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Deep Learning Based Model for Stress Measurement in Online Social Networks

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Computational Data and Social Networks (CSoNet 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14479))

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Abstract

Online Social Networks (OSNs) have become ubiquitous platforms for individuals to express their thoughts and emotions, making them valuable sources for studying mental health. This paper presents a novel Deep Learning-based approach for stress measurement in OSNs. We leverage a comprehensive dataset collected from Kaggle, specifically curated for stress analysis in social media. The proposed model demonstrates remarkable accuracy in identifying stress levels, paving the way for proactive mental health interventions and more targeted support systems in the digital age. This research contributes to the growing body of knowledge addressing mental health challenges in the online world, emphasizing the potential of AI and deep learning techniques in this critical domain.

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References

  1. Al Sobbahi, R., Tekli, J.: Comparing deep learning models for low-light natural scene image enhancement and their impact on object detection and classification: overview, empirical evaluation, and challenges. Signal Process. Image Commun. 116848 (2022)

    Google Scholar 

  2. Chakar, J., Sobbahi, R.A., Tekli, J.: Depthwise separable convolutions and variational dropout within the context of YOLOv3. In: Bebis, G., et al. (eds.) ISVC 2020, pp. 107–120. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-64556-4_9

  3. Forouzandeh, S., Sheikhahmadi, A., Aghdam, A.R., Xu, S.: New centrality measure for nodes based on user social status and behavior on Facebook. Int. J. Web Inf. Syst. 14, 158–176 (2018). https://doi.org/10.1108/ijwis-07-2017-0053

    Article  Google Scholar 

  4. Gao, W., Gao, B.Y.W., Yang, Y., Wang, Y.: Depression detection in social media using XLNet with topic distributions. J. Comput. 33, 095–106 (2022). https://doi.org/10.53106/199115992022083304008

    Article  Google Scholar 

  5. Hasib, K.M., Towhid, N.A., Islam, M.R.: HSDLM: a hybrid sampling with deep learning method for imbalanced data classification. Int. J. Cloud Appl. Comput. (IJCAC) 11(4), 1–13 (2021)

    Google Scholar 

  6. Hidayatullah, M.R., Maharani, N.W.: Depression detection on twitter social media using decision tree. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi) 6, 677–683 (2022). https://doi.org/10.29207/resti.v6i4.4275

  7. Illahi, M., Siddiqui, I.F., Ali, Q., Alvi, F.A.: Ensemble machine learning approach for stress detection in social media texts. Quaid-E-Awam Univ. Res. J. Eng. Sci. Technol. Nawabshah 20(02), 123–128 (2022)

    Google Scholar 

  8. Jain, A.K., Gupta, B.: PHISH-SAFE: URL features-based phishing detection system using machine learning. In: Bokhari, M., Agrawal, N., Saini, D. (eds.) Cyber Security: Proceedings of CSI 2015, pp. 467–474. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-8536-9_44

  9. Keles, B., McCrae, N., Grealish, A.: A systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents. Int. J. Adolesc. Youth 25(1), 79–93 (2020)

    Article  Google Scholar 

  10. Lakhwani, K., et al.: Adaptive and convex optimization-inspired workflow scheduling for cloud environment. Int. J. Cloud Appl. Comput. (IJCAC) 13(1), 1–25 (2023)

    Google Scholar 

  11. Lin, H., et al.: User-level psychological stress detection from social media using deep neural network. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 507–516 (2014)

    Google Scholar 

  12. Nijhawan, T., Attigeri, G., Ananthakrishna, T.: Stress detection using natural language processing and machine learning over social interactions. J. Big Data 9(1) (2022)

    Google Scholar 

  13. Ostic, D., et al.: Effects of social media use on psychological well-being: a mediated model. Front. Psychol. 12, 678766 (2021)

    Google Scholar 

  14. Peñalvo, F.J.G., et al.: Mobile cloud computing and sustainable development: opportunities, challenges, and future directions. Int. J. Cloud Appl. Comput. (IJCAC) 12(1), 1–20 (2022)

    Google Scholar 

  15. Perry, J., Devore, S.K., Pellegrino, C., Salce, A.J.: Social media usage and its effects on the psychological health of adolescents. NASN School Nurse 1942602X231159901 (2023)

