Detecting hyperpartisan narratives and Population Replacement Conspiracy Theories (PRCT) is essential to addressing the spread of misinformation. These complex narratives pose a significant threat, as hyperpartisanship drives political polarisation and institutional distrust, while PRCTs directly motivate real-world extremist violence, making their identification critical for social cohesion and public safety. However, existing resources are scarce, predominantly English-centric, and often analyse hyperpartisanship, stance, and rhetorical bias in isolation rather than as interrelated aspects of political discourse. To bridge this gap, we introduce PartisanLens, the first multilingual dataset of 1617 hyperpartisan news headlines in Spanish, Italian, and Portuguese, annotated in multiple political discourse aspects. We first evaluate the classification performance of widely used Large Language Models (LLMs) on this dataset, establishing robust baselines for the classification of hyperpartisan and PRCT narratives. In addition, we assess the viability of using LLMs as automatic annotators for this task, analysing their ability to approximate human annotation. Results highlight both their potential and current limitations. Next, moving beyond standard judgments, we explore whether LLMs can emulate human annotation patterns by conditioning them on socio-economic and ideological profiles that simulate annotator perspectives. At last, we provide our resources and evaluation; PartisanLens supports future research on detecting partisan and conspiratorial narratives in European contexts.

The proliferation of conspiracy theories and hateful messages on social media poses significant challenges for content moderation and public discourse. Despite their societal impact, existing datasets for automated conspiracy detection remain limited in scope and language coverage. We present a multilingual dataset of conspiracy content on Telegram comprising 5750 messages across English, Dutch, Italian, Spanish and Portuguese from 87 channels documented as disseminating conspiracist and extremist content. Domain experts annotated messages for conspiracist tone, population replacement conspiracy theories, vaccine conspiracies, and hate speech. We extensively report on difficulties and caveats when creating and annotating this type of dataset. We establish classification baselines by evaluating six models in zero-shot fashion and fine-tuning three encoder models, achieving F1 scores up to 0.800 for conspiracist tone, 0.846 for PRCT, 0.843 for vaccine-related conspiracy theories, and 0.734 for hate speech. Inter-annotator agreement was moderate, consistent with the complexity documented in similar annotation tasks.

This study investigates the linguistic composition of hyperpartisan paragraphs in Italian news on climate change, Ukraine war, and immigration by publicly disclosing the dataset to ensure reproducibility. We introduce a new corpus, IHPP, of 356 articles, for a total of 4,861 paragraphs annotated for hyperpartisan news detection at the paragraph level and enriched with span-level annotations of six semantic-pragmatic linguistic traits: figurative speech, irony/sarcasm, epithet, as well as hyperbolic and loaded language. We hypothesized that these traits, while violating Gricean maxims, are key mechanisms of hyperpartisan rhetoric. To test this, we fine-tuned a set of mono- and multilingual BERT models for hyperpartisan detection and evaluated their incorporation in the embedding space. Then, we applied explainable techniques, e.g. Integrated Gradients and SHAP to analyze how models allocate attribution to normal and linguistic-trait tokens. Our result show that loaded language is the most discriminative trait. The dataset is released: https://github.com/MichJoM/IHPP-Climate.

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: https://anonymous.4open.science/r/Climate_HP-RB-D5EF/README.md