Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses

Abstract

Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment, however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring. This requires a holistic approach to understanding mental health issues that are missing from the existing research. Mental health should be understood in the context of social and environmental factors. More research and awareness are needed, as well as interventions to address root causes. The effectiveness and risks of medications should also be studied. This paper proposes a big data and machine learning-based approach for the automatic discovery of parameters related to mental health from Twitter data. The parameters are discovered from three different perspectives, Drugs & Treatments, Causes & Effects, and Drug Abuse. We used Twitter to gather 1,048,575 tweets in Arabic about psychological health in Saudi Arabia. We built a big data machine learning software tool for this work. A total of 52 parameters were discovered for all three perspectives. We defined 6 macro-parameters (Diseases & Disorders, Individual Factors, Social & Economic Factors, Treatment Options, Treatment Limitations, and Drug Abuse) to aggregate related parameters. We provide a comprehensive account of mental health, causes, medicines and treatments, mental health and drug effects, and drug abuse, as seen on Twitter, discussed by the public and health professionals. Moreover, we identify their associations with different drugs. The work will open new directions for social media-based identification of drug use and abuse for mental health, as well as other micro and macro factors related to mental health. The methodology can be extended to other diseases and provides a potential for discovering evidence for forensics toxicology from social and digital media.

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“…The paper belongs to our extensive research on utilizing Information and Communication Technology (ICT) to tackle issues in smart cities and societies. Our work encompasses deep journalism [39,255], labor economics [256], transportation [39], smart families and homes [40], healthcare [50,257], education during COVID-19 [52], and event detection [55]. In the future, we aim to enhance the methodology in this paper through advanced deep learning techniques and apply them to enhancing tourism and other societal, economic, environmental, and cultural issues.…”

Multi-generational labour markets: Data-driven discovery of multi-perspective system parameters using machine learning

“…This paper is part of our broader work on the use of information and communication technology (ICT) to address challenges facing smart cities and societies. Our work on this topic has included the concept of Deep Journalism, 145,146 as well as research on topics such as transportation, 146 tourism, 147 smart families and homes, 148 healthcare services for cancer, 58 mental health, 149 education during the COVID-19 pandemic, 150 energy systems 151 and AI-based event detection. 152 Future work will be directed to improving the methodological approach presented in this paper using advanced deep learning methods and their applications to investigate and improve labour economics and other problems facing our societies.…”

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