    Google Scholar 

  16. Ramadan, Z.B., Farah, M.F.: Influencing the influencers: the case of retailers’ social shopping platforms. Int. J. Web Based Communities 16(3), 279–295 (2020)

    Article  Google Scholar 

  17. Ren, P., et al.: A survey of deep active learning. ACM Comput. Surv. (CSUR) 54(9), 1–40 (2021)

    Article  Google Scholar 

  18. Sayour, M.H., Kozhaya, S.E., Saab, S.S.: Autonomous robotic manipulation: real-time, deep-learning approach for grasping of unknown objects. J. Robot. 2022 (2022)

    Google Scholar 

  19. Selvadass, S., Bruntha, P.M., Priyadharsini, K.: Stress analysis in social media using ml algorithms. In: 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 1502–1506. IEEE (2022)

    Google Scholar 

  20. Shankar, K., Perumal, E., Elhoseny, M., Taher, F., Gupta, B., El-Latif, A.A.A.: Synergic deep learning for smart health diagnosis of covid-19 for connected living and smart cities. ACM Trans. Internet Technol. (TOIT) 22(3), 1–14 (2021)

    Article  Google Scholar 

  21. Singh, A., Gupta, B.B.: Distributed denial-of-service (DDoS) attacks and defense mechanisms in various web-enabled computing platforms: issues, challenges, and future research directions. Int. J. Semant. Web Inf. Syst. (IJSWIS) 18(1), 1–43 (2022)

    Article  Google Scholar 

  22. Tembhurne, J.V., Almin, M.M., Diwan, T.: Mc-DNN: fake news detection using multi-channel deep neural networks. Int. J. Semant. Web Inf. Syst. (IJSWIS) 18(1), 1–20 (2022)

    Article  Google Scholar 

  23. Turcan, E., McKeown, K.: Dreaddit: a reddit dataset for stress analysis in social media. arXiv preprint arXiv:1911.00133 (2019)

  24. Yas, H., et al.: The negative role of social media during the covid-19 outbreak. Int. J. Sustain. Dev. Plan. 16, 219–228 (2021). https://doi.org/10.18280/ijsdp.160202

  25. Zhang, Z., Sun, R., Zhao, C., Wang, J., Chang, C.K., Gupta, B.B.: Cyvod: a novel trinity multimedia social network scheme. Multimedia Tools Appl. 76, 18513–18529 (2017)

    Article  Google Scholar 

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Acknowledgement

This research work is supported by National Science and Technology Council (NSTC), Taiwan Grant No. NSTC112-2221-E-468-008-MY3.

Author information

Authors and Affiliations

  1. Ronin Institute, Montclair, NJ, 07043, USA

    Akshat Gaurav

  2. Department of Computer Science and Information Engineering, Asia University, Taichung City, Taiwan

    Brij B. Gupta

  3. Department of Electrical and Computer Engineering, Lebanese American University, Beirut, Lebanon

    Brij B. Gupta

  4. School of Science and Technology, Hong Kong Metropolitan University, Kowloon, Hong Kong

    Kwok Tai Chui

  5. International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taichung City, Taiwan

    Varsha Arya

Authors
  1. Akshat Gaurav
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  2. Brij B. Gupta
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  3. Kwok Tai Chui
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  4. Varsha Arya
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Corresponding author

Correspondence to Brij B. Gupta .

Editor information

Editors and Affiliations

  1. National Economics University, Hanoi, Vietnam

    Minh Hoàng Hà

  2. Florida Atlantic University, Boca Raton, FL, USA

    Xingquan Zhu

  3. University of Florida, Gainesville, FL, USA

    My T. Thai

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Gaurav, A., Gupta, B.B., Chui, K.T., Arya, V. (2024). Deep Learning Based Model for Stress Measurement in Online Social Networks. In: Hà, M.H., Zhu, X., Thai, M.T. (eds) Computational Data and Social Networks. CSoNet 2023. Lecture Notes in Computer Science, vol 14479. Springer, Singapore. https://doi.org/10.1007/978-981-97-0669-3_36

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  • DOI: https://doi.org/10.1007/978-981-97-0669-3_36

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0668-6

  • Online ISBN: 978-981-97-0669-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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