A Hybrid Model for Identifying Social Media Addiction Patterns Among Young People

Social media addiction is a growing concern among young people, with numerous negative impacts on mental health and well-being. In this study, our goal was to develop a hybrid machine learning model for spotting young people's patterns of social media addiction. To evaluate the hybrid model's accuracy in relation to the other two models, recurrent neural network-RNN and long short-term memory -LSTM models were used. Our findings show that the hybrid model has a precision of 67.55%, which is more than the accuracy of the RNN model-59.77
^%
and the LSTM model-65.42 %. These results suggest that the hybrid model can be more effective in identifying social media addiction patterns in young people.