PartisanLens is a dataset focused on hyperpartisanship, stance detection, and PRCT, featuring human-authored rationales and detailed annotations.

partisanlens/
│
├── data/ 📦 Dataset, keywords & rationales
├── data_curation/ 🧪 Data sampling, statistics, and analysis scripts
│ ├── analysis/ 📊 Data analysis scripts
│ └── DPP_extraction.py
├── experiments/ 🧠 Model training, inference, rationale generation
│ ├── build-templated-rationales.py
│ ├── rephrase-rationales.py
│ ├── inference.py
│ └── finetune.py
└── annotation_guidelines.pdf 📄 Annotation schema and instructions

PartisanLens includes:

• 🔴🔵 Hyperpartisan annotations – identifying overtly partisan language
• 🧭 Stance detection – determining whether the speaker is pro, against, or neutral towards immigration
• 🧠 PRCT labels – Population Replacement Conspiracy Theories

Each sample contains:

• A political text segment
• Task-specific labels (hyperpartisan, stance, PRCT)
• Span annotation (loaded language, name calling and appeal to fear)

We provide Python scripts to explore how LLMs and finetuned models handle reasoning with rationales.

This script uses a LLM to rephrase and enrich templated rationales for each instance in the dataset, while preserving the original task labels. The output is a step-by-step explanation in JSON format for each example.

python3 experiments/rephrase-rationales.py \
 --dataset data/train_templated_rationales.csv \
 --output data/train_rephrased_rationales.csv \
 --hf_token your_huggingface_token

This script performs LLM-based inference using zero-shot or few-shot prompting, either to generate rationales and predict labels or only predict labels. You can select different models and modes depending on your use case.

python3 experiments/inference.py \
 --dataset data/test.csv \
 --model llama3.3-70 \
 --mode rationales \
 --output data/predictions.tsv \
 --hf_token your_huggingface_token

You can choose between two modes when running the script:

Fine-tune a model on the dataset with options for generating either rationales or labels.

python3 finetune.py \
 --dataset data/train.csv \
 --model MODEL_NAME llama3.3-70

The data_curation/ directory contains:

• 📈 Scripts for analyzing dataset composition
• ⚖️ Sampling strategies used the create the dataset
• 🧮 Statistical reports and visualizations

Full documentation of tasks, labeling protocols, and rationale-writing instructions are provided in:

📄 annotation_guidelines.pdf

• 🧠 Interpretability research using rationales
  Use the human-curated / LLM-improved rationales to evaluate and improve model transparency and explainability.

• 🔍 Political bias and stance analysis
  Study how models detect hyperpartisan language and take stances toward immigration claims.

• 🤖 Fine-tuning models with explanation supervision
  Train models not only to classify but also to generate or use rationales, improving generalization and trustworthiness.

📌 @inproceedings{maggini-etal-2026-partisanlens, title = "{P}artisan{L}ens: A Multilingual Dataset of Hyperpartisan and Conspiratorial Immigration Narratives in {E}uropean Media", author = "Maggini, Michele Joshua and Piot, Paloma and P{'e}rez, Anxo and Marino, Erik Bran and Montesinos, L{'u}a Santamar{'i}a and Cotovio, Ana Lisboa and Abu{'i}n, Marta V{'a}zquez and Parapar, Javier and Gamallo, Pablo", editor = "Demberg, Vera and Inui, Kentaro and Marquez, Llu{'i}s", booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)", month = mar, year = "2026", address = "Rabat, Morocco", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2026.eacl-long.53/", doi = "10.18653/v1/2026.eacl-long.53", pages = "1171--1186", ISBN = "979-8-89176-380-7", abstract = "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." }
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