Psychological stress is threatening people’s health. It is non-trivial to detect stress timely for proactive care. With the popularity of social media, people are used to sharing their daily activities and interacting with friends on social media platforms, making it feasible to leverage online social network data for stress detection. It is find that users stress state is closely related to that of his/her friends in social media, and a large-scale dataset from real-world social platforms is employed to systematically study the correlation of users’ stress states and social interactions. It is first defined a set of stress-related textual, 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.
How to detect Stress based on Social Interactions
- Finding stress based on social media is achieved by following steps
- Extract twitter live stream for different users
- Use AFINN kind of dictionary words for identifying scores of stress based words
- Compare the different user scores using graph