The paper introduces Reader-Centric Misleadingness Understanding (RCMN), a framework that defines misleadingness in public discourse through five dimensions: misleading mechanism, likely reader interpretation, evidence-warranted interpretation, emotional arousal, and communicative intent. Using this framework, the authors build an evidence-grounded dataset and find that misleadingness is diverse, extending beyond fabrication to include unsupported inference, exaggeration, and omission, often linked to heightened emotional arousal and distortive intent. They also evaluate whether lightweight claim-and-context representations can capture reader-level interpretations, showing that while such representations can recover interpretations, they struggle to identify the underlying misleading mechanisms, underscoring the need for richer contextual and evidential data.
By Peiling Yi
arXiv:2609.38256v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly used to answer questions about politically contentious issues, yet evaluations typically treat a model's...
By Olivia Macmillan-Scott, Michael Jacobs, Nils Metternich, Mirco Musolesi
arXiv:2606. 10852v1 Announce Type: cross Abstract: LLM deception is often evaluated through direct markers such as fabricated claims, explicit lies, or strategic concealment.
By Polydoros Giannouris, Mohsinul Kabir, Sophia Ananiadou
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
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
The study compares communication styles of large language models (LLMs) and humans in explaining COVID‑19 misinformation, using a dataset of 1,498 fact‑checking claims and 99 blinded reader evaluations. LLM‑generated explanations scored lower on persuasive strategies, certainty, and alignment with social values, yet over 60% of participants preferred LLM content for clarity, completeness, and persuasiveness. The findings suggest that reader preference may not align with traditional measures of communication quality, highlighting both the promise and limits of LLMs in health communication.
By Jiawei Zhou, Kritika Venkatachalam, Minje Choi, Koustuv Saha, Munmun De Choudhury