@inproceedings{gargova-etal-2026-multilingual,
    title = "A Multilingual Linguistic Analysis of Human vs {LLM}-Generated News in a Disinformation Context",
    author = "Gargova, Silvia  and
      Perez-Montero, Alba  and
      Lloret Pastor, Elena  and
      Moreda Pozo, Paloma",
    editor = "Frenda, Simona  and
      Stranisci, Marco Antonio  and
      Ashraf, Shaina  and
      Ren, Ada  and
      Konstas, Ioannis  and
      Naseem, Usman",
    booktitle = "Proceedings of the 1st Workshop on Information Disorder ({I}n{D}or) @ {LREC} 2026",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/ingest-nlpsi/2026.indor-1.9/",
    doi = "10.63317/345wj5pp9nkh",
    pages = "85--96",
    ISBN = "978-2-493814-87-6",
    abstract = "The rise of Large Language Models has shifted the Information Disorder landscape toward automated threats. This study investigates the linguistic construction of synthetic news by comparing GPT-5, Gemini 2.5, and Grok 4 across English, Spanish, and Bulgarian. Using multilingual human-authored verified news and disinformation as seeds, we analyze how prompt informativeness and model architecture influence deceptive content production. Our methodology employs five metrics: semantic similarity, factual consistency, readability, lexical richness, and persuasion technique frequency. Our analysis reveals that while prompt scarcity leads to informational loss, LLMs maintain a homogenized stylistic template regardless of input length. Unlike human authors, who intensify rhetorical and emotional markers to drive deceptive intent, LLMs adhere to a neutral register. This study identifies distinct statistical patterns in generated content characterized by hyper-standardized readability and high lexical density (p {\ensuremath{<}} 0.001). These features serve as robust ``LLM signatures'', enabling a classification accuracy of 96{\%} across English, Spanish, and Bulgarian. These findings suggest that generated disinformation relies on invariant syntactic structures rather than nuanced human rhetoric, providing a framework for detection tools centered on structural patterns rather than content veracity."
}