Emotion fusion for mental illness detection from social media: A survey
It addresses the problem of early mental illness detection for public health by summarizing existing research, but it is incremental as it compiles and analyzes prior work without introducing new methods.
This survey reviews approaches for detecting mental illness from social media posts by fusing emotion information, highlighting different fusion strategies and discussing challenges like dataset availability and algorithm performance.
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.