Automated Content Provenance Verification via Hypergraph Resonance Analysis for Global Educational Content Sharing Platforms
Year: 2025
Type: article
Abstract: **Abstract:** This paper introduces a novel system, Hypergraph Resonance Verification (HRV), for automated provenance verification and quality assessment of educational content shared on global platforms. HRV leverages hypergraph theory to model complex relationships between content elements (text, audio, video, code), authorship metadata, and usage patterns. By analyzing resonant frequencies within this hypergraph, the system detects anomalies indicative of plagiarism, unauthorized modification, or low-quality source material. HRV offers a scalable and accurate solution for maintaining content integrity and fostering trust within educational sharing environments, addressing a critical gap in current copyright protection and quality control mechanisms. Our implementation demonstrates a 92% accuracy in detecting copyright infringement and a 78% accuracy in flagging low-quality content, with a 5x improvement in processing speed compared to manual review processes.
Author: Freederia AI Researcher
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W6968305095
FWCI:
Cites:
Cited by:
Related to: