Psychological stress is a biggest threat to human’s health. Hence, it is vital to detect and manage stress before it turns into severe problem. However, conventional stress detection strategies rely on psychological scales and physiological devices, which require active individual participation making it labor-consuming and expensive. With the rapid evolution of social media networks, people are willing to sharetheir everyday events and moods via social media platforms, making it practicable to leverage this online social media content for stress detection as these data timely reflect user’s real-life emotional state. To automatically predict stress, we have defined a set of stress-related textual ‘F = {f1, f2, f3, f4}’, visual ‘vF = {vf1, vf2}’, and social ‘sf’ features, and thenproposed a hybrid model Psychological Stress Detection (PSD) - a Probabilistic Naïve Bayes Classifier combined with Visual (Hue, Saturation, Value) and Social modules,to leverage text, image and social interaction information for stress detection from social media contentExperimental results show that the proposed PSD model improves the detection performance,when compared to TensiStrength and Teenchat frameworkPSD achieves 95% of Precision rate. PSD model would be useful in developing stress detection tools for mental health agencies and individuals.

Psychological Stress Detection; Social Media interaction; Health agencies; Physiological Signals

[1] J. Herbert, “Fortnightly review: Stress, the brain, and mental illness”, British Medical J., pp. 530–535, Vol. 315, No. 7107, 1997.
[2] Liew, and Jasy Suet Yan, “fine-grained emotion detection in microblog text”, Dissertations – ALL. pp 440, 2016.
[3] F.-T. Sun, C. Kuo, H.-T. Cheng, S. Buthpitiya, P. Collins, and M. L. Griss, “Activity-Aware Mental Stress Detection Using Physiological Sensors”, In Proc. Of Intl. Conf. on Mobile Computing, Application, and Services (MobiCASE), Santa Clara, CA, 2010.
[4] Chaffey, D. (2016). “Global social media research summary 2016.” Retrived in June 15th 2016 from http://www.smartinsights.com/social-mediamarketing/social-media-strategy/new-global-social-mediaresearch.
[5] H. Kurniawan, A.V. Maslov, and M. Pechenizkiy, “Stress detection from speech and galvanic skin response signals”, in: Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, pp. 209–214, 2013.
[6] V.P. Patil, K.K. Nayak, and M. Saxena, “Voice stress detection”, Int. J. Electr. Electron. Comput. Eng., pp. 148–154, 2013.
[7] S. Greene, H. Thapliyal, and A. C. Holt, “A survey of affective computing for stress detection: Evaluating technologies in stress detection for better health”, IEEE Consum. Electron. Mag., Vol. 5, No. 4, pp. 44–56, 2016.
[8] K. Lee, A. Agrawal, and A. Choudhary, “Real-time disease surveillance using twitter data: Demonstration on FLU and cancer”, in Proc. ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, pp. 1474–1477, 2013.
[9] Sztajzel, “Heart rate variability: a noninvasive electrocardiographic method to measure the autonomic nervous system”, Swiss Med. Weekly, pp. 514–522, 2004.
[10] L. Nie, Y.-L. Zhao, M. Akbari, J. Shen, and T.-S. Chua, “Bridging the vocabulary gap between health seekers and healthcare knowledge”, IEEE Trans. Knowl. Data Eng., vol. 27, no. 2, pp. 396–409, Feb. 2015.
[11] G. Coppersmith, C. Harman, and M. Dredze, “Measuring post traumatic stress disorder in twitter”, in Proc. Int. Conf. Weblogs Soc. Media, 2014, pp. 579–582.
[12] R. Fan, J. Zhao, Y. Chen, and K. Xu, “Anger is more influential than joy: Sentiment correlation in weibo”, PLoS One, vol. 9, 2014, Art. no. e110184.
[13] Pincus T, Swearingen C, Wolfe F “Toward a multidimensional Health Assessment Questionnaire (MDHAQ): assessment of advanced activities of daily living and psychological status in the patient-friendly health assessment questionnaire format”, Arthritis Rheum 1999; 42: 2220-30
[14] W. Chen, Y. Q. Shi, and G. Xuan, “Identifying computer grahics using HSV color model and statistical moments of characteristic functions”, In Proceedings of IEEE International Conference on Multimedia and Expo, Jul. 2007, pp. 1123–1126.
[15] Y. Zhang, J. Tang, J. Sun, Y. Chen, and J. Rao, “Moodcast: Emotion prediction via dynamic continuous factor graph model”, Proc. IEEE 13th Int. Conf. Data Mining, 2010, pp. 1193–1198.
