@inproceedings{perez-montero-etal-2026-pragmatic,
title = "Pragmatic Profiling for Disinformation Detection: An Exploratory Analysis of Stylistic Features in {S}panish News",
author = "Perez-Montero, Alba and
Mir{\'o} Maestre, Mar{\'i}a and
Lloret, Elena and
Moreda, Paloma",
editor = "Mitkov, Ruslan and
Mu{\~n}oz, Rafael and
Lloret, Elena and
Ranasinghe, Tharindu and
Estevanell-Valladares, Ernesto L. and
Lamsiyah, Salima and
Montoyo, Andr{\'e}s and
Ezzini, Saad",
booktitle = "Proceedings of the Second International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security",
month = jun,
year = "2026",
address = "Alicante, Spain",
publisher = "Department of Languages and Information Systems, University of Alicante",
url = "https://aclanthology.org/2026.nlpaics-1.3/",
pages = "25--35",
abstract = "The spread of misleading and fabricated information has made automatic disinformation detection a central challenge for Natural Language Processing. While most approaches have focused on lexical, syntactic, or semantic cues, deceptive discourse is also shaped by pragmatic choices that reflect how information is framed, qualified, and directed toward readers. This paper investigates whether pragmatic information can improve disinformation detection in Spanish by explicitly incorporating two annotation layers: communicative intentions and subjectivity markers. We compare the impact of these pragmatic features across three modeling paradigms: traditional classifiers, an encoder-based transformer (RoBERTa), and generative language models (GPT-oss and Mistral-small). Our results show that pragmatic augmentation consistently improves over text-only baselines, with subjectivity markers displaying stronger discriminative power than intention labels. Statistical testing further confirms that the observed gains are robust for the generative models evaluated. These findings support the view that authorial stance and communicative purpose provide useful complementary evidence for veracity classification."
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<abstract>The spread of misleading and fabricated information has made automatic disinformation detection a central challenge for Natural Language Processing. While most approaches have focused on lexical, syntactic, or semantic cues, deceptive discourse is also shaped by pragmatic choices that reflect how information is framed, qualified, and directed toward readers. This paper investigates whether pragmatic information can improve disinformation detection in Spanish by explicitly incorporating two annotation layers: communicative intentions and subjectivity markers. We compare the impact of these pragmatic features across three modeling paradigms: traditional classifiers, an encoder-based transformer (RoBERTa), and generative language models (GPT-oss and Mistral-small). Our results show that pragmatic augmentation consistently improves over text-only baselines, with subjectivity markers displaying stronger discriminative power than intention labels. Statistical testing further confirms that the observed gains are robust for the generative models evaluated. These findings support the view that authorial stance and communicative purpose provide useful complementary evidence for veracity classification.</abstract>
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%0 Conference Proceedings
%T Pragmatic Profiling for Disinformation Detection: An Exploratory Analysis of Stylistic Features in Spanish News
%A Perez-Montero, Alba
%A Miró Maestre, María
%A Lloret, Elena
%A Moreda, Paloma
%Y Mitkov, Ruslan
%Y Muñoz, Rafael
%Y Lloret, Elena
%Y Ranasinghe, Tharindu
%Y Estevanell-Valladares, Ernesto L.
%Y Lamsiyah, Salima
%Y Montoyo, Andrés
%Y Ezzini, Saad
%S Proceedings of the Second International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security
%D 2026
%8 June
%I Department of Languages and Information Systems, University of Alicante
%C Alicante, Spain
%F perez-montero-etal-2026-pragmatic
%X The spread of misleading and fabricated information has made automatic disinformation detection a central challenge for Natural Language Processing. While most approaches have focused on lexical, syntactic, or semantic cues, deceptive discourse is also shaped by pragmatic choices that reflect how information is framed, qualified, and directed toward readers. This paper investigates whether pragmatic information can improve disinformation detection in Spanish by explicitly incorporating two annotation layers: communicative intentions and subjectivity markers. We compare the impact of these pragmatic features across three modeling paradigms: traditional classifiers, an encoder-based transformer (RoBERTa), and generative language models (GPT-oss and Mistral-small). Our results show that pragmatic augmentation consistently improves over text-only baselines, with subjectivity markers displaying stronger discriminative power than intention labels. Statistical testing further confirms that the observed gains are robust for the generative models evaluated. These findings support the view that authorial stance and communicative purpose provide useful complementary evidence for veracity classification.
%U https://aclanthology.org/2026.nlpaics-1.3/
%P 25-35
Markdown (Informal)
[Pragmatic Profiling for Disinformation Detection: An Exploratory Analysis of Stylistic Features in Spanish News](https://aclanthology.org/2026.nlpaics-1.3/) (Perez-Montero et al., NLPAICS 2026)
ACL