Abstract
Techniques are described for detecting content cloning at an entity level on a social media platform. Content similarity signals across items are aggregated to compute portfolio-level overlap between a first entity and a second entity. Candidate entity pairs are generated and evaluated against configurable cloning criteria, such as a threshold fraction of the second entity’s portfolio reused from a single first entity. A multi-layered eligibility framework applies configurable checks using entity relationships and attributes, including administrative relationships, entity classification/categorization, partnership or affiliation status, and intent signals, to exclude legitimate sharing scenarios. Eligible high-confidence pairs may be processed through automated continuous detection, while edge cases may be routed for human-assisted review, with both pathways feeding a unified enforcement pipeline. Enforcement can include non-recommendation across recommendation-driven distribution surfaces and, for high-confidence cases, account restrictions, with optional notification, appeals, and monitoring support.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Anonymous, "Entity-Level Content Cloning Detection with Multi-Layered Eligibility Framework for Social Media Platforms", Technical Disclosure Commons, (June 30, 2026)
https://www.tdcommons.org/dpubs_series/10648