# Stanceosaurus 2.0: Classifying Stance Towards Russian and Spanish Misinformation

arXiv: https://arxiv.org/abs/2402.03642 · HTML: https://mlsanity.com/paper/2402.03642/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 6 Feb 2024.

## Builds on (most cited first, 13 of 13)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [On the Cross-lingual Transferability of Monolingual Representations](https://mlsanity.com/paper/1910.11856/) (2019)
- [Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT](https://mlsanity.com/paper/1904.09077/) (2019)
- [Are Multilingual Models Effective in Code-Switching?](https://mlsanity.com/paper/2103.13309/) (2021)
- [Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts](https://mlsanity.com/paper/2108.11830/) (2021)
- [How Language-Neutral is Multilingual BERT?](https://mlsanity.com/paper/1911.03310/) (2019)
- [Stance Detection Benchmark: How Robust Is Your Stance Detection?](https://mlsanity.com/paper/2001.01565/) (2020)
- [Multilingual Argument Mining: Datasets and Analysis](https://mlsanity.com/paper/2010.06432/) (2020)
- [Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media](https://mlsanity.com/paper/2205.12382/) (2022)
- [Stance Prediction for Russian: Data and Analysis](https://mlsanity.com/paper/1809.01574/) (2018)
- [Automated multilingual detection of Pro-Kremlin propaganda in newspapers and Telegram posts](https://mlsanity.com/paper/2301.10604/) (2023)

## Built on by (most cited first, 2 of 2)

- [SCRum-9: Multilingual Stance Classification over Rumours on Social Media](https://mlsanity.com/paper/2505.18916/) (2025)
- [To Lie or Not to Lie? Investigating The Biased Spread of Global Lies by LLMs](https://mlsanity.com/paper/2604.06552/) (2026)

## Abstract (arXiv)

The Stanceosaurus corpus (Zheng et al., 2022) was designed to provide high-quality, annotated, 5-way stance data extracted from Twitter, suitable for analyzing cross-cultural and cross-lingual misinformation. In the Stanceosaurus 2.0 iteration, we extend this framework to encompass Russian and Spanish. The former is of current significance due to prevalent misinformation amid escalating tensions with the West and the violent incursion into Ukraine. The latter, meanwhile, represents an enormous community that has been largely overlooked on major social media platforms. By incorporating an additional 3,874 Spanish and Russian tweets over 41 misinformation claims, our objective is to support research focused on these issues. To demonstrate the value of this data, we employed zero-shot cross-lingual transfer on multilingual BERT, yielding results on par with the initial Stanceosaurus study with a macro F1 score of 43 for both languages. This underlines the viability of stance classification as an effective tool for identifying multicultural misinformation.