JMIR Publications

ABSTRACT

Background:

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 will determine how machine learning and data analysis can be applied to text-based digital media data, to understand mental health and to 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 hand-search using Google Scholar. The searches were carried out using the following categories: (mental health OR suicide) AND machine learning AND data analysis AND digital interventions.

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) Understanding 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 wellbeing.

Conclusions:

Our findings show that data analysis and machine learning can be utilized to gain valuable insights: where online conversations relating to depression have shown to vary among different ethnic groups; teenagers engage in an online conversation about suicide more often than adults; and people seeking support in online 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, where analysis has revealed that there was increased anxiety and depression, and online communities played a part 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 digital media analysis in mental health and suicide prevention. In addressing depression, a crucial step involves identifying the factors that contribute to happiness and employing machine learning to forecast these sources of 'happiness'. This could extend to understanding how various activities result in improved happiness across different socio-economic 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.