# Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework

arXiv: https://arxiv.org/abs/2011.06149 · HTML: https://mlsanity.com/paper/2011.06149/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2020.

## Built on by (most cited first, 25 of 25)

- [Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires](https://mlsanity.com/paper/2204.10432/) (2022)
- [IRFL: Image Recognition of Figurative Language](https://mlsanity.com/paper/2303.15445/) (2023)
- [Symptom Identification for Interpretable Detection of Multiple Mental Disorders](https://mlsanity.com/paper/2205.11308/) (2022)
- [Depression Detection on Social Media with Large Language Models](https://mlsanity.com/paper/2403.10750/) (2024)
- [DEPTWEET: A Typology for Social Media Texts to Detect Depression Severities](https://mlsanity.com/paper/2210.05372/) (2022)
- [Datasets for Depression Modeling in Social Media: An Overview](https://mlsanity.com/paper/2503.21513/) (2025)
- [Multitask learning for recognizing stress and depression in social media](https://mlsanity.com/paper/2305.18907/) (2023)
- [Your Model Is Not Predicting Depression Well And That Is Why: A Case Study of PRIMATE Dataset](https://mlsanity.com/paper/2403.00438/) (2024)
- [A Survey on Multilingual Mental Disorders Detection from Social Media Data](https://mlsanity.com/paper/2505.15556/) (2025)
- [Learning Multi-Tasks with Inconsistent Labels by using Auxiliary Big Task](https://mlsanity.com/paper/2201.02305/) (2022)
- [Self-Supervised Embeddings for Detecting Individual Symptoms of Depression](https://mlsanity.com/paper/2406.17229/) (2024)
- [Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach](https://mlsanity.com/paper/2209.02765/) (2022)
- [Depression detection in social media posts using affective and social norm features](https://mlsanity.com/paper/2303.14279/) (2023)
- [LocalTweets to LocalHealth: A Mental Health Surveillance Framework Based on Twitter Data](https://mlsanity.com/paper/2402.13452/) (2024)
- [Explainable Multimodal Depression Recognition in Clinical Interviews via PHQ-Aligned Symptom Summarization](https://mlsanity.com/paper/2501.16106/) (2025)
- [LLM Assistance for Pediatric Depression](https://mlsanity.com/paper/2501.17510/) (2025)
- [MAMA-Memeia! Multi-Aspect Multi-Agent Collaboration for Depressive Symptoms Identification in Memes](https://mlsanity.com/paper/2512.25015/) (2025)
- [Measuring What Matters!! Assessing Therapeutic Principles in Mental-Health Conversation](https://mlsanity.com/paper/2604.05795/) (2026)
- [Deep Temporal Modelling of Clinical Depression through Social Media Text](https://mlsanity.com/paper/2211.07717/) (2022)
- [Evaluating Lexicon Incorporation for Depression Symptom Estimation](https://mlsanity.com/paper/2404.19359/) (2024)
- [JiraiBench: A Bilingual Benchmark for Evaluating Large Language Models' Detection of Human Self-Destructive Behavior Content in Jirai Community](https://mlsanity.com/paper/2503.21679/) (2025)
- [SynSym: A Synthetic Data Generation Framework for Psychiatric Symptom Identification](https://mlsanity.com/paper/2603.21529/) (2026)
- [FigSIM: A Dataset for Fine-grained Suicide Severity and Figurative Language in Suicide Memes](https://mlsanity.com/paper/2606.02523/) (2026)
- [WPG-MoE: Weak-Prior-Guided Dense Mixture-of-Experts for User-Level Social Media Depression Detection](https://mlsanity.com/paper/2607.04350/) (2026)
- [Structure vs. Chain-of-Thought: Evaluating LLM Criteria Extraction for Depression Severity](https://mlsanity.com/paper/2609.39049/) (2026)

## Abstract (arXiv)

Existing studies on using social media for deriving mental health status of users focus on the depression detection task. However, for case management and referral to psychiatrists, healthcare workers require practical and scalable depressive disorder screening and triage system. This study aims to design and evaluate a decision support system (DSS) to reliably determine the depressive triage level by capturing fine-grained depressive symptoms expressed in user tweets through the emulation of Patient Health Questionnaire-9 (PHQ-9) that is routinely used in clinical practice. The reliable detection of depressive symptoms from tweets is challenging because the 280-character limit on tweets incentivizes the use of creative artifacts in the utterances and figurative usage contributes to effective expression. We propose a novel BERT based robust multi-task learning framework to accurately identify the depressive symptoms using the auxiliary task of figurative usage detection. Specifically, our proposed novel task sharing mechanism, co-task aware attention, enables automatic selection of optimal information across the BERT layers and tasks by soft-sharing of parameters. Our results show that modeling figurative usage can demonstrably improve the model's robustness and reliability for distinguishing the depression symptoms.