mlsanityData to 7 Oct 2026

Stanceosaurus 2.0: Classifying Stance Towards Russian and Spanish Misinformation

arXiv 2402.03642 ↗First posted 6 Feb 2024cs.CL cs.CY cs.LGcited by 2

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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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 13 of its 35 references that are in the map. First posted on arXiv 6 Feb 2024.

Builds on

13 of 35 references.

References · 13

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,239
  3. 2019How multilingual is Multilingual BERT?cited by 642
  4. 2019On the Cross-lingual Transferability of Monolingual Representationscited by 575
  5. 2019Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERTcited by 301
  6. 2021Are Multilingual Models Effective in Code-Switching?cited by 56
  7. 2021Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contextscited by 56
  8. 2019How Language-Neutral is Multilingual BERT?cited by 51
  9. 2020Stance Detection Benchmark: How Robust Is Your Stance Detection?cited by 33
  10. 2020Multilingual Argument Mining: Datasets and Analysiscited by 19
  11. 2022Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Mediacited by 10
  12. 2018Stance Prediction for Russian: Data and Analysiscited by 8
1 more
  1. 2023Automated multilingual detection of Pro-Kremlin propaganda in newspapers and Telegram postscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.