Stress Detection from Social Media Articles: New Dataset Benchmark and Analytical Study

Abstract

Stress detection is a basic and essential task for examining the mental health of a given population. With the rapid digitalization leading to text-based forms of communication gaining dominance over spoken ones, there is now the chance to develop analytical studies for stress detection directly from textual inputs in social media. However, only a limited number of benchmarks are publicly available. To this end, we create four high quality datasets based on Twitter and Reddit, which are designed particularly for the task of stress detection from social media texts. The main contributions are three-folds: 1) for each dataset, we provide a detailed description on our dataset construction process, including data collection, data preprocessing and annotation; 2) we perform a comparative study on the performance of different rule-based and machine learning-based approaches on the proposed datasets as the new benchmark of this field; 3) we study the feasibility and reliability of existing systems. Extensive experiments show that Transformer-based models outperform lexical-based and embedding-based methods. Also, we observe that existing methods based on sentiment polarity detection cannot be directly adapted to the task of stress detection and there remains space for further improvements. We hope this study could pave the way for future studies in the field of computational stress detection. © 2022 IEEE.