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

arXiv: https://arxiv.org/abs/2106.16175 · HTML: https://mlsanity.com/paper/2106.16175/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 34 references that are in the map.
- First posted on arXiv 30 Jun 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Depression and Self-Harm Risk Assessment in Online Forums](https://mlsanity.com/paper/1709.01848/) (2017)
- [Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences](https://mlsanity.com/paper/1804.07000/) (2018)
- [An Exploratory Analysis of the Relation Between Offensive Language and Mental Health](https://mlsanity.com/paper/2105.14888/) (2021)
- [Detecting Early Onset of Depression from Social Media Text using Learned Confidence Scores](https://mlsanity.com/paper/2011.01695/) (2020)

## Built on by (most cited first, 16 of 16)

- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [It's Just a Matter of Time: Detecting Depression with Time-Enriched Multimodal Transformers](https://mlsanity.com/paper/2301.05453/) (2023)
- [Emotion fusion for mental illness detection from social media: A survey](https://mlsanity.com/paper/2304.09493/) (2023)
- [Sequence-to-Sequence Lexical Normalization with Multilingual Transformers](https://mlsanity.com/paper/2110.02869/) (2021)
- [On the State of NLP Approaches to Modeling Depression in Social Media: A Post-COVID-19 Outlook](https://mlsanity.com/paper/2410.08793/) (2024)
- [BLUE at Memotion 2.0 2022: You have my Image, my Text and my Transformer](https://mlsanity.com/paper/2202.07543/) (2022)
- [Utilizing ChatGPT Generated Data to Retrieve Depression Symptoms from Social Media](https://mlsanity.com/paper/2307.02313/) (2023)
- [A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media](https://mlsanity.com/paper/2108.00279/) (2021)
- [An End-to-End Set Transformer for User-Level Classification of Depression and Gambling Disorder](https://mlsanity.com/paper/2207.00753/) (2022)
- [Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks](https://mlsanity.com/paper/2306.16891/) (2023)
- [A Survey on Multilingual Mental Disorders Detection from Social Media Data](https://mlsanity.com/paper/2505.15556/) (2025)
- [Deep Knowledge-Infusion For Explainable Depression Detection](https://mlsanity.com/paper/2409.02122/) (2024)
- [Life is not Always Depressing: Exploring the Happy Moments of People Diagnosed with Depression](https://mlsanity.com/paper/2204.13569/) (2022)
- [Temporal fine-tuning for early risk detection](https://mlsanity.com/paper/2505.11280/) (2025)
- [Automatic detection of problem-gambling signs from online texts using large language models](https://mlsanity.com/paper/2312.00804/) (2023)
- [Navigating through the hidden embedding space: steering LLMs to improve mental health assessment](https://mlsanity.com/paper/2510.16373/) (2025)

## 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.