Limits...
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

A real tweet example
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: A real tweet example

Mentions: Recent studies demonstrate the feasibility of tweet-leveled stress and depression detection, since depressed and stressful individuals look micro-blog as a channel for emotional release and interaction [1, 2]. The most closely related work of this paper is [2–7], which designed and implemented a micro-blog platform for sensing and helping ease teens’ stress. A number of tweeting content and tweeting context features were explored for tweet-level teenagers’ stress detection [6]. Detected user’s psychological stress from cross-media micro-blog via a deep convolution network on sequential tweeting time series in a certain time period. Considering a tweet is limited to 140 characters, which may not be long enough for teens to express their stress categories and stress levels, [7] incorporated social interactions between teens and their friends under each tweet in stress detection. Due to the topic continuity from the tweeting content to the follow-up comments and responses between the teen author and his/her friends, [7] combined the contents of comments and responses under the tweet to supplement the tweeting content. Take a real tweet for example. From the tweeting content “I still feel sad.” in Fig. 1, it is hard to recognize the stress category. However, from the comments and responses under the tweet, sentence “I broke up with my boyfriend.” reveals this teen has an affection stress.Fig. 1


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)

A real tweet example
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: A real tweet example
Mentions: Recent studies demonstrate the feasibility of tweet-leveled stress and depression detection, since depressed and stressful individuals look micro-blog as a channel for emotional release and interaction [1, 2]. The most closely related work of this paper is [2–7], which designed and implemented a micro-blog platform for sensing and helping ease teens’ stress. A number of tweeting content and tweeting context features were explored for tweet-level teenagers’ stress detection [6]. Detected user’s psychological stress from cross-media micro-blog via a deep convolution network on sequential tweeting time series in a certain time period. Considering a tweet is limited to 140 characters, which may not be long enough for teens to express their stress categories and stress levels, [7] incorporated social interactions between teens and their friends under each tweet in stress detection. Due to the topic continuity from the tweeting content to the follow-up comments and responses between the teen author and his/her friends, [7] combined the contents of comments and responses under the tweet to supplement the tweeting content. Take a real tweet for example. From the tweeting content “I still feel sad.” in Fig. 1, it is hard to recognize the stress category. However, from the comments and responses under the tweet, sentence “I broke up with my boyfriend.” reveals this teen has an affection stress.Fig. 1

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