Result of Stress Detectionin Social MediaforSocial Interaction

In this paper we've got to notice the strain supported social interaction exploitation media like Facebook, twitter etc. We have a tendency to discover to manually notice individuals' psychological stress via social media. Using actual on-line micro-blog knowledge, we have a tendency to 1st examine the correlation between users' stress and their cheeping content, social engagement and behavior patterns. In social Psychological stress is threatening people's health. It's non-trivial to sight stress timely for proactive care. With the recognition of social media, individuals sharing their daily work and interacting with friends on social media platforms, creating it doable to leverage on-line social network data for stress detection. throughout this paper, we have a tendency to discover that users stress state is closely associated with that of his/her friends in social media, which we have a tendency to use a large-scale dataset from real-world social platforms to systematically study the correlation of users' stress states and social interactions. We've got a bent to first outline a group of stress-related matter, visual, and social attributes from varied aspects, then propose a totally distinctive hybrid model - and issue graph model combined with Convolutional Neural Network to leverage tweet content and social interaction knowledge for stress detection. We've got a bent to boot discover many intriguing phenomena, i.e. the number of social structures of distributed connections (i.e. with no delta connections) of stressed users is around 14 % over that of non-stressed users, indicating that the social structure of stressed users' friends tend to be less connected and fewer troublesome than that of non-stressed users.