A Broad Survey on Detection of Depression in Societal Platforms using Machine Learning Model for the Public Health Care System

Anxiety and depression are on the rise, particularly since the COVID-19 epidemic, yet detection rates have not kept pace. There has been a lot of talk about people showing signs of mental health problems on social media sites like Facebook, Twitter etc. The social media anxiety and sadness detected using machine learning algorithms is considered and reviewed in this research. Soon after depression was recognized as a major public health problem around the world, efforts were made to improve its detection. The speed with which technology is developing is changing the way people talk to one another. Standardized scales that rely on patients’ subjective reactions or clinical diagnoses from attending clinicians are typically used to detect depression, despite their limitations. First, the replies patients give on conventional standardized measures may be influenced by factors such as the patient’s current mental state, the nature of the clinician-patient relationship, the patient’s current mood, and the patient’s previous experiences and memory bias. Social media platforms like Twitter, Facebook, Telegram, and Instagram have exploded in popularity as places for people to open up about their innermost thoughts, psyche, and feelings with the proliferation of the Internet. Text is analyzed using psychological analysis to pull out relevant aspects, characteristics, and information from the perspectives of users. Psychological analysts rely on social media for the early identification of depressive symptoms and patterns of behavior. A person’s social network may tell us a lot about the thoughts and actions that precede the start of depression, such as the person’s isolation, the importance they place on themselves, and the hours they spend awake. This research presents a brief review that attempts to synthesize the literature on the use of Machine Learning (ML) techniques on social media text data for the purpose of detecting depressive symptoms and to point the way toward future research in this field.