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.

翻译：心理健康问题是全球最普遍的公共卫生问题之一，对人们的生活和社会健康产生负面影响。随着社交媒体日益普及，通过分析用户在社交平台上发布的帖子进行心理健康早期检测的研究兴趣持续增长。基于情绪与心理疾病之间的关联性，利用并融合情绪信息已成为一项有价值的研究方向。本文对融合情绪信息的社交媒体心理疾病检测方法进行了全面综述。我们首先回顾了不同的融合策略及其优缺点，继而探讨了该领域研究者面临的主要挑战，包括数据集可用性与质量、算法性能及可解释性等问题，并提出了若干潜在的未来研究方向。