# Emotion fusion for mental illness detection from social media: A survey

arXiv: https://arxiv.org/abs/2304.09493 · HTML: https://mlsanity.com/paper/2304.09493/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 178 references that are in the map.
- First posted on arXiv 19 Apr 2023.

## Builds on (most cited first, 30 of 32)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare](https://mlsanity.com/paper/2110.15621/) (2021)
- [Detection and Resolution of Rumours in Social Media: A Survey](https://mlsanity.com/paper/1704.00656/) (2017)
- [Depression and Self-Harm Risk Assessment in Online Forums](https://mlsanity.com/paper/1709.01848/) (2017)
- [Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts](https://mlsanity.com/paper/1906.01267/) (2019)
- [Dreaddit: A Reddit Dataset for Stress Analysis in Social Media](https://mlsanity.com/paper/1911.00133/) (2019)
- [EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis](https://mlsanity.com/paper/2205.01996/) (2022)
- [StageNet: Stage-Aware Neural Networks for Health Risk Prediction](https://mlsanity.com/paper/2001.10054/) (2020)
- [CubeMLP: An MLP-based Model for Multimodal Sentiment Analysis and Depression Estimation](https://mlsanity.com/paper/2207.14087/) (2022)
- [SpanEmo: Casting Multi-label Emotion Classification as Span-prediction](https://mlsanity.com/paper/2101.10038/) (2021)
- [Suicidal Ideation and Mental Disorder Detection with Attentive Relation Networks](https://mlsanity.com/paper/2004.07601/) (2020)
- [Data set creation and empirical analysis for detecting signs of depression from social media postings](https://mlsanity.com/paper/2202.03047/) (2022)
- [Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media](https://mlsanity.com/paper/1710.05429/) (2017)
- [Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines](https://mlsanity.com/paper/1803.07427/) (2018)
- [Early Risk Detection of Pathological Gambling, Self-Harm and Depression Using BERT](https://mlsanity.com/paper/2106.16175/) (2021)
- [Multimodal Emotion Classification](https://mlsanity.com/paper/1903.12520/) (2019)
- [Not Just Depressed: Bipolar Disorder Prediction on Reddit](https://mlsanity.com/paper/1811.04655/) (2018)
- [Detection and Classification of mental illnesses on social media using RoBERTa](https://mlsanity.com/paper/2011.11226/) (2020)
- [EmoMent: An Emotion Annotated Mental Health Corpus from two South Asian Countries](https://mlsanity.com/paper/2208.08486/) (2022)
- [TensiStrength: Stress and relaxation magnitude detection for social media texts](https://mlsanity.com/paper/1607.00139/) (2016)
- [Deep Bag-of-Sub-Emotions for Depression Detection in Social Media](https://mlsanity.com/paper/2103.01334/) (2021)
- [Emotion-based Modeling of Mental Disorders on Social Media](https://mlsanity.com/paper/2201.09451/) (2022)
- [Weakly-Supervised Methods for Suicide Risk Assessment: Role of Related Domains](https://mlsanity.com/paper/2106.02792/) (2021)
- [Dissimilarity Mixture Autoencoder for Deep Clustering](https://mlsanity.com/paper/2006.08177/) (2020)

## Built on by (most cited first, 13 of 13)

- [EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysis](https://mlsanity.com/paper/2401.08508/) (2024)
- [Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Health](https://mlsanity.com/paper/2304.10447/) (2023)
- [Emotion Detection for Misinformation: A Review](https://mlsanity.com/paper/2311.00671/) (2023)
- [Still Not Quite There! Evaluating Large Language Models for Comorbid Mental Health Diagnosis](https://mlsanity.com/paper/2410.03908/) (2024)
- [Explainable AI for Mental Disorder Detection via Social Media: A survey and outlook](https://mlsanity.com/paper/2406.05984/) (2024)
- [Can Third-parties Read Our Emotions?](https://mlsanity.com/paper/2504.18673/) (2025)
- [Augmenting Reddit Posts to Determine Wellness Dimensions impacting Mental Health](https://mlsanity.com/paper/2306.04059/) (2023)
- [Evaluating the Effectiveness of the Foundational Models for Q&A Classification in Mental Health care](https://mlsanity.com/paper/2406.15966/) (2024)
- [Evaluating Lexicon Incorporation for Depression Symptom Estimation](https://mlsanity.com/paper/2404.19359/) (2024)
- [Tracing Human Stress from Physiological Signals using UWB Radar](https://mlsanity.com/paper/2410.10155/) (2024)
- [Expansion Quantization Network: An Efficient Micro-emotion Annotation and Detection Framework](https://mlsanity.com/paper/2411.06160/) (2024)
- [Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinement](https://mlsanity.com/paper/2601.13481/) (2026)
- [PsihoRo: Depression and Anxiety Romanian Text Corpus](https://mlsanity.com/paper/2602.18324/) (2026)

## Abstract (arXiv)

Mental illnesses are one of the most prevalent public health problems worldwide, which negatively influence people's lives and society's health. With the increasing popularity of social media, there has been a growing research interest in the early detection of mental illness by analysing user-generated posts on social media. According to the correlation between emotions and mental illness, leveraging and fusing emotion information has developed into a valuable research topic. In this article, we provide a comprehensive survey of approaches to mental illness detection in social media that incorporate emotion fusion. We begin by reviewing different fusion strategies, along with their advantages and disadvantages. Subsequently, we discuss the major challenges faced by researchers working in this area, including issues surrounding the availability and quality of datasets, the performance of algorithms and interpretability. We additionally suggest some potential directions for future research.