  Mental disorders such as depression and suicidal ideation are hazardous,
affecting more than 300 million people over the world. However, on social
media, mental disorder symptoms can be observed, and automated approaches are
increasingly capable of detecting them. The considerable number of social media
users and the tremendous quantity of user-generated data on social platforms
provide a unique opportunity for researchers to distinguish patterns that
correlate with mental status. This research offers a roadmap for analysis,
where mental state detection can be based on machine learning techniques. We
describe the common approaches for predicting and identifying the disorder
using user-generated content. This research is organized according to the data
collection, feature extraction, and prediction algorithms. Furthermore, we
review several recent studies conducted to explore different features of
candidate profiles and their analytical methods. Following, we debate various
aspects of the development of experimental auto-detection frameworks for
identifying users who suffer from disorders, and we conclude with a discussion
of future trends. The introduced methods can help complement screening
procedures, identify at-risk people through social media monitoring on a large
scale, and make disorders easier to treat in the future.
