Studying Unemployment Effects on Mental Health: Social Media versus the Traditional Approach

Samara Ahmed, Adil E. Rajput, Akila Sarirete, Asma Aljaberi, Ohoud Alghanem and Abrar Alsheraigi
Additional contact information
Samara Ahmed: Psychiatry Division, College of Medicine, King Abdulaziz University, Jeddah 22252, Saudi Arabia
Adil E. Rajput: Computer Science Department, College of Engineering, Effat University, Jeddah 22332, Saudi Arabia
Akila Sarirete: Computer Science Department, College of Engineering, Effat University, Jeddah 22332, Saudi Arabia
Asma Aljaberi: Information Systems Department, College of Engineering, Effat University, Jeddah 22332, Saudi Arabia
Ohoud Alghanem: Information Systems Department, College of Engineering, Effat University, Jeddah 22332, Saudi Arabia
Abrar Alsheraigi: Information Systems Department, College of Engineering, Effat University, Jeddah 22332, Saudi Arabia

Sustainability, 2020, vol. 12, issue 19, 1-14

Abstract: Social media, traditionally reserved for social exchanges on the Internet, has been increasingly used by researchers to gain insight into different facets of human life. Unemployment is an area that has gained attention by researchers in various fields. Medical practitioners especially in the area of mental health have traditionally monitored the effects of involuntary unemployment with great interest. The question we want to address is as follows: while many researchers have been using data from social media and microblogging sites in the past few years, do they provide results consistent with traditional research? Furthermore, if the data are indeed consistent, are they detailed enough to deduce possible reasons and remedies? We believe that having a concise answer to these questions is imperative for a sustainable mechanism for medical practitioners and researchers to gather and analyze data. The stigma of mental health prevents a good portion of society from seeking help, but the anonymity provided by the Internet could shatter such barriers, thus allowing people affected by conditions such as mental health and unemployment to express themselves freely. In this work, we compare the feedback gathered from social media using crowdsourcing techniques to results obtained prior to the advent of social media and microblogging. We find that the results are consistent in terms of (1) financial strain being the biggest stressor and concern, (2) the onslaught of depression being typical and (3) possible interventions, including reemployment and support from friends and family, playing a crucial role in minimizing the effects of involuntary unemployment. Lastly, we could not find enough evidence to study effects on physical health and somatization in this work.

Keywords: social media; unemployment; crowdsourcing; natural language processing; mental health (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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