The paper introduces the Persuasion Index (PI), a taxonomy of 15 persuasion dimensions grounded in psychological and communication theories, implemented with 55 lexicon- and rule-based sub-features. PI is modular, allowing individual detectors to be swapped while preserving its theoretical framework. Evaluations on four English argumentative datasets show that PI provides a shared, lightweight feature space that captures meaningful predictive signals and reveals consistent dimension-level associations with persuasion outcomes, with variations across topics and stances.
By Liancheng Gong, Zhiyang Wang, Yiwei Xu, Julia Mendelsohn
arXiv:2610.09464v1 Announce Type: new
Abstract: Political discourse often uses rhetorical and persuasive language to frame narratives, influence public opinion, and mobilize audiences. While Bangla n...
By Rohit Kumar Sen, Anik Chowdhury
The paper introduces UkrMeme, a dataset of 200 Ukrainian wartime memes annotated for persuasive techniques, and PTM-UA, a Ukrainian translation of a subset of the Propaganda 950 benchmark that retains the original visual content. The authors evaluate vision‑language models in a zero‑shot prompting setup across image, text, and multimodal inputs using hierarchical F1 metrics. They also compare Layer‑wise Relevance Propagation and model‑generated token attributions to analyze the semantic categories of evidence emphasized by each explanation method.
By Nazar Andrushko, Vera Schmitt, Veronika Solopova
The paper introduces “Persuasio”, a multi‑agent dialogue platform that uses a formal argumentation theory to adjudicate winners in free‑text debates. Using this system, the authors generated 192 debates on a UK political topic involving humans and large language models (LLMs), and evaluated 22 interlocutors through automated adjudication and 9,702 crowdsourced pairwise judgments across 1,386 annotation instances. The results show a consistent decoupling between subjective persuasiveness—where LLMs dominate—and formal argumentative strength—where humans remain competitive, with multi‑agent and retrieval‑augmented variants widening this gap.
By Jordan Robinson, Angus R. Williams, Katie Atkinson, Anthony G. Cohn
The study examines how language influences AI chatbot responses to questions about the war in Ukraine, revealing that the same AI systems (GPT, Claude, Gemini) produce varying political stances across 112 languages. By evaluating 20 statements in 112 languages, the researchers found that Russia‑leaning versus Ukraine‑leaning answers differ by language, mirroring global political attitudes such as public support for Russia, UN voting patterns, and aid levels. This pattern persists across all three models and even when specific statement pairs are removed, suggesting that information warfare could embed geopolitical biases into AI training data.
By Maxim Chupilkin
arXiv:2608. 15746v1 Announce Type: new Abstract: We present a forensic analysis of the generation pipeline behind a recent AI-driven influence campaign.
By Benjamin Icard, Elouan Vuichard, Louis Lefebvre, Lila Sainero, Thomas Girault, Alice Breton, Tanguy Launay, Gauvain Bourgne, Morgane Casanova, Guillaume Gadek, Victor Kl\"otzer, Michel Le Nouy, Guillaume Gravier, Jean-Gabriel Ganascia, Paul \'Egr\'e
The paper introduces SWARM, a multilingual dataset of 2,183 search engine results in nine languages, annotated for support of Russian propaganda narratives. It evaluates a source-based blocklist, supervised classifiers, and zero‑shot large language models, finding that blocklists miss most propaganda and that content‑level models vary in performance, with the best LLM achieving an F1 of 0.73. The study highlights the need for per‑language, content‑level detection of search‑borne propaganda.
By Manuel Tonneau, Abhinav Dubey, Farhan Shaikh, Ilaria Vitulano, Martha Stolze, Hale Dedeoglu, Clara Riechert, Ella Kuka, Maryna Sydorova, Mykola Makhortykh, Elizaveta Kuznetsova
arXiv:2510.13387v3 Announce Type: replace
Abstract: Large language models (LLMs) still struggle with strategic persuasion, largely because existing approaches either neglect information asymmetry or...
By Buwei He, Yang Liu, Zhaowei Zhang, Zixia Jia, Yang Yu, Huijia Wu, Zhaofeng He, Zilong Zheng, Yipeng Kang
Enthymemes, arguments with unstated premises or conclusions, are pervasive in persuasive discourse, yet their annotation remains notoriously subjective. We present a resource of 1,482 tweets from politically controversial discourse, annotated by five annotators for the presence of enthymemes and their argument structure, designed to study label variation.
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
arXiv:2606.12186v2 Announce Type: replace
Abstract: Enthymemes, arguments with unstated premises or conclusions, are pervasive in persuasive discourse, yet their annotation remains notoriously subjec...
By Martial Pastor, Nelleke Oostdijk
Propaganda detection is an essential task in natural language processing (NLP), particularly in the context of manipulative political communications. However, identifying specific propaganda technique...