THE MENTAL HEALTH EFFECT FOR ADOLESCENT WITH THE USE OF MACHINE LEARNING ON SOCIAL MEDIA DATA
DOI:
https://doi.org/10.1234/aira.v3i2.72Keywords:
Adolescent, Mental Health, Machine Learning, Social Media, YouthAbstract
The prevalence of mental health disorders among adolescents has become a critical public health concern, particularly given the increasing engagement with social media. This systematic literature review aims to comprehensively analyze the implications of machine learning (ML) applied to social media data in identifying and predicting mental health effects in adolescents. Despite an exponential rise in the use of social media platforms, there remains an urgent need for effective methods to monitor and address adolescent mental health issues. Various studies have highlighted that social media can both exacerbate and alleviate mental health challenges, yet the integration of innovative analytical techniques such as machine learning has not been sufficiently explored. Thus, the problem this review addresses is the gap in systematic understanding regarding how machine learning can effectively analyze social media interactions to yield insights into mental health conditions faced by adolescents. The justification for conducting this review lies in the alarming statistics that demonstrate a rise in anxiety and depression among adolescents—the demand for mental health services outstrips the available resources across various institutions, particularly in educational settings as shown in studies related to college mental health outcomes Auerbach et al. (2018). This review endeavors to clarify whether machine learning can provide timely and effective predictive measures, thereby contributing to significant advancements in intervention methodologies and support systems tailored for adolescents. The objectives encompass evaluating existing machine learning models, synthesizing findings from relevant studies, and formulating research questions that address the nuanced interplay between social media usage and adolescent mental health outcomes.
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