Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated News investigates whether people can distinguish news articles written by humans from those generated by large language models (LLMs). Using JudgeGPT, the study collected 2,318 judgments from 1,054 participants on content from six LLMs, finding that participants could not reliably differentiate machine-generated from human-written text. The research also shows that domain expertise predicts judgment accuracy, distinct response strategies exist, and accuracy declines after about 30 evaluations due to cognitive fatigue.
By Alexander Loth, Martin Kappes, Marc-Oliver Pahl
arXiv:2610.15961v1 Announce Type: cross
Abstract: Emergent misalignment (EM) occurs when fine-tuning a language model to a narrow task makes it misaligned on unrelated ones. Because organizations now...
By Alex Mart\'i Guiu, Nicolas Zumarraga, Kevin O'Sullivan, Robert Jakob, Anna Hedstr\"om
arXiv:2607. 20487v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to answer questions about political information, including in election-adjacent information settings where factual errors and ideological distortions are high-stakes.
By Chendi Wang, Liam Cunningham, Tom Yishay, Jieying Chen
arXiv:2610.03077v1 Announce Type: new
Abstract: Propaganda detection is an essential task in natural language processing (NLP), particularly in the context of manipulative political communications. H...
By Claudiu Creanga, Ioachim Lihor, Liviu P. Dinu
Propaganda detection is an essential task in natural language processing (NLP), particularly in the context of manipulative political communications. However, identifying specific propaganda technique...
arXiv:2608.21389v1 Announce Type: cross
Abstract: Generative AI enables customized misinformation at scale, yet defenses remain largely reactive. We present empirical findings from a human-subject st...
By Alexander Loth, Martin Kappes, Marc-Oliver Pahl
The paper presents a white‑box deception detection system that uses probes trained on the largest deception dataset (FIBS) and a novel architecture aggregating across layers and tokens. The probes achieve 98.8% AUC on SHADE‑Arena, outperforming a text‑monitoring baseline, and reach up to 99.7% AUC when detecting introspective deception that cannot be inferred from context alone. They also effectively detect deception in open‑weight models on politically sensitive topics and when models are pressured about their beliefs.
By Oskar J. Hollinsworth, Alex F. Spies, Tigist Diriba, Adam Gleave, Chris Cundy
FakeSpotter is a new tool that estimates the viral misinformation risk of textual content by measuring structural fingerprints of misinformation instead of directly judging truthfulness. It operates across linguistic, narrative, logical, and critical‑thinking dimensions, using repeated large language model assessments and domain‑specific logistic regression classifiers for both short and long texts. In a labeled corpus of 764 texts, FakeSpotter achieved macro F1 scores of 0.788 for short texts and 0.793 for long texts, and its interpretive layer offers explainable outputs such as feature‑based scores, signal agreement, and a caution index for social listening.
By Giovanni Spitale, Federico Germani
The paper presents a theory-informed computational framework that converts cross-disciplinary theories of fake news into measurable features for automated detection and explanation. By reviewing theories from social sciences, psychology, economics, and more, the authors establish a broad theoretical foundation for computational modeling. Experiments on benchmark datasets demonstrate that theory-derived features are predictive, provide interpretable diagnostic signals, and that multi-feature models generally outperform individual features, though gains are modest.
By Zhaoyang Cao, Miriam Metzger, Reza Zafarani
arXiv:2610.12445v2 Announce Type: replace-cross
Abstract: Recent incidents have highlighted the challenge of monitoring LLM agents and the danger of models deceiving people. We show that white-box de...
By Oskar J. Hollinsworth, Alex F. Spies, Tigist Diriba, Adam Gleave, Chris Cundy
The study evaluates large language models (LLMs) that generate political networks by comparing them to survey-reported partisan exposure from the ANES 2020–2022 Social Media Study. It finds that while network structure and political assortativity vary across model configurations, many models either over‑separate Democratic and Republican egos or underestimate Republican exposure, leading to group‑specific errors in local partisan environments. These discrepancies persist even among networks with similar aggregate properties, indicating that aggregate metrics alone can mask important individual‑level inaccuracies.
By Marco Tonin, Michele Tizzoni, Bruno Lepri, Luca Maria Aiello
arXiv:2601. 05050v3 Announce Type: replace Abstract: Large language models (LLMs) have been shown to be persuasive across a variety of contexts.
By Thomas H. Costello, Kellin Pelrine, Matthew Kowal, Jasper Timm, Antonio A. Arechar, Jean-Fran\c{c}ois Godbout, Adam Gleave, David Rand, Gordon Pennycook