By: Prof. Zuhoor Al-Khanjri and Al-Shaima Al-Nassri

Department of Computer Science, College of Science 

Social media addiction has become a significant concern, contributing to various health issues such as sleep deprivation, anxiety, and reduced immunity. Current detection methods are predominantly manual and rely on self-reports, which can be biased and often lead to delayed intervention.

The AI-powered system employs advanced machine learning algorithms, including Random Forests, Logistic Regression, and Reinforcement Learning, to analyse user interaction patterns, content engagement, and time spent on social media. By leveraging a diverse dataset collected through surveys and behavioural observations, it identifies hidden patterns of addiction.

The results demonstrate that our model can accurately detect early signs of addiction. The system analyses user behaviour in real time and provides alerts alongside personalised recovery recommendations tailored to individual user needs.

We propose implementing this system on a larger scale to promote healthier digital usage and enable timely interventions for individuals at risk of social media addiction. By harnessing AI technology, we can raise awareness and offer better support for users struggling with social media dependency.