Deep Learning Based Model for Stress Measurement in Online Social Networks

Akshat Gaurav; Brij B. Gupta; Kwok Tai Chui; Varsha Arya

doi:10.1007/978-981-97-0669-3_36

Deep Learning Based Model for Stress Measurement in Online Social Networks

Akshat Gaurav, Brij B. Gupta, Kwok Tai Chui, Varsha Arya

Electronic Engineering and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Computational Data and Social Networks - 12th International Conference, CSoNet 2023, Proceedings
Editors	Minh Hoàng Hà, Xingquan Zhu, My T. Thai
Pages	398-406
Number of pages	9
DOIs	https://doi.org/10.1007/978-981-97-0669-3_36
Publication status	Published - 2024
Event	12th International Conference on Computational Data and Social Networks, CSoNet 2023 - Hanoi, Viet Nam Duration: 11 Dec 2023 → 13 Dec 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14479 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Computational Data and Social Networks, CSoNet 2023
Country/Territory	Viet Nam
City	Hanoi
Period	11/12/23 → 13/12/23

Keywords

Deep Learning
Mental Health
Online Social Networks
Reddit
Stress Detection

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-981-97-0669-3_36

Cite this

Gaurav, A., Gupta, B. B., Chui, K. T., & Arya, V. (2024). Deep Learning Based Model for Stress Measurement in Online Social Networks. In M. H. Hà, X. Zhu, & M. T. Thai (Eds.), Computational Data and Social Networks - 12th International Conference, CSoNet 2023, Proceedings (pp. 398-406). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14479 LNCS). https://doi.org/10.1007/978-981-97-0669-3_36

Gaurav, Akshat ; Gupta, Brij B. ; Chui, Kwok Tai et al. / Deep Learning Based Model for Stress Measurement in Online Social Networks. Computational Data and Social Networks - 12th International Conference, CSoNet 2023, Proceedings. editor / Minh Hoàng Hà ; Xingquan Zhu ; My T. Thai. 2024. pp. 398-406 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{c47b88c0e4094ec5823a25bed4c0034b,

title = "Deep Learning Based Model for Stress Measurement in Online Social Networks",

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.",

keywords = "Deep Learning, Mental Health, Online Social Networks, Reddit, Stress Detection",

author = "Akshat Gaurav and Gupta, {Brij B.} and Chui, {Kwok Tai} and Varsha Arya",

note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.; 12th International Conference on Computational Data and Social Networks, CSoNet 2023 ; Conference date: 11-12-2023 Through 13-12-2023",

year = "2024",

doi = "10.1007/978-981-97-0669-3_36",

language = "English",

isbn = "9789819706686",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "398--406",

editor = "H{\`a}, {Minh Ho{\`a}ng} and Xingquan Zhu and Thai, {My T.}",

booktitle = "Computational Data and Social Networks - 12th International Conference, CSoNet 2023, Proceedings",

}

Gaurav, A, Gupta, BB, Chui, KT & Arya, V 2024, Deep Learning Based Model for Stress Measurement in Online Social Networks. in MH Hà, X Zhu & MT Thai (eds), Computational Data and Social Networks - 12th International Conference, CSoNet 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14479 LNCS, pp. 398-406, 12th International Conference on Computational Data and Social Networks, CSoNet 2023, Hanoi, Viet Nam, 11/12/23. https://doi.org/10.1007/978-981-97-0669-3_36

Deep Learning Based Model for Stress Measurement in Online Social Networks. / Gaurav, Akshat; Gupta, Brij B.; Chui, Kwok Tai et al.
Computational Data and Social Networks - 12th International Conference, CSoNet 2023, Proceedings. ed. / Minh Hoàng Hà; Xingquan Zhu; My T. Thai. 2024. p. 398-406 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14479 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Deep Learning Based Model for Stress Measurement in Online Social Networks

AU - Gaurav, Akshat

AU - Gupta, Brij B.

AU - Chui, Kwok Tai

AU - Arya, Varsha

N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

PY - 2024

Y1 - 2024

N2 - 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.

AB - 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.

KW - Deep Learning

KW - Mental Health

KW - Online Social Networks

KW - Reddit

KW - Stress Detection

UR - http://www.scopus.com/inward/record.url?scp=85187659964&partnerID=8YFLogxK

U2 - 10.1007/978-981-97-0669-3_36

DO - 10.1007/978-981-97-0669-3_36

M3 - Conference contribution

AN - SCOPUS:85187659964

SN - 9789819706686

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 398

EP - 406

BT - Computational Data and Social Networks - 12th International Conference, CSoNet 2023, Proceedings

A2 - Hà, Minh Hoàng

A2 - Zhu, Xingquan

A2 - Thai, My T.

T2 - 12th International Conference on Computational Data and Social Networks, CSoNet 2023

Y2 - 11 December 2023 through 13 December 2023

ER -

Gaurav A, Gupta BB, Chui KT, Arya V. Deep Learning Based Model for Stress Measurement in Online Social Networks. In Hà MH, Zhu X, Thai MT, editors, Computational Data and Social Networks - 12th International Conference, CSoNet 2023, Proceedings. 2024. p. 398-406. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-981-97-0669-3_36