research-article

Towards Mental Health Analysis in Social Media for Low-resourced Languages

Author:

Muskan GargAuthors Info & Claims

ACM Transactions on Asian and Low-Resource Language Information Processing, Volume 23, Issue 3

Article No.: 47, Pages 1 - 22

https://doi.org/10.1145/3638761

Published: 09 March 2024 Publication History

Abstract

The surge in internet use for expression of personal thoughts and beliefs has made it increasingly feasible for the social Natural Language Processing (NLP) research community to find and validate associations between social media posts and mental health status. Cross-sectional and longitudinal studies of low-resourced social media data bring to fore the importance of real-time responsible Artificial Intelligence (AI) models for mental health analysis in native languages. Aiming at classifying research for social computing and tracking advances in the development of learning-based models, we propose a comprehensive survey on mental health analysis for social media and posit the need of analyzing low-resourced social media data for mental health. We first classify three components for computing on social media as: SM- data mining/natural language processing on social media, IA- integrated applications with social media data and user-network modeling, and NM- user and network modeling on social networks. To this end, we posit the need of mental health analysis in different languages of East Asia (e.g., Chinese, Japanese, Korean), South Asia (Hindi, Bengali, Tamil), Southeast Asia (Malay, Thai, Vietnamese), European languages (Spanish, French) and the Middle East (Arabic). Our comprehensive study examines available resources and recent advances in low-resourced languages for different aspects of SM, IA, and NM to discover new frontiers as potential field of research.

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Cited By

Nedungadi PVeena GTang KMenon RRaman R(2025)AI Techniques and Applications for Online Social Networks and Media: Insights From BERTopic ModelingIEEE Access10.1109/ACCESS.2025.354379513(37389-37407)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3543795
Kim JPark OPark E(2025) MOSS-6 : a multi-label dataset and deep learning model for detecting diverse social support-seeking behaviours in online mental health communities Information, Communication & Society10.1080/1369118X.2025.2460558(1-35)Online publication date: 10-Feb-2025
https://doi.org/10.1080/1369118X.2025.2460558
Mohmand RHabib UUsman MBaili JNam Y(2024)A Deep Learning Approach for Automated Depression Assessment Using Roman UrduIEEE Access10.1109/ACCESS.2024.351926412(193387-193401)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3519264
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Index Terms

Towards Mental Health Analysis in Social Media for Low-resourced Languages
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing design and evaluation methods
      1. Social network analysis
    2. Collaborative and social computing theory, concepts and paradigms
      1. Social engineering (social sciences)
      2. Social media
2. Information systems
  1. Information systems applications
    1. Data mining
  2. World Wide Web
    1. Web applications
      1. Social networks
    2. Web searching and information discovery
      1. Personalization

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cover image ACM Transactions on Asian and Low-Resource Language Information Processing

ACM Transactions on Asian and Low-Resource Language Information Processing Volume 23, Issue 3

March 2024

277 pages

EISSN:2375-4702

DOI:10.1145/3613569

Editor:
Imed Zitouni
Google, USA

Issue’s Table of Contents

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 09 March 2024

Online AM: 30 December 2023

Accepted: 16 December 2023

Revised: 12 September 2023

Received: 01 November 2022

Published in TALLIP Volume 23, Issue 3

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Cited By

Nedungadi PVeena GTang KMenon RRaman R(2025)AI Techniques and Applications for Online Social Networks and Media: Insights From BERTopic ModelingIEEE Access10.1109/ACCESS.2025.354379513(37389-37407)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3543795
Kim JPark OPark E(2025) MOSS-6 : a multi-label dataset and deep learning model for detecting diverse social support-seeking behaviours in online mental health communities Information, Communication & Society10.1080/1369118X.2025.2460558(1-35)Online publication date: 10-Feb-2025
https://doi.org/10.1080/1369118X.2025.2460558
Mohmand RHabib UUsman MBaili JNam Y(2024)A Deep Learning Approach for Automated Depression Assessment Using Roman UrduIEEE Access10.1109/ACCESS.2024.351926412(193387-193401)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3519264
Gavhane JPrasad RJadhav AParashar S(2024)Natural Language Processing for Emotion Recognition and AnalysisAffective Computing for Social Good10.1007/978-3-031-63821-3_6(107-128)Online publication date: 8-Oct-2024
https://doi.org/10.1007/978-3-031-63821-3_6
Mishra DDeshpande SAnna MTiwari A(2024)Exploring the Ethical Dimensions and Societal Consequences of Affective ComputingAffective Computing for Social Good10.1007/978-3-031-63821-3_5(91-105)Online publication date: 8-Oct-2024
https://doi.org/10.1007/978-3-031-63821-3_5
Lokhande HGarware CKudale TKumar R(2024)Personalized Well-Being Interventions (PWIs): A New Frontier in Mental HealthAffective Computing for Social Good10.1007/978-3-031-63821-3_10(183-200)Online publication date: 8-Oct-2024
https://doi.org/10.1007/978-3-031-63821-3_10

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