Stress Identification in Online Social Networks

Online social networks have become one of the primary ways of communication to individuals. It rapidly gen-erates a large volume of textual and non-textual data such as images, audio, and videos. In particular, textual data plays a vital role in detecting mental health-related problems such as stress, depression, anxiety, and emotional and behavioral disorders. In this paper, we identify the mental stress of online users in social networks using a transformers-based RoBERTa model and an autoregressive model, also called XLNet. We implement this model in both a constrained system and an unconstrained system. The constrained system maintains the gold standard datasets such as training, validation, and testing. On the other hand, the unconstrained system divides the given dataset into user-specific training, validation, and test sets. Our results indicate that the proposed transformers-based RoBERTa model achieves a better result in both constrained and unconstrained systems than the state-of-the-art models.