most citedCan Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated News

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cs.CY2026

Interrupting the Chain: Human Perception of AI-Generated Disinformation Through a Kill Chain Lens

Alexander Loth, Martin Kappes, Marc-Oliver Pahl

Generative AI enables customized misinformation at scale, yet defenses remain largely reactive. We present empirical findings from a human-subject study (n=504 participants, n=2,43…

cs.CY20262 cited

Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated News

Alexander Loth, Martin Kappes, Marc-Oliver Pahl

Can humans tell whether a news article was written by a person or a large language model (LLM)? We investigate this question using JudgeGPT, a study platform that independently mea…

cs.CY20261 cited

Eroding the Truth-Default: A Causal Analysis of Human Susceptibility to Foundation Model Hallucinations and Disinformation in the Wild

Alexander Loth, Martin Kappes, Marc-Oliver Pahl

As foundation models (FMs) approach human-level fluency, distinguishing synthetic from organic content has become a key challenge for Trustworthy Web Intelligence. This paper prese…

cs.CY20262 cited

Industrialized Deception: The Collateral Effects of LLM-Generated Misinformation on Digital Ecosystems

Alexander Loth, Martin Kappes, Marc-Oliver Pahl

Generative AI and misinformation research has evolved since our 2024 survey. This paper presents an updated perspective, transitioning from literature review to practical counterme…

cs.CY2026

The Verification Crisis: Expert Perceptions of GenAI Disinformation and the Case for Reproducible Provenance

Alexander Loth, Martin Kappes, Marc-Oliver Pahl

The growth of Generative Artificial Intelligence (GenAI) has shifted disinformation production from manual fabrication to automated, large-scale manipulation. This article presents…