Adaptive privacy permissions for social media web

In the era of digital communication, ensuring user privacy on social media platforms is essential. A major challenge is aligning platform privacy settings with individual user preferences without overwhelming them with too many questions. This research presents a methodology for designing adaptive privacy preferences on social media sites, aimed at minimizing user burden while accurately capturing their privacy needs. The methodology involves four steps: clustering users based on their responses, classifying them into specific groups, selecting the most important questions, and implementing the tailored settings. By grouping users according to their privacy concerns, we gain insights into their preferences, which help in customizing their privacy settings. We applied various classification techniques, including random forest, gradient boosting, AdaBoost, SVM, and k-NN, with random forest showing the highest accuracy in assigning users to appropriate clusters. The selection of questions is optimized using feature importance analysis, ensuring that only the most critical questions are asked, which reduces user fatigue while maintaining accuracy. This research is significant because it offers practical, personalized privacy controls that can enhance user satisfaction and privacy protection on social media platforms. By reducing the number of questions and tailoring settings to specific user groups, our methodology improves the overall user experience and privacy management.