Data collected directly from users is essential for the research, development, and personalization of social media platforms. However, this dependence on data collection can result in significant privacy breaches, exposing users to various risks and threats linked to data sharing. The primary challenge addressed in this research is finding a balance between data monetization and user privacy, which is crucial as social media platforms strive to enhance personalization and engagement while protecting user information. To tackle this issue, we categorize social media platforms according to their shared usage and services, identifying the specific balance required for each category based on usage patterns and modern privacy risks. We create a framework that aligns these balance needs with the integration of efforts from regulations, conventional frameworks, and Privacy-Enhancing Technologies (PETs) customized for each platform category. This paper aims to present five types of social media platforms and illustrate how stakeholders within each platform can utilize various technologies, existing frameworks, and regulatory measures to effectively manage the associated trade-offs.