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. 2024 Jun 27:11:e55747.
doi: 10.2196/55747.

Insights Derived From Text-Based Digital Media, in Relation to Mental Health and Suicide Prevention, Using Data Analysis and Machine Learning: Systematic Review

Colm Sweeney #  1 , Edel Ennis #  1 , Maurice D Mulvenna  2 , Raymond Bond  2 , Siobhan O'Neill  1
Affiliations

Insights Derived From Text-Based Digital Media, in Relation to Mental Health and Suicide Prevention, Using Data Analysis and Machine Learning: Systematic Review

Colm Sweeney et al. JMIR Ment Health. .

Abstract

Background: Text-based digital media platforms have revolutionized communication and information sharing, providing valuable access to knowledge and understanding in the fields of mental health and suicide prevention.

Objective: This systematic review aimed to determine how machine learning and data analysis can be applied to text-based digital media data to understand mental health and aid suicide prevention.

Methods: A systematic review of research papers from the following major electronic databases was conducted: Web of Science, MEDLINE, Embase (via MEDLINE), and PsycINFO (via MEDLINE). The database search was supplemented by a hand search using Google Scholar.

Results: Overall, 19 studies were included, with five major themes as to how data analysis and machine learning techniques could be applied: (1) as predictors of personal mental health, (2) to understand how personal mental health and suicidal behavior are communicated, (3) to detect mental disorders and suicidal risk, (4) to identify help seeking for mental health difficulties, and (5) to determine the efficacy of interventions to support mental well-being.

Conclusions: Our findings show that data analysis and machine learning can be used to gain valuable insights, such as the following: web-based conversations relating to depression vary among different ethnic groups, teenagers engage in a web-based conversation about suicide more often than adults, and people seeking support in web-based mental health communities feel better after receiving online support. Digital tools and mental health apps are being used successfully to manage mental health, particularly through the COVID-19 epidemic, during which analysis has revealed that there was increased anxiety and depression, and web-based communities played a part in reducing isolation during the pandemic. Predictive analytics were also shown to have potential, and virtual reality shows promising results in the delivery of preventive or curative care. Future research efforts could center on optimizing algorithms to enhance the potential of text-based digital media analysis in mental health and suicide prevention. In addressing depression, a crucial step involves identifying the factors that contribute to happiness and using machine learning to forecast these sources of happiness. This could extend to understanding how various activities result in improved happiness across different socioeconomic groups. Using insights gathered from such data analysis and machine learning, there is an opportunity to craft digital interventions, such as chatbots, designed to provide support and address mental health challenges and suicide prevention.

Keywords: digital intervention; machine learning; mental health; text analysis.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram. NLP: natural language processing.
See this image and copyright information in PMC

References

    1. Coppersmith G, Dredze M, Harman C. Quantifying mental health signals in Twitter. Proceedings of the 2014 Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality; EMNLP '14; June 17, 2014; Baltimore, MA. 2014. pp. 51–60. https://aclanthology.org/W14-3207.pdf - DOI
    1. De Choudhury M, Kiciman E, Dredze M, Coppersmith G, Kumar M. Discovering shifts to suicidal ideation from mental health content in social media. Proc SIGCHI Conf Hum Factor Comput Syst. 2016 May;2016:2098–110. doi: 10.1145/2858036.2858207. https://europepmc.org/abstract/MED/29082385 - DOI - PMC - PubMed
    1. Park M, Cha C, Cha M. Depressive moods of users portrayed in Twitter. Proceedings of the 2012 ACM SIGKDD Workshop on Healthcare Informatics; HIKDD '12; August 12, 2012; Beijing, China. 2012. pp. 1–8. https://nyuscholars.nyu.edu/ws/files/134720119/depressive_moods_kdd.pdf
    1. Sik D, Németh R, Katona E. Topic modelling online depression forums: beyond narratives of self-objectification and self-blaming. J Ment Health. 2023 Apr;32(2):386–95. doi: 10.1080/09638237.2021.1979493. doi: 10.1080/09638237.2021.1979493. - DOI - DOI - PubMed
    1. Burnap P, Colombo G, Amery R, Hodorog A, Scourfield J. Multi-class machine classification of suicide-related communication on Twitter. Online Soc Netw Media. 2017 Aug;2:32–44. doi: 10.1016/j.osnem.2017.08.001. https://linkinghub.elsevier.com/retrieve/pii/S2468-6964(17)30060-5 S2468-6964(17)30060-5 - DOI - PMC - PubMed

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