# Social Behaviour Understanding using Deep Neural Networks: Development of Social Intelligence Systems

arXiv: https://arxiv.org/abs/2105.09489 · HTML: https://mlsanity.com/paper/2105.09489/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 20 May 2021.

## Builds on (most cited first, 4 of 4)

- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [CNN Architectures for Large-Scale Audio Classification](https://mlsanity.com/paper/1609.09430/) (2016)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Cognitive Assessment Estimation from Behavioral Responses in Emotional Faces Evaluation Task -- AI Regression Approach for Dementia Onset Prediction in Aging Societies](https://mlsanity.com/paper/1911.12135/) (2019)

## Built on by (most cited first, 1 of 1)

- ["Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions](https://mlsanity.com/paper/2506.09354/) (2025)

## Abstract (arXiv)

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.