Digital Innovations in Mental Health and Education: Leveraging Social Media and Machine Learning

The recent developments in the intersection of social media, mental health, and academic performance research highlight a growing emphasis on leveraging digital footprints for mental health interventions and understanding the dual role of social media in education. Innovations in machine learning (ML) and deep learning (DL) are at the forefront, enabling the analysis of vast amounts of data from social media and smartphone usage to predict mental health risks and understand emotional dynamics. These technologies are being applied to detect linguistic patterns and emotional cues associated with mental health issues, offering new avenues for early intervention and suicide prevention. Additionally, the integration of active and passive data from smartphones is proving to be a promising approach for predicting mental disorders in adolescents, showcasing the potential of digital phenotyping in mental health research. On the educational front, studies are exploring the impact of social media on academic performance, revealing a complex relationship where social media can both facilitate and hinder academic success. This body of work underscores the importance of balancing social media use with academic responsibilities and integrating digital tools into educational strategies.

Noteworthy Papers

  • Employing Social Media to Improve Mental Health Outcomes: This research harnesses social media data to detect and predict mental health risks, emphasizing the need for ethical considerations and interdisciplinary collaborations.
  • Deep Learning-Based Feature Fusion for Emotion Analysis and Suicide Risk Differentiation in Chinese Psychological Support Hotlines: Introduces a novel method combining acoustic and deep learning features for emotion analysis, offering insights into emotional dynamics and suicide prevention.
  • Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data: Demonstrates the feasibility of using smartphone data and ML to predict mental health risks in adolescents, highlighting the potential of digital phenotyping.
  • Understanding Mental Health Content on Social Media and Its Effect Towards Suicidal Ideation: Explores the application of ML and DL in analyzing social media data for suicide prevention, advocating for ethical and responsible use of these technologies.

Sources

Is social media hindering or helping Academic Performance? A case study of Walter Sisulu University Buffalo City Campus

Employing Social Media to Improve Mental Health Outcomes

Deep Learning-Based Feature Fusion for Emotion Analysis and Suicide Risk Differentiation in Chinese Psychological Support Hotlines

Digital Phenotyping for Adolescent Mental Health: A Feasibility Study Employing Machine Learning to Predict Mental Health Risk From Active and Passive Smartphone Data

Understanding Mental Health Content on Social Media and Its Effect Towards Suicidal Ideation

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