Abstract
Among mental health diseases, depression is one of the most severe, as it often leads to suicide; due to this, it is important to identify and summarize existing evidence concerning depression sign detection research on social media using the data provided by users. This review examines aspects of primary studies exploring depression detection from social media submissions (from 2016 to mid-2021). The search for primary studies was conducted in five digital libraries: ACM Digital Library, IEEE Xplore Digital Library, SpringerLink, Science Direct, and PubMed, as well as on the search engine Google Scholar to broaden the results. Extracting and synthesizing the data from each paper was the main activity of this work. Thirty-four primary studies were analyzed and evaluated. Twitter was the most studied social media for depression sign detection. Word embedding was the most prominent linguistic feature extraction method. Support vector machine (SVM) was the most used machine-learning algorithm. Similarly, the most popular computing tool was from Python libraries. Finally, cross-validation (CV) was the most common statistical analysis method used to evaluate the results obtained. Using social media along with computing tools and classification methods contributes to current efforts in public healthcare to detect signs of depression from sources close to patients.
Highlights
Mental disorders are a worldwide health problem affecting a large number of people and causing numerous deaths every year
We can say that the focus of this research is valuable in the application of tools to detect the onset of depressive problems in people so that they can be used in healthcare institutions, as well as in the support of individuals, making those who suffer from mental problems more participatory in relation to their mental health
We conclude that the principal differences between our literature review and similar works are as follows: (1) we analyze the most recent relevant works; (2) we identify the social media sites most commonly studied and the features of the datasets retrieved; and we determine (3) the linguistic feature extraction methods, (4) machine-learning algorithms, (5) computing tools, and (6) mathematical analysis methods most commonly applied in depression sign detection from social media
Summary
Mental disorders are a worldwide health problem affecting a large number of people and causing numerous deaths every year. The stress factors of the COVID-19 crisis indicate that a great number of people in the world may be in the course of developing depression as a result of the new and unusual lifestyle caused by the pandemic. It is common for the effects of a viral disease to affect people’s moods, causing them to go into depressive states; the COVID-19 crisis has increased the chances of depression, which in turn will make recovery from the pandemic harder across a spectrum of needs [4]. When the period of social isolation finishes, people suffering from depression will have a harder time returning to their common social activities and exercise, and when the virus infection abates, people with depression are more likely to suffer from immunological problems, making them more prone to other conditions [6]
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