ProBel is a bilingual Arabic and English resource for propaganda detection that aligns binary labels, multi-label annotations for 23 propaganda techniques grouped into six categories, technique-labeled spans, and reference explanations for news sentences. The dataset supports matched binary, coarse-grained, multi-label, and span-level tasks in both languages, and the authors evaluate zero‑shot prompting, task‑specific fine‑tuning, and joint training. A single bilingual multi‑task model achieves the best overall performance, with cross‑task analysis revealing that joint classification preserves binary performance while span‑only training can weaken sentence‑level prediction, and that joint bilingual training yields the most stable results.
By Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Elisa Sartori, Giovanni Da San Martino, Firoj Alam
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 explores how large language models can detect hidden narratives in social messages without training data. By feeding the models human-written narrative descriptions, performance improves markedly, while automatically generated descriptions or few-shot examples can hurt accuracy. Ensemble techniques, especially majority voting, further boost robustness, and larger models show the best results with less sensitivity to prompts.
By Jes\'us M. Fraile-Hern\'andez, Anselmo Pe\~nas, Patrick Giedemann
MIL-BERT is a neural network algorithm that classifies large texts by selecting relevant excerpts, inspired by multiple instance learning. It scales to samples with nearly 1 million tokens and has been evaluated on seven datasets, achieving state‑of‑the‑art results on three long‑text tasks such as political bias detection, trigger warning identification, and author demographic inference. The model also generalizes from weakly‑labeled text bags to accurately classify smaller instances.
By John Cadigan, Dayne Freitag, Eric Yeh
The paper reports a comprehensive study of transformer-based NLP models for detecting check-worthy social media posts, covering data collection, preprocessing, architecture selection, fine‑tuning, testing, and implementation. It focuses on multilingual models that can process English and low‑resource languages such as Arabic, Bulgarian, Dutch, Polish, Czech, and Slovak, and compares their performance to state‑of‑the‑art baselines. The work introduces multi‑label multilingual classifiers that simultaneously identify harmful content and posts containing verifiable factual claims efficiently.
By Sebastian Kula
arXiv:2607. 20463v1 Announce Type: new Abstract: This paper presents an AI-driven browser extension that identifies clickbait to help users avoid misleading Internet articles.
By Wojciech Michaluk, Tymoteusz Urban, Mateusz Kubita, Soveatin Kuntur, Anna Wr\'oblewska