Chronic stress may also be linked with several physiological illnesses. Mental strain is generally recognised as a social concern, potentially leading to a functional impairment at work. It circumscribes how an individual thinks, feels and responds to any circumstances. Mental soundness is a condition of well-being wherein a person understands his/her potential, participates in his or her community and is able to deal effectively with the challenges and obstacles of everyday life. The DAO provides a powerful framework and a useful resource that can be expanded and adapted to a wide range of substance use and mental health domains to help advance big data analytics of web-based data for substance use epidemiology research. The ontology is recurrently updated to capture evolving concepts in different contexts and applied to analyze data related to social media and dark web marketplaces. The integration of the ontology with machine learning algorithms dramatically decreased the false alarm rate by adding external knowledge to the machine learning process. The ontology is flexible and can easily accommodate new concepts. The current version of the DAO comprises 315 classes, 31 relationships, and 814 instances among the classes. The quality of the ontology was evaluated using a set of tools and best practices recognized by the semantic web community and the artificial intelligence community that engage in natural language processing. The 101 method includes determining the domain and scope of ontology, reusing existing knowledge, enumerating important terms in ontology, defining the classes, their properties and creating instances of the classes. The domain and scope of the DAO were defined using competency questions from popular ontology methodology (101 ontology development). The key aims were to describe the development and application of the drug abuse ontology (DAO) as a framework for analyzing web-based and social media data to inform public health and substance use research in the following areas: determining user knowledge, attitudes, and behaviors related to nonmedical use of buprenorphine and illicitly manufactured opioids through the analysis of web forum data Prescription Drug Abuse Online Surveillance analyzing patterns and trends of cannabis product use in the context of evolving cannabis legalization policies in the United States through analysis of Twitter and web forum data (eDrugTrends) assessing trends in the availability of novel synthetic opioids through the analysis of cryptomarket data (eDarkTrends) and analyzing COVID-19 pandemic trends in social media data related to 13 states in the United States as per Mental Health America reports. There is a growing interest in the use of these novel data sources for epidemiological surveillance of substance use behaviors and trends. Web-based resources and social media platforms play an increasingly important role in health-related knowledge and experience sharing. Finally, we conclude by discussing the current limitations and future directions in the research of suicidal ideation detection. Furthermore, we present several Reddit-based datasets utilized to construct suicidal ideation detection models. The findings of the review outline the prevalent methods of data collection, data annotation, data preprocessing, feature engineering, model development, and evaluation. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we selected 26 recent studies, published between 20. To achieve this objective, we conducted a literature review of the recent articles detailing machine learning and natural language processing techniques applied to Reddit data to detect the presence of suicidal ideations. The objective of this paper is to investigate methods employed to detect suicidal ideations on the Reddit forum. This prompted research that applies machine learning and natural language processing techniques to detect suicidality among social media and forum users. At the same time, individuals contemplating suicide are increasingly turning to social media and online forums, such as Reddit, to express their feelings and share their struggles with suicidal thoughts. However, there are challenges associated with conventional suicide-risk screening methods. The early detection of suicidal ideation is critical for suicide prevention. Hundreds of thousands of people commit suicide every year. Suicide is a major public-health problem that exists in virtually every part of the world.
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