社会网络用户心理健康自动评估研究综述

心理健康问题正迅速成为世界范围内最严重和最普遍的公共卫生问题之一。社会网络的兴起与普及带来大量与社会网络用户心理状态相关的数据。近年来,利用社会网络数据自动评估检测用户心理健康的研究吸引着越来越多的学者,取得了不少成果,但未见对这些成果进行总结分析的工作。该文对社会网络用户心理健康自动评估的相关文献进行评述: 在现有文献基础上总结归纳了心理健康自动评估的概念及界定;从评估任务、社会网络数据集构造、评估用到的特征等方面概述了社会网络用户心理健康自动评估的国内外研究现状;比较分析了现有自动评估方法的特点,包括基于特征工程的方法和基于深度学习的方法;总结了现有研究存在的问题和面临的挑战,包括评估性能问题、数据质量问题、隐私伦理问题、原因抽取问题和自动干预问题等。未来的研究应该结合其他数据流,并需要患者、临床医生和数据科学家之间开展更大的合作,以使机器学习在心理健康问题的原因提取、预防疏导等方面得到新的应用。

A Review on Automatic Assessment of Mental Health for Social Network Users

Mental health problems are increasingly becoming one of the most serious and widespread public health issues in the world. The rise and popularity of social network brings a lot of data related to psychological state of its users. The research of applying social network data to automatically evaluate and detect users' mental health status has attracted more and more scholars in recent years. This paper reviews the relevant literature on the automatic assessment of mental health for social network users. Based on the existing literature, we sum up the concept and definition of automatic assessment of mental health, review the related researches at home and abroad from different aspects of assessment task, social network data-sets construction, the characteristics used in the assessment and so on. The characteristics of existing methods including feature engineering based methods and deep learning basedmethods are compared. Finally, we discuss the problems and challenges for this task, including assessment performance, data quality, privacy ethics, reason extraction and automatic intervention. Future research is suggested to combine other data streams and collaborate between patients, clinicians and data scientists to apply machine learning in causation extraction, prevention and counseling of mental health problems.