The sustainability of society can be negatively impacted by mental health problems on both people and communities. The treatment of mental disorders faces a number of difficulties, but it is more crucial to address their underlying causes because doing so can help prevent mental health issues from developing or returning. This necessitates a more comprehensive understanding of mental health issues than what has been found in previous studies. It is important to consider social and environmental aspects while analysing mental health. There is a need for more investigation and education, as well as for root cause-focused solutions. Medication dangers and effectiveness should both be researched. This study suggests a big data and machine learning-based method for automatically identifying mental health-related factors from Twitter data. 52 parameters in total were found for the three views. To combine relevant parameters, we established six macro-parameters. We give a thorough description of mental health, including its causes, medications and therapies, effects of drugs on the brain, and drug abuse, as discussed by the general people and medical professionals on Twitter. Additionally, we pinpoint their connections to other medicines. The research will pave the way for new methods of identifying drug addiction and use in relation to mental health on social media, as well as other micro- and macro-factors. The approach can be applied to different illnesses and offers the chance to find forensic toxicological evidence from social and digital media.
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Journal of Forensic Medicine received 165 citations as per Google Scholar report
