With the rapid development in artificial intelligence, social computing has evolved beyond social informatics toward the birth of social intelligence systems. This paper, therefore, takes initiatives to propose a social behaviour understanding framework with the use of deep neural networks for social and behavioural analysis. The integration of information fusion, person and object detection, social signal understanding, behaviour understanding, and context understanding plays a harmonious role to elicit social behaviours. Three systems, including depression detection, activity recognition and cognitive impairment screening, are developed to evidently demonstrate the importance of social intelligence. The study considerably contributes to the cumulative development of social computing and health informatics. It also provides a number of implications for academic bodies, healthcare practitioners, and developers of socially intelligent agents.

翻译：随着人工智能的迅速发展,社会计算已经从社会信息学发展到社会情报系统的诞生,因此,本文件采取主动行动,提出社会行为理解框架,利用深层神经网络进行社会和行为分析,整合信息聚合、人和物体的检测、社会信号的理解、行为理解和背景理解,在引导社会行为方面发挥了和谐的作用。发展了三种系统,包括抑郁症检测、活动识别和认知障碍筛查,以明确显示社会情报的重要性。这项研究为社会计算和健康信息学的累积发展做出了重要贡献。它也为学术机构、保健从业人员和社会智能代理人的开发者提供了若干影响。