Detecting Miscitation on the Scholarly Web through LLM-Augmented Text-Rich Graph Learning

ArXi:2603.12290v1 Announce Type: cross Scholarly web is a vast network of knowledge connected by citations. However, this system is increasingly compromised by miscitation, where references do not or even contradict the claims they are cited for. Current miscitation detection methods, which primarily rely on semantic similarity or network anomalies, struggle to capture the nuanced relationship between a citation's context and its place in the wider network.