Provenance of data with rights and interests in online rumor data element circulation on social media

Jianbo Zhao (), Huailiang Liu (), Kai Shu, Qisen Fang, Chufan Tan, Yue Su, Lianyue Wu, Peijie Liu, Hai Shen and Jing Tian
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Jianbo Zhao: Xi’an International Studies University
Huailiang Liu: Xidian University
Kai Shu: Xidian University
Qisen Fang: Xidian University
Chufan Tan: Xidian University
Yue Su: Xidian University
Lianyue Wu: Xidian University
Peijie Liu: Xidian University
Hai Shen: Xi’an International Studies University
Jing Tian: Xi’an International Studies University

Palgrave Communications, 2025, vol. 12, issue 1, 1-22

Abstract: Abstract Social media platforms, as the primary carriers of online rumor dissemination, enable users to gain profits from the platform through activities such as content creation, browsing, and sharing. However, the complexity of data rights and the attribution of responsibility hinders the comprehensive tracing of rumor propagation paths and the precise identification of data infringement subjects. By reusing 92 circulation processes from 13 data lifecycle models, this paper abstracts the circulation process of online rumor data elements, standardizes the “five rights separation” framework for data rights confirmation among ternary data subjects, and defines a Rights-and-Interests-Attributed Data Element. Through integration with PROV-O and ProVOC models, this paper constructs PROV-OCC—an ontological model for data with rights and interests provenance in rumor circulation—comprising 3 parent classes and 32 object properties. It implements a seven-element semantic representation combining W7 provenance technology and validates the model through ontological reasoning via knowledge graph representation of typical rumor cases, verifying its effectiveness in tracing data rights changes, infringement subjects, and propagation paths. The data provenance model supports the recovery and compensation of infringement-related profits, enabling the timely restoration of compromised trust and order for governments and platforms.

Date: 2025
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DOI: 10.1057/s41599-025-05437-z

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