Detecting Hyperpartisanship and Rhetorical Bias in Climate Journalism: A Sentence-Level Italian Dataset

We present the first Italian dataset for joint hyperpartisan and rhetorical bias detection in climate change discourse, enhancing the complexity in modeling hyperpartisan detection.Our annotation scheme achieves a Cohen's kappa agreement of 0.63 on the gold test set (173 sentences).The dataset comprises 48 articles (1,010 sentences) from far-right media, annotated at sentence level for both binary hyperpartisan classification and the multi-label classification of 17 rhetorical biases.We conduct extensive text analysis revealing significant correlations between hyperpartisan content and specific rhetorical techniques.Our experiments with state-of-the-art language models (GPT-4o-mini) and Italian BERTbase models establish strong baselines for both classification tasks.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 crosslingual hyperpartisan detection and provides insights into the rhetorical strategies employed in Italian climate change discourse.To the best of our knowledge, we are the first to tackle hyperpartisan detection related to logical fallacies, focusing on on the sentence level.