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@article{207172,
        author = {Geetanjali Narware and Dr. Anish Kumar Choudhary},
        title = {Social Media Addiction Using Machine Learning Technologies},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {2},
        pages = {4133-4139},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207172},
        abstract = {While social media is a vital tool for communication in today's world, its overuse has created a problem that is known as social media addiction. This addiction can have a negative impact on mental health, academic performance, productivity, sleep quality, and social relationships. The traditional approach to detection is largely through surveys and/or psychological testing, which tend to be subjective and time consuming. The study introduces a Machine Learning (ML) based solution to identify the signs of addiction to social media through the analysis of users' behavioral patterns, including screen time, log-in frequency, interaction rate, emotional dependency and checking notifications. The proposed system conducts data preprocessing, feature extraction, model training, and model classification to classify different levels of addiction. The performance of several machine learning algorithms, such as Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), and Artificial Neural Network (ANN) is compared using the measures of accuracy, precision, recall and f1-score. Experimental results show that the accuracy of prediction of the models Random Forest and ANN is higher than other models. The suggested model offers a smart and effective approach to early detection of social media addiction, encouraging healthy digital behaviors and fostering digital well-being for users and researchers.},
        keywords = {Social Media Addiction, Machine Learning, Artificial Intelligence, Classification Algorithms, Behavioral Analysis, Digital Well-being, Predictive Modeling},
        month = {July},
        }