Social Media as a Tool for Depression Detection

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Social Media as a Tool for Depression Detection

Nowadays, many psychologists and other mental health care professionals are concerned with the problem of the significant influence of social media on the development of depression, especially in adolescent populations. Moreover, enormous efforts are put into understanding this issue more comprehensively and seeking ways to prevent this condition. The chosen article is Exploring the Dominant Features of Social Media for Depression Detection. Thirteen authors from leading universities of the Republic of Korea have worked on this study, including Jamil Hussein from Kyung Hee University. The article appeared in the Journal of Information Science and is related computer technologies field. The intended audience is physicians who can diagnose mental illnesses and psychiatrists. The authors are guided by the tendency among researchers to use social media linguistic data on Facebook to detect depressive signs, which facilitates early treatment of depression. Hussein et al. have identified predominant features that help assess individuals with and without depressive symptoms properly (756). The reviewed article would make a viable source for a research paper on the link between social media and depression because it suggests an adequate technological tool for detecting depressive signs. However, it lacks an analysis of the ethical aspect of using similar instruments before contact with a potential patient.

The article claims that it is possible to identify the signs of depression using a specifically developed Socially Mediated Patient Portal (SMPP) that analyzes Facebook users posts. At the same time, the utilization of such technologies may raise ethical issues. To name a few, it can be collecting social media data without the prior consent of a user or impaired access to such technologies because of socio-economic reasons (Laacke et al. 4-5). In addition, the article by Hussain et al. argues that various demographic dimensions, including education, age, and gender, are correlated with depressive symptoms (755). However, it would be more relevant to focus on the population of a particular age, namely adolescents, because they represent a risk group for social-media-induced depression and suicide (Vidal et al. 235-236). The study by Hussain et al. is well-organized and contains a detailed description of the methods and participants, as well as the SMPP application work principles. The relevance of the article to the selected topic can be explained by the extensive use of social media, mostly among young people, and the subsequent challenges of this phenomenon. I have chosen this article because of the rapid development of technologies and their growing use in medical settings, which contributes to the early detection of impaired health conditions. I found this article with the help of the SAGE Publications database using the keywords social media and depression. Thus, the potential research topic can examine the technologys effectiveness in detecting depressive signs and the ethical implications of employing such methods in practice.

The methodology of the discussed article possesses particular strengths because it uses an extensive data set accessed through the mypersonality project. This tool allowed for examining data of more than six thousand Facebook users of different ages and gender. In addition, the researchers described how they generated depression-detecting features through the SMPP application, which helped in distinguishing help-seeking, depressive, self-disclosure, and other marker taggers (Hussain et al. 743-744). On the one hand, the methodology appears to be effective because it shows adequate results regarding a large number of participants, and thus it allows a precise evaluation of the instrument. On the other hand, it has not been explained properly how this detecting method can be employed in practice without igniting ethical concerns.

Works Cited

Hussain, Jamil, et al. Exploring the Dominant Features of Social Media for Depression Detection. Journal of Information Science, vol. 46, no. 6, 2020, pp. 739-759. Web.

Laacke, Sebastian, et al. Artificial Intelligence, Social Media and Depression. A New Concept of Health-Related Digital Autonomy. The American Journal of Bioethics, vol. 21, no. 7, 2021, pp. 4-20. Web.

Vidal, Carol, et al. Social Media Use and Depression in Adolescents: A Scoping Review. International Review of Psychiatry, vol. 32, no. 3, 2020, pp. 235-253. Web.

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