Effect of virtual reality distraction on children's behavior during dental treatment: a systematic review of the literature.

Mental health problems, especially depression, affect around 300 million people worldwide and is a major concern. Unfortunately, depression plays a significant role in suicide, especially in young people (10–24 years old), since suicide is the third largest cause of death in this age group. Despite its seriousness, depression is often misdiagnosed and treated without therapy; more than half of individuals who suffer from it never get help. Social media platforms have become vast digital spaces where people share experiences and emotions, offering insight into mental states. The widespread use of these platforms generates large volumes of data reflecting users’ sentiments, opinions, and mental conditions. Some posts indicate individuals may be experiencing mental health challenges, raising the need to identify and support them efficiently. Despite signs of depression in a significant portion of the population, many remain untreated, often due to reluctance to seek help. Automated analysis of social media could aid in early detection of mental health issues. Recent research focuses on using social media activity to detect depression, employing models like LSTM, CNNs, and transformer-based NLP tools such as BERT. These models excel at capturing language nuances due to their design and large-scale training on diverse datasets. However, their lack of interpretability limits their application in sensitive areas like mental health. To address this, proposed a topic-enriched auxiliary task to enhance model understanding, while analyzed posting schedules. Still, current methods mainly offer binary classifications without explainability, reducing their value to users and healthcare professionals. Research on improving interpretability in this context remains limited.