# An Annotated Dataset for Explainable Interpersonal Risk Factors of Mental Disturbance in Social Media Posts

arXiv: https://arxiv.org/abs/2305.18727 · HTML: https://mlsanity.com/paper/2305.18727/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 28 references that are in the map.
- First posted on arXiv 30 May 2023.

## Builds on (most cited first, 5 of 5)

- [A Survey of the State of Explainable AI for Natural Language Processing](https://mlsanity.com/paper/2010.00711/) (2020)
- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction](https://mlsanity.com/paper/2102.09427/) (2021)
- [Detecting Suicide Risk in Online Counseling Services: A Study in a Low-Resource Language](https://mlsanity.com/paper/2209.04830/) (2022)
- [Am I No Good? Towards Detecting Perceived Burdensomeness and Thwarted Belongingness from Suicide Notes](https://mlsanity.com/paper/2206.06141/) (2022)

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

- [MentaLLaMA: Interpretable Mental Health Analysis on Social Media with Large Language Models](https://mlsanity.com/paper/2309.13567/) (2023)
- [Large Language Models in Mental Health Care: a Scoping Review](https://mlsanity.com/paper/2401.02984/) (2024)
- [NBIAS: A Natural Language Processing Framework for Bias Identification in Text](https://mlsanity.com/paper/2308.01681/) (2023)
- [ChatGPT's One-year Anniversary: Are Open-Source Large Language Models Catching up?](https://mlsanity.com/paper/2311.16989/) (2023)
- [MentalGLM Series: Explainable Large Language Models for Mental Health Analysis on Chinese Social Media](https://mlsanity.com/paper/2410.10323/) (2024)
- [LOST: A Mental Health Dataset of Low Self-esteem in Reddit Posts](https://mlsanity.com/paper/2306.05596/) (2023)
- [Clinical Insights: A Comprehensive Review of Language Models in Medicine](https://mlsanity.com/paper/2408.11735/) (2024)
- [mhGPT: A Lightweight Generative Pre-Trained Transformer for Mental Health Text Analysis](https://mlsanity.com/paper/2408.08261/) (2024)
- [FedMentor: Domain-Aware Differential Privacy for Heterogeneous Federated LLMs in Mental Health](https://mlsanity.com/paper/2509.14275/) (2025)
- [M-HELP: Using Social Media Data to Detect Mental Health Help-Seeking Signals](https://mlsanity.com/paper/2508.15440/) (2025)
- [Responsible Evaluation of AI for Mental Health](https://mlsanity.com/paper/2602.00065/) (2026)
- [MHDash: An Online Platform for Benchmarking Mental Health-Aware AI Assistants](https://mlsanity.com/paper/2602.00353/) (2026)
- [TrustMH-Bench: A Comprehensive Benchmark for Evaluating the Trustworthiness of Large Language Models in Mental Health](https://mlsanity.com/paper/2603.03047/) (2026)
- [Improving Disease Detection from Social Media Text via Self-Augmentation and Contrastive Learning](https://mlsanity.com/paper/2405.01597/) (2024)
- [MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders](https://mlsanity.com/paper/2410.06845/) (2024)
- [From Pattern Recognizers to Personalized Companions: A Survey of Large Language Models in Mental Health](https://mlsanity.com/paper/2609.25186/) (2026)

## Abstract (arXiv)

With a surge in identifying suicidal risk and its severity in social media posts, we argue that a more consequential and explainable research is required for optimal impact on clinical psychology practice and personalized mental healthcare. The success of computational intelligence techniques for inferring mental illness from social media resources, points to natural language processing as a lens for determining Interpersonal Risk Factors (IRF) in human writings. Motivated with limited availability of datasets for social NLP research community, we construct and release a new annotated dataset with human-labelled explanations and classification of IRF affecting mental disturbance on social media: (i) Thwarted Belongingness (TBe), and (ii) Perceived Burdensomeness (PBu). We establish baseline models on our dataset facilitating future research directions to develop real-time personalized AI models by detecting patterns of TBe and PBu in emotional spectrum of user's historical social media profile.