mlsanityData to 7 Oct 2026

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

arXiv 2304.09493 ↗First posted 19 Apr 2023cs.CL cs.AIcited by 13

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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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 32 of its 178 references that are in the map. First posted on arXiv 19 Apr 2023.

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32 of 178 references.

References · 32

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20 more
  1. 2022EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysiscited by 75
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 7; 2025: 1; 2026: 2.

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  1. 2024EmoLLMs: A Series of Emotional Large Language Models and Annotation Tools for Comprehensive Affective Analysiscited by 62
  2. 2023Domain-specific Continued Pretraining of Language Models for Capturing Long Context in Mental Healthcited by 25
  3. 2023Emotion Detection for Misinformation: A Reviewcited by 20
  4. 2024Still Not Quite There! Evaluating Large Language Models for Comorbid Mental Health Diagnosiscited by 12
  5. 2024Explainable AI for Mental Disorder Detection via Social Media: A survey and outlookcited by 6
  6. 2025Can Third-parties Read Our Emotions?cited by 4
  7. 2023Augmenting Reddit Posts to Determine Wellness Dimensions impacting Mental Healthcited by 3
  8. 2024Evaluating the Effectiveness of the Foundational Models for Q&A Classification in Mental Health carecited by 1
  9. 2024Evaluating Lexicon Incorporation for Depression Symptom Estimationcited by 0
  10. 2024Tracing Human Stress from Physiological Signals using UWB Radarcited by 0
  11. 2024Expansion Quantization Network: An Efficient Micro-emotion Annotation and Detection Frameworkcited by 0
  12. 2026Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinementcited by 0
1 more
  1. 2026PsihoRo: Depression and Anxiety Romanian Text Corpuscited by 0