A Meta-learning based Stress Category Detection Framework on Social Media

Psychological stress has become a wider-spread and serious health issue in modern society. Detecting stressors that cause the stress could enable people to take effective actions to manage the stress. Previous work relied on the stressor dictionary built upon words from the stressor-related categories in the LIWC (Linguistic Inquiry and Word Count), and focused on stress categories that appear frequently on social media. In this paper, we build a meta-learning based stress category detection framework, which can learn how to distinguish a new stress category with very little data through learning on frequently appeared categories without relying on any lexicon. It is comprised of three modules, i.e., encoder module, induction module, and relation module. The encoder module focuses on learning category-relevant representation of each tweet with Dependency Graph Convolutional Network and tweet attention. The induction module deploys Mixture of Experts mechanism to integrate and summarize a representation for each category. The relation module is adopted to measure the correlation between each pair of query tweets and categories. Through the three modules and the meta-training process, we can then obtain a model which learns to learn how to identify stress categories and can directly be employed to a new category with little labelled data. Our experimental results show that the proposed framework can achieve 75.3 accuracy with 3 labeled data for the rarely appeared stress categories. We also build a stress category dataset consisting of 12 stress categories with 1,553 manually labeled stressful microblogs which can help train AI models to assist psychological stress diagnosis.