Panhale Chetan Bharathrao, K Srilakshmi


Psychological stress threatens people's health. It is not easy to spot stress in a timely and proactive manner. With popular social networking, people are accustomed to sharing their daily activities and interacting with friends on social media platforms, making it possible to take advantage of social network data over the Internet to detect stress. In this paper, users' stress status is closely related to their friends in social networks, and we use a broad set of data from real social platforms to conduct a systematic study of the relationship of stress. And social interactions of users. First, we define a set of textual, visual and social characteristics associated with stress from multiple aspects, and then propose a new hybrid model: a global graphical model shared with the neural network to leverage the use of Twitter content and social interaction information to detect stress. Experimental results show that the proposed model can improve detection performance by 5-10% in F1. In further analysis of social interaction data, we also found many interesting phenomena, i.e., the number of dispersed social connections (i.e., without Delta connections) of users is approximately 13% higher than non-fanatical users, indicating that the social structure of stressful friends tends to be less connected and less complex than non-authorized users.


AndreyBogomolov, Bruno Lepri, MichelaFerron, Fabio Pianesi,and Alex Pentland.Daily stress recognition from mobile phonedata, weather conditions and individual traits. In ACM InternationalConference on Multimedia, pages 477–486, 2014.

Chris Buckley and EllenMVoorhees.Retrieval evaluation with incompleteinformation. In Proceedings of the 27th annual internationalACM SIGIR conference on Research and development in informationretrieval, pages 25–32, 2004.

Xiaojun Chang, Yi Yang, Alexander G Hauptmann, Eric P Xing,and Yao-Liang Yu.Semantic concept discovery for large-scalezero-shot event detection.InProceedings of International JointConference on Artificial Intelligence, pages 2234–2240, 2015.

WanxiangChe, Zhenghua Li, and Ting Liu.Ltp: A chineselanguage technology platform. In Proceedings of International Conferenceon Computational Linguistics, pages 13–16, 2010.

Chihchung Chang and Chih-Jen Lin. Libsvm: a library for supportvector machines. ACM TRANSACTIONS ON INTELLIGENTSYSTEMS AND TECHNOLOGY, 2(3):389–396, 2001.

Dan C Ciresan, Ueli Meier, Jonathan Masci, Luca Maria Gambardella,and J ¨ urgenSchmidhuber. Flexible, high performanceconvolutional neural networks for image classification.InProceedingsof International Joint Conference on Artificial Intelligence, pages1237–1242, 2011.

Sheldon Cohen and Thomas A. W. Stress, social support, and thebufferinghypothesis.Psychological Bulletin, 98(2):310–357, 1985.

Glen Coppersmith, Craig Harman, and Mark Dredze.Measuringpost traumatic stress disorder in twitter. In Proceedings of theInternational Conference on Weblogs and Social Media, pages 579–582,2014.

Rui Fan, Jichang Zhao, Yan Chen, and KeXu. Anger is moreinfluential than joy: Sentiment correlation in weibo. PLoS ONE,2014.

Zhanpeng Fang, Xinyu Zhou, Jie Tang, Wei Shao, A.C.M. Fong,Longjun Sun, Ying Ding, Ling Zhou, , and JarderLuo. Modelingpaying behavior in game social networks.InIn Proceedings of theTwenty-Third Conference on Information and Knowledge Management(CIKM’14), pages 411–420, 2014.

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