Efficiently Combining Tweet Content and Social Interactions to Enhance Stress Recognition Performance

Abstract

Mental stress is becoming a threat to people’s health today. With the rapid pace of life, more and more people are feeling stressed. Increase of stress has become a great challenge to human health and life quality. Thus, there is significant importance to detect stress before it turns into severe problems. This study defines 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.