Tripartite Coupling of Content, Dissemination, and Cognition: A Collaborative Detection Framework for AI-Generated Fake News in the Era of Generative AI

DOI

10.2991/978-94-6463-823-3_14How to use a DOI?

Keywords

Deep Learning; Generative Artificial Intelligence; Dynamic Coupling Mechanism

Abstract

With the groundbreaking development of generative artificial intelligence technologies, fake news has evolved from single-modal textual distortion in the traditional media era to multimodal, cross-platform information pollution, posing systemic threats to the trust architecture and information ecosystem of digital society. This study systematically reviews the definitional evolution of fake news and its core characteristics across distinct technological epochs (traditional media, social media, and generative artificial intelligence), revealing the technical challenges posed by generative AI-driven fake news in terms of multimodal authenticity enhancement, dynamic adversarial evolution, and cross-platform camouflage escalation. Addressing the prevailing issues of single-dimensional fragmentation, mechanistic fusion, and dynamic feedback deficiency in contemporary fake news detection research, this work critically examines the bottlenecks in content verification, dissemination modeling, and cognitive quantification techniques. Building upon this analysis, we propose an innovative tripartite analytical framework encompassing “content-dissemination-cognition” dimensions. This framework integrates causal inference, temporal heterogeneous graph networks, and cognitive bias indices, achieving multidimensional collaborative defense through a differential equation-driven dynamic coupling mechanism. The proposed paradigm provides both theoretical foundations and methodological scaffolding for resolving fake news detection challenges in techno-social coupled environments.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xuanyi Li
PY  - 2025
DA  - 2025/08/31
TI  - Tripartite Coupling of Content, Dissemination, and Cognition: A Collaborative Detection Framework for AI-Generated Fake News in the Era of Generative AI
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 148
EP  - 164
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_14
DO  - 10.2991/978-94-6463-823-3_14
ID  - Li2025
ER  -