis of Depression in Social Mediacited by 4
  9. 2022An End-to-End Set Transformer for User-Level Classification of Depression and Gambling Disordercited by 4
  10. 2023Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networkscited by 3
  11. 2025A Survey on Multilingual Mental Disorders Detection from Social Media Datacited by 3
  12. 2024Deep Knowledge-Infusion For Explainable Depression Detectioncited by 2
4 more
  1. 2022Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depressioncited by 1
  2. 2025Temporal fine-tuning for early risk detectioncited by 1
  3. 2023Automatic detection of problem-gambling signs from online texts using large language modelscited by 0
  4. 2025Navigating through the hidden embedding space: steering LLMs to improve mental health assessmentcited by 0