A systematic literature review of machine learning in online social network data for mental health

Abstract

good opportunity to review and quickly diagnose a particular disease by monitoring the previous
data. The biggest challenge in this is that the nature of Big data is such that it is difficult to sort and
process. The aim of this paper is to find the usefulness of applying various machine learning
techniques to the online data for health informatics
Resources: Three databases were employed i.e Web of Science, IEEE library. We focussed on articles
that were published between 2009 till 2019. Further , those publications are considered that applied
ML for health informatics for mental disorderfor further systematic review.
Results: We identified 710 results from IEEE and 201 from Web of science of past 10 years out of
which we identified eligible studies. ML models that have been used in the eligible papers are those
employing the study on mental health and memory related disorders. Mental state of persons suffering
from depression or any disorders can be easily extracted from social network platforms in the forms
of sentiments,emotionsand thoughts expressed by users at the social sites.