Elsevier

Decision Support Systems

Volume 162, November 2022, 113792
Decision Support Systems

Mining voices from self-expressed messages on social-media: Diagnostics of mental distress during COVID-19

https://doi.org/10.1016/j.dss.2022.113792Get rights and content

Highlights

  • Uses computational intelligence and NLP to unveil factors underlying mental health issues after COVID-19.

  • Uncovers that pandemic and subsequent lockdown anxiety are critical for mental health outcomes.

  • Reveals that financial burden causes suicidal ideation before the pandemic.

  • Offers insights for health policy makers, governments, psychiatric practitioners, and psychologists.

Abstract

The COVID-19 pandemic has had a severe impact on mankind, causing physical suffering and deaths across the globe. Even those who have not contracted the virus have experienced its far-reaching impacts, particularly on their mental health. The increased incidences of psychological problems, anxiety associated with the infection, social restrictions, economic downturn, etc., are likely to aggravate with the virus spread and leave a longer impact on humankind. These reasons in aggregation have raised concerns on mental health and created a need to identify novel precursors of depression and suicidal tendencies during COVID-19. Identifying factors affecting mental health and causing suicidal ideation is of paramount importance for timely intervention and suicide prevention. This study, thus, bridges this gap by utilizing computational intelligence and Natural Language Processing (NLP) to unveil the factors underlying mental health issues. We observed that the pandemic and subsequent lockdown anxiety emerged as significant factors leading to poor mental health outcomes after the onset of COVID-19. Consistent with previous works, we found that psychological disorders have remained pre-eminent. Interestingly, financial burden was found to cause suicidal ideation before the pandemic, while it led to higher odds of depressive (non-suicidal) thoughts for individuals who lost their jobs. This study offers significant implications for health policy makers, governments, psychiatric practitioners, and psychologists.

Keywords

Mental health
Depression
Suicidal ideation
Natural language processing
Social-media
COVID-19
Pandemic

Cited by (0)

Rahul Kumar is an assistant professor in information systems at Indian Institute of Management (IIM) Sambalpur, India. He holds his doctorate from Indian Institute of Management (IIM) Ranchi, specializing in the area of information systems. His research interests are recommender systems, text mining, machine learning and natural language processing. He has authored articles on recommender systems and collaborative filtering in reputed journals such as Information Systems and E-Business Management (ISEB), Journal of Behavioral and Experimental Finance (JBEF) and International Journal of Operational Research (IJOR).

Shubhadeep Mukherjee is an assistant professor in the area of operations management and decision sciences at Xavier Institute of Management Bhubaneswar (XIMB), Xavier University. He has got his PhD from Indian Institute of Management (IIM) Ranchi specializing in the area of information systems. Dr. Mukherjee has published his work in the area of applications of natural language processing and machine learning. He has keen research interests in humanitarian aspects of machine learning based research.

Tsan-Ming Choi (Jason) is currently a professor in Department and Graduate Institute of Business Administration of National Taiwan University. He has published extensively in journals such as Decision Sciences, Decision Support Systems, EJOR, IEEE Transactions (various), Journal of Operations Management, Naval Research Logistics, Production and Operations Management, Transportation Research (Parts B, D, E). He is currently the co-editor-in-chief of Transportation Research Part E, a department editor of IEEE Transactions on Engineering Management, a senior editor of Decision Support Systems, and Production and Operations Management, and an associate editor of Decision Sciences. He is also a member of the engineering panel, Research Grants Council (Hong Kong).

Lalitha Dhamotharan is a lecturer of entrepreneurship and business analytics at the University of Exeter Business School. She holds a PhD in econometrics with a focus on international finance. Before joining the University of Exeter, she was a postgraduate researcher as well as the Hub of All Things (HAT) research and policy manager for an EPSRC funded project at the University of Warwick. She also has experience in teaching for the innovation and entrepreneurship course at the University of Warwick. Lalitha worked as a senior analyst dealing with research and policies issues on various sectors in South East Asia in Malaysia.

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