The study introduces a two‑dimensional framework to audit French news headlines, distinguishing salience framing—captured by four wording devices—from selection framing—captured by outlet‑level story form and high‑charge distributions. Using a 10,000‑headline supervision set annotated by LLMs and human arbitration, the authors classify 902,111 headlines from 25 outlets (2022‑2025) and find that salience and selection diverge yet correlate, that default thresholds inflate salience estimates, and that headlines mentioning Jews, the Far‑right, and Muslims exhibit the highest salience rates. The authors release their dataset, lexicons, and code, claiming it is the largest French headline framing audit to date.
By Amr Sobhy
arXiv:2610.28092v1 Announce Type: cross
Abstract: Persuasive writing exploits cognitive biases, recruiting a reader's mental shortcuts to shape belief, often without the reader noticing. We call the...
By Yousra Fettach, Tijl De Bie
arXiv:2405. 17838v3 Announce Type: replace-cross Abstract: Socio-linguistic indicators of affectively-relevant phenomena, such as emotion or sentiment, are often extracted from text to better understand features of human-computer interactions, including on social media.
By Keith Burghardt, Daniel M. T. Fessler, Chyna Tang, Anne Pisor, Kristina Lerman
The paper presents a quantitative method for analyzing historical change in Italian media by reconstructing a diachronic corpus of about 600,000 articles from "La Repubblica" (1985‑2000). Using NLP to examine lexical and semantic patterns, and tools from complex systems, the authors identify shifts in media discourse, such as Italy’s transition from the First to the Second Republic and major international conflicts like the Gulf and Kosovo Wars. They find that war periods correspond to lower semantic entropy, indicating a more focused discourse.
By Dario Zarcone, Salvatore Miccich\`e, David Sanchez
arXiv:2608.21385v1 Announce Type: cross
Abstract: Social media has become a central arena in which armed conflicts are contested, yet the pro-Israel and pro-Palestine communities on Telegram, whose b...
By Michail Zafeiropoulos, Despoina Antonakaki, Sotiris Ioannidis
The paper presents a system for the Touché 2026 causality extraction challenge, focusing on counter‑causal claims—sentences that appear causal but actually deny causation. It tackles three subtasks: detecting causal sentences, extracting cause and effect spans, and labeling polarity (procausal, counter‑causal, or uncausal). The approach uses a fine‑tuned classifier with a cross‑task rule for detection, an ensemble of three RoBERTa‑large BILOU+CRF taggers for extraction, and counter‑causal data augmentation via a large language model for polarity classification, achieving state‑of‑the‑art scores on the CCNC test set.
By Roham Zendehdel Nobari, Shayan Sooratgar
The paper introduces a controlled inversion test to evaluate whether large language models can reverse known framing transformations in news articles while preserving facts. Using 60 articles and three framing types—evaluative lexis, agency realization, and information salience—the study generates 540 paired variants. Results show high factual preservation (~0.84) but low reversal success (0.044–0.068), indicating that recognizing a framing does not guarantee its undoing.
By Yi Liu
The paper introduces STAR‑Ar, a BERT‑BiLSTM‑CRF model designed for the Daleel 2026 Arabic argument mining shared task. It treats argument discourse unit detection and classification as a token‑level sequence labeling problem, achieving an F1‑score of 72.69 on validation and 73.7 on test data. Analysis shows that models trained only on editorial texts perform worse than those trained on debates, mainly due to the smaller editorial dataset.
By Bhuvanesh Verma, Ali Abusaleh, Alexander Mehler
CMNIE is a new benchmark for extracting structured information from Chinese military news, covering event triggers, arguments, named entities, and entity relations under a unified schema. The dataset contains 13,000 manually annotated instances with 7 event types, 10 argument roles, 7 entity types, and 8 relation types. Experiments show that current supervised models, zero‑shot LLMs, and fine‑tuned LLMs struggle with relation extraction and exact span matching, highlighting the challenge of joint structured extraction in this domain.
By Yan Yu, Mengna Zhu, Zhenyu Song, Hao Yang, Haiwen Chen, Mao Wang
MGAL is a new multilingual benchmark for evaluating long‑context large language models, built from United Nations reports in six official UN languages and covering 8K to 128K tokens. It tests four linguistic granularities—word, sentence, paragraph, and document—while also stratifying examples by their position within the document (begin, middle, end). Experiments show that models excel at word‑level tasks but struggle with coarser granularity, and that closed‑source models outperform others in lower‑resource languages, revealing challenges such as local semantic crowding and a fluency‑consistency gap.
By Chunhan Li, Chenglin Xu, Zongyang Zhang, Jiale Liu, Zhuoxi Rao, Xudong Jia, Junxiu He, Menglin Yang, Wenjuan Gong, Zhengzhe Liu, Chengwei Qin
arXiv:2609.25298v1 Announce Type: new
Abstract: Cultural evaluation coverage and robustness in language models are difficult to diagnose because pretraining corpora and cultural benchmarks are rarely...
By Yusser Al Ghussin, Eva Gavaller, Cristina Espa\~na-Bonet, Josef van Genabith, Simon Ostermann
arXiv:2608. 06589v1 Announce Type: cross Abstract: While large language model outputs are frequently analysed as a collective super variety termed "AI language," this chapter argues that this perspective coexists with distinct, model-specific linguistic signatures akin to human idiolects.
By Karolina Rudnicka, Thomas Stephan Juzek