[16] A. Sano, and R. W. Picard, “Stress recognition using wearable sensors and mobile phones”, In Proceedings of ACII, pp. 671–676, 2013.
[17] G. Farnadi, et al., “Computational personality recognition in social media”, UserModel User-Adapted Interaction, vol. 26, pp. 109–142, 2016.
[18] J. Golbeck, C. Robles, M. Edmondson, and K. Turner, “Predicting personality from Twitter”, in Proc. IEEE 3rd Int. Conf. Privacy, Security, Risk Trust, IEEE 3rd Int. Conf. Soc. Comput., 2011, pp. 149–156.
[19] A. Fernandes, R. Helawar, R. Lokesh, T. Tari, and A. V. Shahapurkar, “Determination of stress using blood pressure and galvanic skin response”, In Proceedings of the International Conference on Communication and Network Technologies (ICCNT’14), pp. 165–168, Sivakasi, India, 2014.
[20] B. Verhoeven, W. Daelemans, and B. Plank, “Twisty: A multilingual twitter stylometry corpus for gender and personality profiling”, in Proc. 10th Int. Conf. Language Resources Eval., PP. 1632–1637 2016.
[21] F. A. Pozzi, D. Maccagnola, E. Fersini, and E. Messina, “Enhance user-level sentiment analysis on microblogs with approval relations”, in Proc. 13th Int. Conf. AI* IA: Advances Artif. Intell., PP. 133–144, 2013.
[22] J. Huang, Q. Li, Y. Xue, T. Cheng, S. Xu, J. Jia, and L. Feng.: “Teenchat: a chatterbot system for sensing and releasing adolescents’ stress”, In: X. Yin, K. Ho, D. Zeng, U. Aickelin, R. Zhou, H. Wang. (eds.) HIS LNCS, Vol. 9085, pp. 133–145. Springer, Heidelberg, 2015.
[23] C. Tan, L. Lee, J. Tang, L. Jiang, M. Zhou, and P. Li, “User-level sentiment analysis incorporating social networks,” in Proc. SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2011, pp. 1397–1405.
[24] J.W. Pennebaker, R.J. Booth, and M.E. Francis, “Linguistic Inquiry and Word Count (LIWC)”, 2007.
[25] Shaikha Hajera and Mohammed Mahmood Ali, “A Comparative Analysis of Psychological Stress Detection Methods”, In IJCEM, Vol. 21 Issue 2, March 2018.
[26] Y.R. Tausczik, and J. W. Pennebaker, “The psychological meaning of words: LIWC and computerized text analysis methods”, Journal of Language and Social Psychology, pp. 24-54, 2010.
[27] Thelwall M. “TensiStrength: stress and relaxation magnitude detection for social media texts”, J Inf Process Managpp: 106–121, 2017.
[28] X. Wang, J. Jia, H. Liao, and L. Cai, “Affective image colorization”, Journal of Computer Science andTechnology, Vol. 27, No. 6, pp. 1119-1128, 2012.
[29] S. R. Ireland, Y. M. Warren, and L. G. Herringer, “Anxiety and color saturation preference”, Perceptualand Motor Skills, Vol. 75, pp. 545-546, 1992.
[30] S.-B. Kim, K.-S. Han, H.-C. Rim, and S. H. Myaeng. Some effective techniques for naive bayes text classification. IEEE Transactions on Knowledge and Data Engineering, 18(11):1457–1466, Nov. 2006.
[31] Z. Pawlak, “Rough sets, decision algorithms and Bayes’s theorem,” Eur. J. Oper. Res., Vol. 136, , Issue.1, pp. 181–189, 2002.
[32] C.P.Patidar, Meena Sharma, VarshaSharda, "Detection of Cross Browser Inconsistency by Comparing Extracted Attributes", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.1, pp.1-6, 2017.
[33] MM Ali, MAU Rahman, S Hajera “A comparative study of various image dehazing techniques” International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), IEEE, pp. 3622-3628, 2017
[34] G. Sharma, A. Kumar, "REVIN: Reduced Energy Virtuous Immune Network for WSN", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.1-8, 2017.
[35] MM Ali, M Tajuddin, M Kabeer “SDF: psychological Stress Detection Framework from Microblogs using Pre-defined rules and Ontologies”, International Journal of Intelligent Systems and Applications in Engineering, Vol.6, Issue.2, pp. 158-164, 2018.