Double‐Edged Sword of Social Media Algorithms: Assessing the Risks to University Cybersecurity and Student Data Privacy

ABSTRACT As universities undergo accelerated digital transformation, social media algorithms—while streamlining campus services—have emerged as a pivotal yet under examined source of student privacy vulnerabilities and cyber‐attack exposure. The opaque, recommendation‐driven “black box” mechanisms of these platforms incentivize users to over‐disclose sensitive personal information, while the aggregation of heterogeneous behavioral data across platforms intensifies risks of cross‐platform re‐identification and comprehensive user profiling. This process catalyzes a latent but systemic risk transmission chain: algorithm‐induced disclosure → multi‐source behavioral fusion → targeted exploitation . To accurately assess and block this chain, this paper constructs a privacy risk assessment mechanism for universities to evaluate the risks to university network security and student data privacy. First, the platform's digital footprint is structured into a 128‐dimensional feature vector; second, a weighted linear risk scoring function is designed based on the “Personal Information Protection Law of the People's Republic of China” and weighting; finally, based on high‐confidence association rules, the Drools engine is embedded to achieve dynamic early warning. Empirical results show that this mechanism can effectively identify high‐risk individuals (the top 18.01% covers 81.19% of leakage events) and reveals patterns such as a 267.1% surge in high‐risk behaviors among second‐year students during exam weeks. This study makes the implicit transmission chain of social media algorithms explicit, providing a feasible technical path and decision support for universities to build a proactive, time‐based, and hierarchical privacy protection system.