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A systematic exploration of the micro-blog feature space for teens stress detection.

Zhao L, Li Q, Xue Y, Jia J, Feng L - Health Inf Sci Syst (2016)

Bottom Line: 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.Involving comments and responses under the tweet supplement the detection and improves the detection accuracy of 16.8 %.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Technology, Institute of Data Science, Centre for Computational Mental Healthcare Research, Tsinghua University, 100084 Beijing, China.

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 %.

No MeSH data available.


Related in: MedlinePlus

Performance of stress level detection
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Fig4: Performance of stress level detection

Mentions: For the tweets which are annotated as stressful by the users and also have nonzero detected stress levels, we compare their stress level differences using detected level lower than, equal with, and higher than what the user annotated. Figure 4 shows that for the four stress categories, tweets with accurately detected stress levels occupy more than 50 % of the whole stressful tweets, and around 10 % tweets are detected with unexpected lower stress levels. It illustrates that our detection method based on the comprehensive feature space will not miss most of the heavily stressful tweets.Fig. 4


A systematic exploration of the micro-blog feature space for teens stress detection.

Zhao L, Li Q, Xue Y, Jia J, Feng L - Health Inf Sci Syst (2016)

Performance of stress level detection
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4837598&req=5

Fig4: Performance of stress level detection
Mentions: For the tweets which are annotated as stressful by the users and also have nonzero detected stress levels, we compare their stress level differences using detected level lower than, equal with, and higher than what the user annotated. Figure 4 shows that for the four stress categories, tweets with accurately detected stress levels occupy more than 50 % of the whole stressful tweets, and around 10 % tweets are detected with unexpected lower stress levels. It illustrates that our detection method based on the comprehensive feature space will not miss most of the heavily stressful tweets.Fig. 4

Bottom Line: 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.Involving comments and responses under the tweet supplement the detection and improves the detection accuracy of 16.8 %.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Technology, Institute of Data Science, Centre for Computational Mental Healthcare Research, Tsinghua University, 100084 Beijing, China.

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 %.

No MeSH data available.


Related in: MedlinePlus