# A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework

arXiv: https://arxiv.org/abs/2203.11856 · HTML: https://mlsanity.com/paper/2203.11856/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 61 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media](https://mlsanity.com/paper/1910.12574/) (2019)
- [Speaker and Time-aware Joint Contextual Learning for Dialogue-act Classification in Counselling Conversations](https://mlsanity.com/paper/2111.06647/) (2021)
- [Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media](https://mlsanity.com/paper/1710.05429/) (2017)
- [Demographic Inference and Representative Population Estimates from Multilingual Social Media Data](https://mlsanity.com/paper/1905.05961/) (2019)
- [Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS](https://mlsanity.com/paper/2104.04140/) (2021)
- ["When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware Attention Framework for Relationship Extraction](https://mlsanity.com/paper/2009.10155/) (2020)
- ["Who can help me?": Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit](https://mlsanity.com/paper/2105.06398/) (2021)
- [NLP is Not enough -- Contextualization of User Input in Chatbots](https://mlsanity.com/paper/2105.06511/) (2021)
- ["What's ur type?" Contextualized Classification of User Types in Marijuana-related Communications using Compositional Multiview Embedding](https://mlsanity.com/paper/1806.06813/) (2018)
- [Gender Detection on Social Networks using Ensemble Deep Learning](https://mlsanity.com/paper/2004.06518/) (2020)
- [Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification](https://mlsanity.com/paper/1801.08570/) (2018)

## Built on by (most cited first, 8 of 8)

- [Knowledge-enhanced Mixed-initiative Dialogue System for Emotional Support Conversations](https://mlsanity.com/paper/2305.10172/) (2023)
- [Critical Behavioral Traits Foster Peer Engagement in Online Mental Health Communities](https://mlsanity.com/paper/2309.01618/) (2023)
- [Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental Health Meme Classification](https://mlsanity.com/paper/2501.15321/) (2025)
- [M-HELP: Using Social Media Data to Detect Mental Health Help-Seeking Signals](https://mlsanity.com/paper/2508.15440/) (2025)
- [Assess and Prompt: A Generative RL Framework for Improving Engagement in Online Mental Health Communities](https://mlsanity.com/paper/2508.16788/) (2025)
- [Not cool, calm or collected: Using emotional language to detect COVID-19 misinformation](https://mlsanity.com/paper/2303.16777/) (2023)
- [Community Needs and Assets: A Computational Analysis of Community Conversations](https://mlsanity.com/paper/2403.13272/) (2024)
- [IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being](https://mlsanity.com/paper/2406.13791/) (2024)

## Abstract (arXiv)

Analyzing gender is critical to study mental health (MH) support in CVD (cardiovascular disease). The existing studies on using social media for extracting MH symptoms consider symptom detection and tend to ignore user context, disease, or gender. The current study aims to design and evaluate a system to capture how MH symptoms associated with CVD are expressed differently with the gender on social media. We observe that the reliable detection of MH symptoms expressed by persons with heart disease in user posts is challenging because of the co-existence of (dis)similar MH symptoms in one post and due to variation in the description of symptoms based on gender. We collect a corpus of 150k items (posts and comments) annotated using the subreddit labels and transfer learning approaches. We propose GeM, a novel task-adaptive multi-task learning approach to identify the MH symptoms in CVD patients based on gender. Specifically, we adapt a knowledge-assisted RoBERTa based bi-encoder model to capture CVD-related MH symptoms. Moreover, it enhances the reliability for differentiating the gender language in MH symptoms when compared to the state-of-art language models. Our model achieves high (statistically significant) performance and predicts four labels of MH issues and two gender labels, which outperforms RoBERTa, improving the recall by 2.14% on the symptom identification task and by 2.55% on the gender identification task.