Leveraging GDPR Data Portability: A User-Centric Approach to Social Media Analytics

In the modern world social network platforms collect vast amounts of personal information, often creating fragmented collections that limit user awareness and control. While the General Data Protection Regulation (GDPR) has introduced the Right to Data Portability (Article 20), allowing users to download their personal data in standardized formats such as JSON or CSV, these raw exports remain difficult for the average individual to interpret or utilize effectively. This thesis proposes a user-centric tool designed to analyze personal data obtained from Data Download Packages (DDPs). Moving away from traditional public data crawling, the research focuses on the perspective of data ownership. The framework adopts a modular approach to use different datasets from various platforms such as Meta, Twitter, LinkedIn and to perform different kinds of analysis. The primary objective is to transform raw, unorganized data into valuable insights. Depending on the analytical modules applied, the system can generate different outputs: from topic extraction that reveals user interests, to sentiment analysis that measures the emotional tone of the user’s interactions. By leveraging the legal instrument of data portability, this work aims to enhance user control and enable a deeper understanding of personal data value in the social media ecosystem.