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.

In order to prevent and cure debilitating ailments including Alzheimer's disease, schizophrenia, and autism, a new study at Georgia State University's TReNDS Center may result in early detection. This study was published in the Journal of Scientific Reports.

The medical term consists of multiple negative stereotypes while people suffer from mental health issues. It is a myth that patients suffering from mental illness are incompetent or risky. Cutting-edge technology such as artificial intelligence is eradicating the stereotypes associated with mental illness. AI voice analysis is set to transform the issue in the society. This new artificial intelligence model will help to identify mental disorders with early tests and results.