Incorporating Forthcoming Events and Personality Traits in Social Media Based Stress Prediction

Abstract

Predicting the forthcoming stress is critical for stress management. In this article, we consider not only one’s posts on social media, but also learn to understand the influence of stressor/uplift events and individual's reactions to the events by constructing an event-post correlation memory network, which evolves dynamically along with the change of events impact and one’s response reflected from their posts. We further build a joint memory network for modeling the dynamics of one’s emotions incurred by stressor/uplift events, and learn one's personality traits based on linguistic words and a fuzzy neural network. We finally predict one's future stress level based on a fully-connected network with attention, where personality traits, social activeness features, and forthcoming possible events are incorporated. We construct a dataset consisting of 1138 strongly-stressed and 985 weakly-stressed users on microblog. Experimental results show that: (1) our method outperformed the baseline, delivering 81.03 percent of prediction accuracy; (2) integrating the personality traits helped increase the prediction accuracy by 3.97 percent; (3) considering forthcoming events enabled to improve the prediction accuracy by 5.81 percent; (4) strongly-stressed users tended to be more neurotic and less active on social media, complying with psychological studies; (5) data scarcity had negative influence on stress prediction and (6) the dataset that is biased towards female made the model have a better prediction accuracy on female users.