JMIR Publications

ABSTRACT

Background:

There is an increasing number of online support groups where one can get advice and information regarding topics related to mental health.

Objective:

We aimed to investigate what needs are met by Internet users through peer-to-peer interactions.

Methods:

We searched four databases until July 24, 2021. We included qualitative or mixed methods (qualitative-quantitative) studies investigating interactions among Internet users with mental disorders. We used phi coefficient and applied machine learning techniques to investigate associations between type of mental disorders and online interactions connected with searching for help or support. Our protocol was registered in the Open Science Framework (osf.io/j3azv).

Results:

Out of 11,316 identified records, we included 38, which analysed 79,735 Internet users and 218,156 posts. Most frequently interactions concerned people experiencing eating disorders (19%), depression (19%), and substance use disorder (17%). We grouped interactions between Internet users into 42 codes, with ‘network’ being the most common (7%). Most frequently co-existing codes were ‘request for information’ and ‘sharing self-disclosure’ (34 times, φ = 0.57, p <.001). Algorithms that provided the best accuracies in classifying disorders by interactions included logistic regression and decision trees (81%). However, this analysis should be interpreted more as a proof of concept. Included studies were moderately valuable in terms of quality.

Conclusions:

Internet user mostly need help to answer their questions and chatter. Obtaining more data about the online interactions between people with mental disorders could help properly apply machine learning methods to create a tool that helps in screening or even supports assessing mental state. Clinical Trial: Our protocol was registered in the Open Science Framework (osf.io/j3azv).