@inproceedings{maggini-etal-2025-detecting,
    title = "Detecting Hyperpartisanship and Rhetorical Bias in Climate Journalism: A Sentence-Level {I}talian Dataset",
    author = "Maggini, Michele Joshua  and
      Bassi, Davide  and
      Gamallo, Pablo",
    editor = "Dutia, Kalyan  and
      Henderson, Peter  and
      Leippold, Markus  and
      Manning, Christoper  and
      Morio, Gaku  and
      Muccione, Veruska  and
      Ni, Jingwei  and
      Schimanski, Tobias  and
      Stammbach, Dominik  and
      Singh, Alok  and
      Su, Alba (Ruiran)  and
      A. Vaghefi, Saeid",
    booktitle = "Proceedings of the 2nd Workshop on Natural Language Processing Meets Climate Change (ClimateNLP 2025)",
    month = jul,
    year = "2025",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/mtsummit-25-ingestion/2025.climatenlp-1.11/",
    doi = "10.18653/v1/2025.climatenlp-1.11",
    pages = "168--187",
    ISBN = "979-8-89176-259-6",
    abstract = "We present the first Italian dataset for joint hyperpartisan and rhetorical bias detection in climate change discourse. The dataset comprises 48 articles (1,010 sentences) from far-right media outlets, annotated at sentence level for both binary hyperpartisan classification and a fine-grained taxonomy of 17 rhetorical biases. Our annotation scheme achieves a Cohen{'}s kappa agreement of 0.63 on the gold test set (173 sentences), demonstrating the complexity and reliability of the task. We conduct extensive analysis revealing significant correlations between hyperpartisan content and specific rhetorical techniques, particularly in climate change, Euroscepticism, and green policy coverage. To the best of our knowledge, we are the first to tackle hyperpartisan detection related to logical fallacies. Indeed, we studied their correlation. Moreover, up to our knowledge no previous work focused on hyperpartisan at sentence level. Our experiments with state-of-the-art language models (GPT-4o-mini) and Italian BERTbase models establish strong baselines for both tasks, while highlighting the challenges in detecting subtle manipulation strategies applied with rhetorical biases. To ensure reproducibility while addressing copyright concerns, we release article URLs, article id and paragraph{'}s number alongside comprehensive annotation guidelines. This resource advances research in cross-lingual propaganda detection and provides insights into the rhetorical strategies employed in Italian climate change discourse. We provide the code and the dataset to reproduce our results: \url{https://anonymous.4open.science/r/Climate_HP-RB-D5EF/README.md}"
}