Monitoring mental health and substance abuse is a crucial aspect of public healthcare as early detection and intervention can improve health outcomes and reduce the negative impact on individuals, communities, and populations. Internet use is predominant and essential in everyday life; 97% of internet users worldwide are active on social media, and the number of social media accounts per average internet user has grown exponentially in recent years. Every minute, people around the world publicly share volumes of personal and communal health information on several digital platforms including social media, discussion forums and blogs, and various internet search engines. The low-cost stream of available data on social media and other internet-based sources is increasingly exploited by physicians, patients, and the public. In this general scenario, machine learning techniques allow for the efficient analysis of large quantities of social media data, enabling researchers to identify patterns and indicators of mental health and substance abuse amongst the public.
The use of web data and social media also poses new opportunities for monitoring mental health pharmacovigilance and substance abuse. However, this approach is not without its challenges. The sheer volume of social media data requires advanced computational resources, and the reliability and accuracy of the data must be ensured. Additionally, privacy concerns must be carefully considered and addressed. Despite these challenges, the potential benefits of machine learning approaches to monitoring mental health and substance abuse are significant. By analyzing social media data, researchers can identify at-risk individuals, track the spread of mental health and substance abuse disorders, and even predict future occurrences.
Deepening this research can foster interdisciplinary collaborations and encourage the development of novel approaches by facilitating the dissemination of new findings and methodologies. In turn, this can lead to more effective and efficient monitoring and intervention strategies, as well as contribute to improving mental health outcomes of individuals and communities from a public health perspective.
Considering these points, this Research Topic aims to gather contributions reporting examples of applications of machine learning methods on social media data for monitoring mental health and substance abuse together with the challenges and opportunities in this context. Submissions can also address how machine learning techniques can improve the efficiency of public health surveillance strategies, using data from different social media sources.
The following article types are welcome into this collection: Brief Research Report, Hypothesis & Theory, Methods, Original Research, Review, Study Protocol, and Systematic Review.
Monitoring mental health and substance abuse is a crucial aspect of public healthcare as early detection and intervention can improve health outcomes and reduce the negative impact on individuals, communities, and populations. Internet use is predominant and essential in everyday life; 97% of internet users worldwide are active on social media, and the number of social media accounts per average internet user has grown exponentially in recent years. Every minute, people around the world publicly share volumes of personal and communal health information on several digital platforms including social media, discussion forums and blogs, and various internet search engines. The low-cost stream of available data on social media and other internet-based sources is increasingly exploited by physicians, patients, and the public. In this general scenario, machine learning techniques allow for the efficient analysis of large quantities of social media data, enabling researchers to identify patterns and indicators of mental health and substance abuse amongst the public.
The use of web data and social media also poses new opportunities for monitoring mental health pharmacovigilance and substance abuse. However, this approach is not without its challenges. The sheer volume of social media data requires advanced computational resources, and the reliability and accuracy of the data must be ensured. Additionally, privacy concerns must be carefully considered and addressed. Despite these challenges, the potential benefits of machine learning approaches to monitoring mental health and substance abuse are significant. By analyzing social media data, researchers can identify at-risk individuals, track the spread of mental health and substance abuse disorders, and even predict future occurrences.
Deepening this research can foster interdisciplinary collaborations and encourage the development of novel approaches by facilitating the dissemination of new findings and methodologies. In turn, this can lead to more effective and efficient monitoring and intervention strategies, as well as contribute to improving mental health outcomes of individuals and communities from a public health perspective.
Considering these points, this Research Topic aims to gather contributions reporting examples of applications of machine learning methods on social media data for monitoring mental health and substance abuse together with the challenges and opportunities in this context. Submissions can also address how machine learning techniques can improve the efficiency of public health surveillance strategies, using data from different social media sources.
The following article types are welcome into this collection: Brief Research Report, Hypothesis & Theory, Methods, Original Research, Review, Study Protocol, and Systematic Review.
