  Online social media provides a channel for monitoring people's social
behaviors and their mental distress. Due to the restrictions imposed by
COVID-19 people are increasingly using online social networks to express their
feelings. Consequently, there is a significant amount of diverse user-generated
social media content. However, COVID-19 pandemic has changed the way we live,
study, socialize and recreate and this has affected our well-being and mental
health problems. There are growing researches that leverage online social media
analysis to detect and assess user's mental status. In this paper, we survey
the literature of social media analysis for mental disorders detection, with a
special focus on the studies conducted in the context of COVID-19 during
2020-2021. Firstly, we classify the surveyed studies in terms of feature
extraction types, varying from language usage patterns to aesthetic preferences
and online behaviors. Secondly, we explore detection methods used for mental
disorders detection including machine learning and deep learning detection
methods. Finally, we discuss the challenges of mental disorder detection using
social media data, including the privacy and ethical concerns, as well as the
technical challenges of scaling and deploying such systems at large scales, and
discuss the learnt lessons over the last few years.
