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.

Idiomatic expressions are a well-known challenge for neural machine translation, including both traditional sequence-to-sequence models and large language models (LLMs). This paper presents a systematic approach to improve idiom translation between Spanish and Galician. First, we build a high-quality parallel dataset of idioms manually aligned across both languages. Then, we automatically extend this dataset into a large synthetic parallel corpus using LLMs, following a strategy that prioritizes the most frequent idioms observed in authentic corpora. This augmented dataset is used to retrain a seq2seq translation model. We evaluate the resulting system and compare it both to the baseline model without idiom data and to state-of-the-art LLM-based translators such as SalamandraTA. Results show that the translation of idioms improves significantly after the training, alongside a slight boost in the model’s overall performance.