About this Research Topic
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.
Keywords: Machine Learning, Social Media, Data mining, public health surveillance, mental health, substance abuse, psychotropic substance
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.