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 \textsc{PartisanLens}, the first multilingual dataset of \num{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, \textsc{PartisanLens} supports future research on detecting partisan and conspiratorial narratives in European contexts.

翻译：检测党派性叙事与人口替代阴谋论（PRCT）对于应对错误信息的传播至关重要。这些复杂的叙事构成了重大威胁：党派性加剧政治两极分化和制度不信任，而PRCT则直接煽动现实世界中的极端主义暴力，因此识别它们对社会凝聚力与公共安全极为关键。然而，现有资源稀缺，主要以英语为中心，且往往孤立地分析党派性、立场和修辞偏见，而非将其视为政治话语中相互关联的方面。为弥补这一空白，我们引入了 \textsc{PartisanLens}，这是首个包含西班牙语、意大利语和葡萄牙语 \num{1617} 条党派性新闻标题的多语言数据集，并在多个政治话语维度上进行了标注。我们首先评估了广泛使用的大型语言模型（LLMs）在该数据集上的分类性能，为党派性和PRCT叙事的分类建立了稳健的基线。此外，我们评估了使用LLMs作为该任务自动标注工具的可行性，分析了它们逼近人工标注的能力。结果突显了其潜力与当前局限。接着，我们超越标准判断，探索LLMs是否能够通过使其基于模拟标注者视角的社会经济与意识形态画像，来模仿人类标注模式。最后，我们提供了我们的资源与评估，\textsc{PartisanLens} 支持未来在欧洲语境下检测党派性与阴谋论叙事的研究。