International Journal of Advanced Research in Computer and Communication Engineering

Abstract: The proliferation of social media platforms has raised significant concerns regarding its impact on the mental health of students. Since the mid-2010s, research has consistently indicated a correlation between high daily screen time and an increase in adverse mental health outcomes, such as anxiety, depression, and psychological distress in adolescents. These challenges often stem from mechanisms like passive content consumption and upward social comparison, which can trigger envy and depressive symptoms. The consequences are significant, negatively affecting academic performance, sleep quality, and overall well-being, sometimes escalating to severe psychological distress, including thoughts of self-harm. In response, the field has increasingly adopted machine learning to analyse large-scale digital data for early risk detection. Addressing this need, our project proposes a deep learning framework to proactively identify students at risk. Following a multimodal approach that fuses self-reported and behavioural data, a neural network model was developed. It was trained on a comprehensive dataset comprising thousands of anonymized entries from student surveys and their social media activity metrics to classify mental health status. In performance evaluations, the proposed model achieved a classification accuracy exceeding 85%, a result consistent with state-of-the-art benchmarks for similar tasks that report accuracies and precision metrics in the 85-90% range. The findings validate the efficacy of using artificial intelligence as a scalable, non-invasive screening tool within educational institutions. This approach supports the implementation of ethically-grounded early warning systems that can connect at-risk students with crucial support services. Ultimately, this work demonstrates the potential of technology to mitigate the negative psychological effects of social media and foster a healthier, more supportive environment for students.

Keywords: Social media, Mental health, Students, Deep learning, Machine learning, Neural networks, Data analysis, Prediction model, Stress detection, Online impact

Downloads: | DOI: 10.17148/IJARCCE.2025.14905