# Multilingual Stance Detection: The Catalonia Independence Corpus

arXiv: https://arxiv.org/abs/2004.00050 · HTML: https://mlsanity.com/paper/2004.00050/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 16 of 16)

- [Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training](https://mlsanity.com/paper/2109.06050/) (2021)
- [MedS3: Towards Medical Slow Thinking with Self-Evolved Soft Dual-sided Process Supervision](https://mlsanity.com/paper/2501.12051/) (2025)
- [Unsupervised Layer-wise Score Aggregation for Textual OOD Detection](https://mlsanity.com/paper/2302.09852/) (2023)
- [Semi-automatic Generation of Multilingual Datasets for Stance Detection in Twitter](https://mlsanity.com/paper/2101.11978/) (2021)
- [Stanceosaurus: Classifying Stance Towards Multilingual Misinformation](https://mlsanity.com/paper/2210.15954/) (2022)
- [Benchmarking zero-shot stance detection with FlanT5-XXL: Insights from training data, prompting, and decoding strategies into its near-SoTA performance](https://mlsanity.com/paper/2403.00236/) (2024)
- [Large Language Models Meet Stance Detection: A Survey of Tasks, Methods, Applications, Challenges and Future Directions](https://mlsanity.com/paper/2505.08464/) (2025)
- [Collaborative Knowledge Infusion for Low-resource Stance Detection](https://mlsanity.com/paper/2403.19219/) (2024)
- [SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)](https://mlsanity.com/paper/2604.07066/) (2026)
- [Semi-supervised Stance Detection of Tweets Via Distant Network Supervision](https://mlsanity.com/paper/2201.00614/) (2022)
- [Towards Trustworthy Deception Detection: Benchmarking Model Robustness across Domains, Modalities, and Languages](https://mlsanity.com/paper/2104.11761/) (2021)
- [Language Independent Stance Detection: Social Interaction-based Embeddings and Large Language Models](https://mlsanity.com/paper/2210.05715/) (2022)
- [Interpretable Cross-Examination Technique (ICE-T): Using highly informative features to boost LLM performance](https://mlsanity.com/paper/2405.06703/) (2024)
- [SCRum-9: Multilingual Stance Classification over Rumours on Social Media](https://mlsanity.com/paper/2505.18916/) (2025)
- [Multilingual Target-Stance Extraction](https://mlsanity.com/paper/2510.22334/) (2025)
- [Rationale-Guided Knowledge Distillation for Cross-Lingual Stance Detection](https://mlsanity.com/paper/2607.18693/) (2026)

## Abstract (arXiv)

Stance detection aims to determine the attitude of a given text with respect to a specific topic or claim. While stance detection has been fairly well researched in the last years, most the work has been focused on English. This is mainly due to the relative lack of annotated data in other languages. The TW-10 Referendum Dataset released at IberEval 2018 is a previous effort to provide multilingual stance-annotated data in Catalan and Spanish. Unfortunately, the TW-10 Catalan subset is extremely imbalanced. This paper addresses these issues by presenting a new multilingual dataset for stance detection in Twitter for the Catalan and Spanish languages, with the aim of facilitating research on stance detection in multilingual and cross-lingual settings. The dataset is annotated with stance towards one topic, namely, the independence of Catalonia. We also provide a semi-automatic method to annotate the dataset based on a categorization of Twitter users. We experiment on the new corpus with a number of supervised approaches, including linear classifiers and deep learning methods. Comparison of our new corpus with the with the TW-1O dataset shows both the benefits and potential of a well balanced corpus for multilingual and cross-lingual research on stance detection. Finally, we establish new state-of-the-art results on the TW-10 dataset, both for Catalan and Spanish.