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.

翻译：在线社交媒体为监测人们的社会行为和他们的心理痛苦提供了一个渠道。由于COVID-19 人施加的限制,人们越来越多地使用在线社交网络来表达他们的感情。因此,存在着大量由用户生成的社交媒体内容。然而,COVID-19 流行病改变了我们的生活、研究、社交和再创造方式,影响了我们的福祉和心理健康问题。越来越多的研究利用在线社交媒体分析来检测和评估用户的心理状况。在本文中,我们调查社会媒体分析文献,以发现精神失常,特别侧重于2020-2021年期间在COVID-19背景下进行的研究。首先,我们从特征提取类型(从语言使用模式到美学偏好和在线行为)对所调查的研究进行分类。第二,我们探索用来检测精神失常的方法,包括机器学习和深层学习检测方法。最后,我们讨论利用社会媒体数据检测精神失常的挑战,包括隐私和伦理问题,以及大规模扩大和部署这种系统的技术挑战,并讨论过去几年中的经验教训。