Privacy Concerns in Digital Social Science Research: Ethical, Methodological, and Regulatory Transformations

Abstract

The proliferation of digital data sources has turned research into social science, while increasing privacy risks, such as re-identification, monitoring and informed consent reduction. This review discusses how privacy issues have changed research practice, drawing on Nissenbaum’s theory of situational integrity, data justice framework, and changing ideas about informed consent. Privacy risk has promoted changes: from open data system to controlled access, dynamic consent mode, algorithm protection technology and interdisciplinary governance structure. Special privacy protection technologies, such as differential privacy, federated learning and synthetic data generation, can continue to be studied while reducing risks. Administrative data linking, social media research and the application of qualitative digital methods all show the progress and still existing limitations in protection. Re-identification of attacks, fragmentation of rules, power imbalance between researchers and platforms, and potential cooling effect on research are still important. The analysis focuses on the data trust of community management, the standardized ethical agreement of artificial intelligence, and the global interoperability of privacy standards. The conclusion is that good privacy protection is not only a restriction, but also a necessary condition for a reliable and sustainable digital social science, when it is combined with reflection and context-sensitive governance.