A systematic exploration of the micro-blog feature space for teens stress detection. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Zhao, Liang; Li, Qi; Xue, Yuanyuan; Jia, Jia; Feng, Ling
• Is Part Of: Health information science and systems. Volume 4:Issue 1(2016)
• Page Start: 1
• Page End: 12
• Publication Date: 2016-12
• Subjects: Micro-blog -- Feature space -- Teenager -- Stress detection; Medical informatics -- Periodicals; Medicine -- Data processing -- Periodicals; Medical Informatics -- Periodicals; Medical informatics; Medicine -- Data processing; Periodicals; Electronic journals; 610.28
• DOI: 10.1186/s13755-016-0016-3
• Languages: English
• ISSNs: 2047-2501

Abstract

Abstract Background In the modern stressful society, growing teenagers experience severe stress from different aspects from school to friends, from self-cognition to inter-personal relationship, which negatively influences their smooth and healthy development. Being timely and accurately aware of teenagers psychological stress and providing effective measures to help immature teenagers to cope with stress are highly valuable to both teenagers and human society. Previous work demonstrates the feasibility to sense teenagers' stress from their tweeting contents and context on the open social media platform—micro-blog. However, a tweet is still too short for teens to express their stressful status in a comprehensive way. Methods Considering the topic continuity from the tweeting content to the follow-up comments and responses between the teenager and his/her friends, we combine the content of comments and responses under the tweet to supplement the tweet content. Also, such friends' caring comments like "what happened?", "Don't worry!", "Cheer up!", etc. provide hints to teenager's stressful status. Hence, in this paper, we propose to systematically explore the micro-blog feature space, comprised of four kinds of features [tweeting content features (FW), posting features (FP), interaction features (FI), and comment-response features (FC) between teenagers and friends] for teenager' stress category and stress level detection. We extract and analyze these feature values and their impacts on teens stress detection. Results We evaluate the framework through a real user study of 36 high school students aged 17. Different classifiers are employed to detect potential stress categories and corresponding stress levels. Experimental results show that all the features in the feature space positively affect stress detection, and linguistic negative emotion, proportion of negative sentences, friends' caring comments and teen's reply rate play more significant roles than the rest features. Conclusions Micro-blog platform provides easy and effective channel to detect teenagers' psychological stress. Involving comments and responses under the tweet supplement the detection and improves the detection accuracy of 16.8 %.