Abstract
This paper presents a critical assessment analysis on mental health detection in Online Social Networks (OSNs) based on the data sources, machine learning techniques, and feature extraction method. The appropriateness of the mental health detection was also investigated by identifying its data analysis method, comparison, challenges, and limitations. This study reviewed articles published in major databases between 2007 and 2018 through keyword searches. The articles were screened base on their titles and abstracts before the full texts were reviewed. The articles were coded in accordance with data set (e.g., data sources, keywords, and geographical locations), method of data analysis, machine learning or deep learning technique, classifier performance, and feature extraction method. 22 articles were selected for review from the total of 2770. As OSNs exhibit high potential as a data source in early detection of mental health problems, most researchers used text analysis on a new data set extracted from different OSNs sources. The extracted data were examined using a statistical analysis or machine learning techniques. Several studies also applied multimethod techniques, which included distributing questionnaires while requesting for the respondents’ consent to later access and extract information from his/her OSNs account. Big data in OSNs contribute on mental health problem detection. The presented method is an alternative approach to the early detection of mental health problems rather than using traditional strategies, such as collecting data through questionnaires or devices and sensors, which are time-consuming and costly. However, mental health problem detection through OSNs necessitates a comprehensive adoption, innovative algorithms, and computational linguistics to describe its limitations and challenges. Moreover, referrals from mental health specialists as subject matter experts are also required to help obtain accurate and effective information.
Highlights
The current changes in the social landscape have contributed significantly to the increase in the rate of mental health problems and psychological disorders
The objective of this systematic literature review is to conduct a critical assessment analysis on detection of mental health problems using Online Social Networks (OSNs). This analysis consists of the data source, the feature extraction method, and classifier performance in machine learning techniques
The purpose of this systematic review is to conduct a critical assessment on mental health problem detection
Summary
The current changes in the social landscape have contributed significantly to the increase in the rate of mental health problems and psychological disorders. The associate editor coordinating the review of this manuscript and approving it for publication was Shen Yin. individual’s way of life, such as work stress, bad financial situation, family issues, relationship problems, and violence, along with environmental factors [2]. Individual’s way of life, such as work stress, bad financial situation, family issues, relationship problems, and violence, along with environmental factors [2] These situations can contribute to mental health disorders, such as depression, anxiety, stress, and various psychological disorders that exert an impact on the quality of life and holistic well-being of a person. In 2018, WHO released a guideline on managing the physical
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