# Cross-lingual Transfer Learning for Check-worthy Claim Identification over Twitter

arXiv: https://arxiv.org/abs/2211.05087 · HTML: https://mlsanity.com/paper/2211.05087/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 58 references that are in the map.
- First posted on arXiv 9 Nov 2022.

## Builds on (most cited first, 25 of 25)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT](https://mlsanity.com/paper/1904.09077/) (2019)
- [Are All Languages Created Equal in Multilingual BERT?](https://mlsanity.com/paper/2005.09093/) (2020)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society](https://mlsanity.com/paper/2005.00033/) (2020)
- [Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2109.11427/) (2021)
- [Transfer Learning and Distant Supervision for Multilingual Transformer Models: A Study on African Languages](https://mlsanity.com/paper/2010.03179/) (2020)
- [A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters](https://mlsanity.com/paper/2012.15682/) (2020)
- [A Corpus for Multilingual Document Classification in Eight Languages](https://mlsanity.com/paper/1805.09821/) (2018)
- [Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. Task 1: Check-Worthiness](https://mlsanity.com/paper/1808.05542/) (2018)
- [Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms](https://mlsanity.com/paper/2007.07996/) (2020)
- [A Benchmark Dataset of Check-worthy Factual Claims](https://mlsanity.com/paper/2004.14425/) (2020)
- [Adversarial Learning with Contextual Embeddings for Zero-resource Cross-lingual Classification and NER](https://mlsanity.com/paper/1909.00153/) (2019)
- [Overview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News](https://mlsanity.com/paper/2109.12987/) (2021)
- [Overview of the CLEF-2019 CheckThat!: Automatic Identification and Verification of Claims](https://mlsanity.com/paper/2109.15118/) (2021)
- [Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection](https://mlsanity.com/paper/2109.12986/) (2021)
- [Leveraging Adversarial Training in Self-Learning for Cross-Lingual Text Classification](https://mlsanity.com/paper/2007.15072/) (2020)
- [Accenture at CheckThat! 2021: Interesting claim identification and ranking with contextually sensitive lexical training data augmentation](https://mlsanity.com/paper/2107.05684/) (2021)
- [UPV at CheckThat! 2021: Mitigating Cultural Differences for Identifying Multilingual Check-worthy Claims](https://mlsanity.com/paper/2109.09232/) (2021)
- [Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claims](https://mlsanity.com/paper/2002.07725/) (2020)

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

- [Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Research](https://mlsanity.com/paper/2401.11969/) (2024)
- [Entity-aware Cross-lingual Claim Detection for Automated Fact-checking](https://mlsanity.com/paper/2503.15220/) (2025)

## Abstract (arXiv)

Misinformation spread over social media has become an undeniable infodemic. However, not all spreading claims are made equal. If propagated, some claims can be destructive, not only on the individual level, but to organizations and even countries. Detecting claims that should be prioritized for fact-checking is considered the first step to fight against spread of fake news. With training data limited to a handful of languages, developing supervised models to tackle the problem over lower-resource languages is currently infeasible. Therefore, our work aims to investigate whether we can use existing datasets to train models for predicting worthiness of verification of claims in tweets in other languages. We present a systematic comparative study of six approaches for cross-lingual check-worthiness estimation across pairs of five diverse languages with the help of Multilingual BERT (mBERT) model. We run our experiments using a state-of-the-art multilingual Twitter dataset. Our results show that for some language pairs, zero-shot cross-lingual transfer is possible and can perform as good as monolingual models that are trained on the target language. We also show that in some languages, this approach outperforms (or at least is comparable to) state-of-the-art models.