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Social Network Analysis and Mining

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01-12-2023 | Original Article

Identifying discernible indications of psychological well-being using ML: explainable AI in reddit social media interactions

Authors: Pahalage Dona Thushari, Nitisha Aggarwal, Vajratiya Vajrobol, Geetika Jain Saxena, Sanjeev Singh, Amit Pundir

Published in: Social Network Analysis and Mining | Issue 1/2023

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Abstract

The article delves into the application of machine learning and explainable AI to analyze Reddit social media interactions for identifying psychological well-being indicators. It discusses the significance of mental health data analysis, the use of contextual models like BERT and MentalBERT for classification, and the importance of explainable AI techniques like LIME. The study highlights the potential of these methods in understanding and addressing mental health issues, offering valuable insights for healthcare professionals and researchers.

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Abstract

Psychological well-being is a multidimensional construct and identifying it using a systematic, comprehensive approach offers insights fundamental to critical outcomes. Social networks are valuable resources for research, providing a pragmatic way of generating empirical evidence on psychological well-being based on the textual indicators across different populations. This study analyzed the information on various Reddit social media groups dedicated to mental health. The classes, namely depression, anxiety, bipolar, and SuicideWatch, using the SWMH dataset have been analyzed. The text-based interactions of persons with mental illness have common motifs like negative language and expressions like 'hopelessness,' 'emptiness,' or 'helplessness.' Topic modeling identified recurring themes and subjects that helped classify discernible factors influencing mental health. Classifiers for multiclass classification to classify targeted mental health issues based on users' network behavior and posts were trained and tested to get predictions on context (e.g., MentalBERT) and non-context-based (e.g., LR and NB) models. The MentalBERT model outperformed the other eight baseline models with an average accuracy of 76.70%, which is 4% more than reported in previous studies. Explainable AI was used to examine the trustworthiness of each model, and the explanations were evaluated using the LIME model. Explainability is crucial as mental health data characterizes syndromes, outcomes, disorders, and signs/symptoms exhibiting probabilistic interrelationships with each other. Explanations of these intricate interconnections can assist the extensive research around the model of well-being and interventions intended to improve the human condition and distill positive human functioning.

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Title: Identifying discernible indications of psychological well-being using ML: explainable AI in reddit social media interactions
Authors: Pahalage Dona Thushari
Nitisha Aggarwal
Vajratiya Vajrobol
Geetika Jain Saxena
Sanjeev Singh
Amit Pundir
Publication date: 01-12-2023
Publisher: Springer Vienna
Published in: Social Network Analysis and Mining / Issue 1/2023
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-023-01145-1

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Identifying discernible indications of psychological well-being using ML: explainable AI in reddit social media interactions

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Other articles of this Issue 1/2023

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Springer Professional "Wirtschaft"

Springer Professional "Technik"

An exploratory study of net zero discourse based on South Korean newspapers: a topic modeling and sentiment analysis approach

Decision-making approaches in the antagonistic digital communication of the online communities users

An employee retention model using organizational network analysis for voluntary turnover

FNR: a similarity and transformer-based approach to detect multi-modal fake news in social media

How COVID-19 affects user interaction with online streaming service providers on twitter

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