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.

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

“…The Arabic content (key terms, tweets) is not presented in this paper due to the publisher’s requirements. It can be found in an extended version of this paper made available on a preprint server [ 16 ].…”

“…Some of the hashtags used include World Suicide Prevention Day, Suicide Awareness Month, suicide prevention, social anxiety, social phobia, depression, depression month, and seasonal depression. The list of Arabic key terms used in data collection can be found in an extended preprint version of this paper [ 16 ]. The data was collected from the 1 to the 31 of October 2022.…”

“…We intentionally keep it short here. An extended version of this paper including the literature review has been made available as a preprint [ 16 ].…”