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

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.

Automated summary

Mental illnesses are a global public health problem that negatively impact people's lives and society's health. With the rise of social media, there has been increasing research interest in using user-generated posts to detect mental illness. This article provides a comprehensive survey of approaches that incorporate emotion fusion in the detection of mental illness in social media. It reviews different fusion strategies, discusses challenges faced by researchers in this area, and suggests potential directions for future research.