Detecting Users Stress Based on Social Interactions in Social Networks

Authors

  • Poornima Doijad  Department of Computer Science and Engineering, D. Y. Patil Technical Campus, Talsande, Kolhapur, Maharashtra, India

DOI:

https://doi.org//10.32628/CSEIT2283126

Keywords:

Emotion Reorganization, Stress detection, social media, sentiment analysis, neural network.

Abstract

Stress is your body's way of reaction to any kind of requisition or damage when it psychological stress it causes damage to people full body. It is necessary to detect stress on time for obstructive care. There are many Social Media are available where people are very much interested to share their routine activities. These activities grasp the online information, and that will be used for stress diagnosis. The states of stress related to friend on social media, we will study stress states and interactions by using large scale data set by correlating it. We proposed a model to from various points of views textual, visual and social to form a graph model for finding information to stress detection. By examining the people social interactions, find the stressed user, and the stressed user is less active on social media.

References

  1. Andrey Bogomolov, Bruno Lepri, Michela Ferron, Fabio Pianesi and Alex Pentland, Daily stress recognition from mobile phonedata, weather conditions and individual traits. In ACM International Conference on Multimedia, pages 477–486, 2014.
  2. Hong-Han Shuai, Chih-YaShen, De-Nian Yang, Senior Member, IEEE, Yi-FengLan, Wang-Chien Lee, Philip S. Yu, Fellow, IEEE and Ming-Syan Chen, Fellow, IEEEA Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining. In 1041-4347 (c) 2017 IEEE.
  3. Rui Fan, Jichang Zhao, Yan Chen, and KeXu, Anger is more influential than joy: Sentiment correlation in weibo. Plos ONE,2014.
  4. Eileen Fischer and Rebecca Reuber. Social interaction via new social media: (how) can interactions on twitter affect effectual thinking and behavior? Journal of Business Venturing, 26(1):1–18,2011.
  5. Sung Ju Hwang, Discriminative object categorization with external semantic knowledge. 2013.
  6. Epandar D. Kamvar. We feel fine and searching the emotional web. In In Proceedings of WSDM, pages 117–126, 2011.
  7. H. Lin, J. Jia, Q. Guo, Y. Xue, J. Huang, L. Cai, and L. Feng. Psychological stress detection from cross-media microblog data usingdeep sparse neural network. In proceedings of IEEE International Conference on Multimedia & Expo, 2014
  8. Cristian, Lillian Lee, Bo Pang, and Jon Kleinberg. Echoes of power: Language effects and power differences in social interaction. EprintarXiv: 1112.3670, 2011.
  9. Xin Chen, Student Member, IEEE, MihaelaVorvoreanu, and Krishna Madhavan, Mining Social Media Data for Understanding Students’ Learning Experiences, IEEE Transactions On Learning Technologies, Vol. 7, No. 3, July-September 2014
  10. Xie Peter Andreae Mengjie Zhang Paul Warren," Detecting Stress in Spoken English using Decision Trees and Support Vector Machines".
  11. Dr. G V Garje, " Stress detection and sentiment prediction: A Survey", International Journal of Engineering Applied Sciences and Technology, Vol. 1, Issue 2, ISSN 2455-2143, Pages 32-34.
  12. Strength: Stress and relaxation magnitude detection for social media texts.
  13. Enrique Garcia-Ceja, Venet Osmani and Oscar Mayora," Automatic Stress Detection in Working Environments from Smartphones’ Accelerometer Data: A First Step".
  14. Ravneet Kaur, Sarbjeet Singh," A survey of data mining and social network analysis based anomaly detection techniques".
  15. Ayten Ozge Akmandor, Niraj K. Jha," Keep the Stress Away with SODA: Stress Detection and Alleviation System", 2016 IEEE.
  16. Huijie Lin, JiaJia, QuanGuo, Yuanyuan Xue1, Jie Huang1, LianhongCai, Ling Feng, " Psychological Stress Detection From Cross-media Microblog Data Using Deep Sparse Neural Network".
  17. Sa0las, A., Georgakis, P., Nwagboso, C., Ammari, A. and Petalas," Traffic Event Detection Framework Using Social Media", 2017 IEEE International Conference on Smart Grid and Smart Cities.
  18. Santosh Kumar BhartiKorraSathyaBabu Sanjay Kumar Jena," Parsing-based Sarcasm Sentiment Recognition in Twitter Data", 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2022-06-30

Issue

Volume 8, Issue 3, May-June-2022

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Poornima Doijad, " Detecting Users Stress Based on Social Interactions in Social Networks, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 3, pp.150-155, May-June-2022. Available at doi : https://doi.org/10.32628/CSEIT2283126