Information Sensitivity in Data Classification and Internet Governance

Information sensitivity refers to the degree of perceived risk associated with the collection, processing, and use of user data by information systems. The topic has attracted scholarly attention in recent years within fields such as computer science, information systems, internet information studies, and digital governance. This study identifies 60 information items disclosed by users in big-data and AI-driven online environments and classifies them based on their sensitivity. The information categories are clustered into five groups: less sensitive social attributes; consumption traces; individual behavioral details; potential risk information; and health and social sensitivity. Furthermore, the analysis examines temporal and spatial variations in information sensitivity and how they evolve over time. It explores the use of information sensitivity as an indicator of the alignment between users and cyberspace, thus reflecting the performance of internet governance across different regions, cultures, and platforms.