Stress Based Detection on Social Interactions in Social Networks

Abstract

Psychological stress is ominous person’s health. It is non-trivial to detect stress timely for proactive care. With the attractive of social media, person are used to sharing their daily task and communicating with friends on social media platforms, making it feasible to leverage online social network data for stress detection. In this paper, we find that users stress condition is closely related to that of his/her friends in social media, and we employ a large-scale dataset from real-world social platforms to systematically examine the connection of users’ stress condition’s and social interactions. We first define a set of stress-related textual analysis, visual, and social attributes from various aspects, and then propose a novel hybrid model – a factor graph model combined with Convolutional Neural Network to leverage tweet content and social interaction information for stress detection. Experimental results show that the proposed model can better the detection performance by 6-9% in F1-score. By further analysing the social interaction data, we also discover several intriguing phenomena, i.e. the number of social structures of sparse connections (i.e. with no delta connections) of stressed users is around 14% more than that of non- stressed users, indicating that the social structure of stressed users’ friends tend to be less connected and less complicated than that of non-stressed us.