Open Access

ARTICLE

Teensensor: Gaussian Processes for Micro-Blog Based Teen’S Acute and Chronic Stress Detection

Yuanyuan Xue^1,2, Qi Li¹, LingFeng¹

1 Dept. of Computer Science and Technology, Centre for Computational Mental Healthcare Research Institute of Data Science, Tsinghua University, Beijing, China
2 China Transport Telecommunications & Information Center, Beijing, China
E-mail: xue-yy12@tsinghua.org.cn, liqi13@mails.tsinghua.edu.cn, fengling@mail.tsinghua.edu.cn

Computer Systems Science and Engineering 2019, 34(3), 151-164. https://doi.org/10.32604/csse.2019.34.151

Abstract

Stress is a common problem all over the world. More and more teenagers today have to cope with different stressor events coming from school, family, peer relation, self-cognition, romantic relation, etc. Over-stress without proper guidance will lead to a series of potential problems including physical and mental disorders, and even suicide due to the shortage of teen’ s psychological endurance and controllability. Therefore, it is necessary and important to timely sense adolescents’ stress and help them release the stress properly. In this paper, we present a micro-blog based system called TeenSensor, aiming to detect teens acute and chronic stress from their tweeting contents (including linguistic text, song, picture, emoticon, punctuation) and tweeting context (time, and frequency) on micro-blog. We conduct a user study where 69 16/17-year-old high-school students got involved. The result shows that sensing teenagers acute and chronic stress is feasible through the open micro-blog, achieving 69.6% accuracy for acute and 72.2% for chronic, respectively. TeenSensor performs better in detecting the stress of introvert teens than extrovert teens. Among the four typical types of teens stress, self-cognition stress is revealed the least from micro-blog, compared to the study, inter-personal, and affection stress types.

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Citations