The role of social media in shaping visual culture and identity using machine learning methods with featuring engineering

This research investigates the impact of social media in visual culture and identity using three machine learning models which are logistic regression, artificial neural network (ANN), and support vector machine (SVM). Using accuracy, precision, and recall as criteria for categorization, these models were made to analyze the effect of social media on identity formation for individuals and groups with regard to visual cultural trends. The results indicate that the ANN is the best model as it achieved the highest accuracy (0.94), precision (0.91), and recall (0.90), meaning that the model has strong potential to capture the complexity of patterns presenting in visual information as influenced by social media. Contrary, the logistic regression presents the modest performance (accuracy (0.93), precision (0.90), and recall (0.88)) providing reliable insights with interpretability. In contrast, the SVM, registered the least performance (accuracy (0.93), precision (0.87), and recall (0.85)) showing its limitation in pattern deciphering in complex data though such a model still provides advantages in computation efficiency and broader applicability. This research may apply ANN in the analysis of intricate patterns that will describe cultural trends of social media and their impact on visual identity. Such investments in ANN-equipped and well-informed researchers would substantially build the capacity for monitoring.