# Stanceosaurus: Classifying Stance Towards Multilingual Misinformation

arXiv: https://arxiv.org/abs/2210.15954 · HTML: https://mlsanity.com/paper/2210.15954/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 78 references that are in the map.
- First posted on arXiv 28 Oct 2022.

## Builds on (most cited first, 30 of 40)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims](https://mlsanity.com/paper/1909.03242/) (2019)
- [X-FACT: A New Benchmark Dataset for Multilingual Fact Checking](https://mlsanity.com/paper/2106.09248/) (2021)
- [SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours](https://mlsanity.com/paper/1704.05972/) (2017)
- [RumourEval 2019: Determining Rumour Veracity and Support for Rumours](https://mlsanity.com/paper/1809.06683/) (2018)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims](https://mlsanity.com/paper/1906.03538/) (2019)
- [Just Say No: Analyzing the Stance of Neural Dialogue Generation in Offensive Contexts](https://mlsanity.com/paper/2108.11830/) (2021)
- [All-in-one: Multi-task Learning for Rumour Verification](https://mlsanity.com/paper/1806.03713/) (2018)
- [Will-They-Won't-They: A Very Large Dataset for Stance Detection on Twitter](https://mlsanity.com/paper/2005.00388/) (2020)
- [A Retrospective Analysis of the Fake News Challenge Stance Detection Task](https://mlsanity.com/paper/1806.05180/) (2018)
- [Integrating Stance Detection and Fact Checking in a Unified Corpus](https://mlsanity.com/paper/1804.08012/) (2018)
- [Stance Detection Benchmark: How Robust Is Your Stance Detection?](https://mlsanity.com/paper/2001.01565/) (2020)
- [Cross-Domain Label-Adaptive Stance Detection](https://mlsanity.com/paper/2104.07467/) (2021)
- [Few-Shot Cross-Lingual Stance Detection with Sentiment-Based Pre-Training](https://mlsanity.com/paper/2109.06050/) (2021)
- [Pre-train or Annotate? Domain Adaptation with a Constrained Budget](https://mlsanity.com/paper/2109.04711/) (2021)
- [A simple but tough-to-beat baseline for the Fake News Challenge stance detection task](https://mlsanity.com/paper/1707.03264/) (2017)
- [Stance Detection in Web and Social Media: A Comparative Study](https://mlsanity.com/paper/2007.05976/) (2020)
- [Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM](https://mlsanity.com/paper/1704.07221/) (2017)
- [Your Stance is Exposed! Analysing Possible Factors for Stance Detection on Social Media](https://mlsanity.com/paper/1908.03146/) (2019)
- [Contrastive Language Adaptation for Cross-Lingual Stance Detection](https://mlsanity.com/paper/1910.02076/) (2019)
- [Multilingual Stance Detection: The Catalonia Independence Corpus](https://mlsanity.com/paper/2004.00050/) (2020)
- [Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity](https://mlsanity.com/paper/1909.08211/) (2019)

## Built on by (most cited first, 8 of 8)

- [How to Protect Yourself from 5G Radiation? Investigating LLM Responses to Implicit Misinformation](https://mlsanity.com/paper/2503.09598/) (2025)
- [Zero-Shot Conversational Stance Detection: Dataset and Approaches](https://mlsanity.com/paper/2506.17693/) (2025)
- [Large Language Models Meet Stance Detection: A Survey of Tasks, Methods, Applications, Challenges and Future Directions](https://mlsanity.com/paper/2505.08464/) (2025)
- [On the Risk of Evidence Pollution for Malicious Social Text Detection in the Era of LLMs](https://mlsanity.com/paper/2410.12600/) (2024)
- [UKElectionNarratives: A Dataset of Misleading Narratives Surrounding Recent UK General Elections](https://mlsanity.com/paper/2505.05459/) (2025)
- [Enabling Contextual Soft Moderation on Social Media through Contrastive Textual Deviation](https://mlsanity.com/paper/2407.20910/) (2024)
- [Overview of Factify5WQA: Fact Verification through 5W Question-Answering](https://mlsanity.com/paper/2410.04236/) (2024)
- [To Lie or Not to Lie? Investigating The Biased Spread of Global Lies by LLMs](https://mlsanity.com/paper/2604.06552/) (2026)

## Abstract (arXiv)

We present Stanceosaurus, a new corpus of 28,033 tweets in English, Hindi, and Arabic annotated with stance towards 251 misinformation claims. As far as we are aware, it is the largest corpus annotated with stance towards misinformation claims. The claims in Stanceosaurus originate from 15 fact-checking sources that cover diverse geographical regions and cultures. Unlike existing stance datasets, we introduce a more fine-grained 5-class labeling strategy with additional subcategories to distinguish implicit stance. Pre-trained transformer-based stance classifiers that are fine-tuned on our corpus show good generalization on unseen claims and regional claims from countries outside the training data. Cross-lingual experiments demonstrate Stanceosaurus' capability of training multi-lingual models, achieving 53.1 F1 on Hindi and 50.4 F1 on Arabic without any target-language fine-tuning. Finally, we show how a domain adaptation method can be used to improve performance on Stanceosaurus using additional RumourEval-2019 data. We make Stanceosaurus publicly available to the research community and hope it will encourage further work on misinformation identification across languages and cultures.