工业化欺骗：LLM 生成虚假信息对数字生态的连带影响

View PDF HTML (experimental)

Abstract:Generative AI and misinformation research have evolved since our 2024 survey. This paper presents an updated perspective, transitioning from literature review to practical countermeasures. We report on changes in the threat landscape, including improved AI-generated content through Large Language Models (LLMs) and multimodal systems - addressing the adverse impacts of AI on information quality.
This work focuses on our practical contributions: JudgeGPT, a platform to evaluate human perception of AI-generated news, and RogueGPT, a controlled stimulus generation engine for research. Together, these tools form an experimental pipeline for studying how humans perceive and detect AI-generated misinformation.
Our findings show that detection capabilities have improved, but competition between generation and detection continues. We discuss mitigation strategies such as LLM-based detection, inoculation approaches, and the dual-use nature of generative AI.

Comments:	8 pages, 2 figures. Published in the Companion Proceedings of the ACM Web Conference 2026 (WWW Companion '26), pp. 295-302
Subjects:	Computers and Society (cs.CY); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Social and Information Networks (cs.SI)
ACM classes:	I.2.7; K.4.1; K.4.2; H.3.5
Cite as:	arXiv:2601.21963 [cs.CY]
	(or arXiv:2601.21963v3 [cs.CY] for this version)
	https://doi.org/10.48550/arXiv.2601.21963 arXiv-issued DOI via DataCite
Journal reference:	Companion Proceedings of the ACM Web Conference 2026 (WWW '26 Companion), pp. 295-302
Related DOI:	https://doi.org/10.1145/3774905.3795471 DOI(s) linking to related resources

Submission history

From: Alexander Loth [view email]
[v1] Thu, 29 Jan 2026 16:42:22 UTC (26 KB)
[v2] Wed, 11 Feb 2026 14:35:21 UTC (26 KB)
[v3] Thu, 29 Oct 2026 17:03:03 UTC (28 KB)