# Matching Tweets With Applicable Fact-Checks Across Languages

arXiv: https://arxiv.org/abs/2202.07094 · HTML: https://mlsanity.com/paper/2202.07094/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 30 references that are in the map.
- First posted on arXiv 14 Feb 2022.

## Builds on (most cited first, 22 of 22)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Automatic Detection of Fake News](https://mlsanity.com/paper/1708.07104/) (2017)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [Automated Fact Checking: Task formulations, methods and future directions](https://mlsanity.com/paper/1806.07687/) (2018)
- [Explainable Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2011.03870/) (2020)
- [That is a Known Lie: Detecting Previously Fact-Checked Claims](https://mlsanity.com/paper/2005.06058/) (2020)
- [Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection](https://mlsanity.com/paper/1809.08193/) (2018)
- [Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News](https://mlsanity.com/paper/2010.03159/) (2020)
- [Claim Matching Beyond English to Scale Global Fact-Checking](https://mlsanity.com/paper/2106.00853/) (2021)
- [An Exploratory Study of COVID-19 Misinformation on Twitter](https://mlsanity.com/paper/2005.05710/) (2020)
- [Generating Label Cohesive and Well-Formed Adversarial Claims](https://mlsanity.com/paper/2009.08205/) (2020)
- [COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learning](https://mlsanity.com/paper/2101.06278/) (2021)
- [Edited Media Understanding Frames: Reasoning About the Intent and Implications of Visual Misinformation](https://mlsanity.com/paper/2012.04726/) (2020)
- [Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News](https://mlsanity.com/paper/2104.12918/) (2021)
- [AMUSED: An Annotation Framework of Multi-modal Social Media Data](https://mlsanity.com/paper/2010.00502/) (2020)

## Built on by (most cited first, 15 of 15)

- [Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Research](https://mlsanity.com/paper/2401.11969/) (2024)
- [The Perils & Promises of Fact-checking with Large Language Models](https://mlsanity.com/paper/2310.13549/) (2023)
- [Claim Verification in the Age of Large Language Models: A Survey](https://mlsanity.com/paper/2408.14317/) (2024)
- [CONCRETE: Improving Cross-lingual Fact-checking with Cross-lingual Retrieval](https://mlsanity.com/paper/2209.02071/) (2022)
- [Zero-shot and Few-shot Learning with Instruction-following LLMs for Claim Matching in Automated Fact-checking](https://mlsanity.com/paper/2501.10860/) (2025)
- [Truth over Tricks: Measuring and Mitigating Shortcut Learning in Misinformation Detection](https://mlsanity.com/paper/2506.02350/) (2025)
- [Large Language Models for Multilingual Previously Fact-Checked Claim Detection](https://mlsanity.com/paper/2503.02737/) (2025)
- [Breaking Language Barriers with MMTweets: Advancing Cross-Lingual Debunked Narrative Retrieval for Fact-Checking](https://mlsanity.com/paper/2308.05680/) (2023)
- [Cross-Lingual Learning vs. Low-Resource Fine-Tuning: A Case Study with Fact-Checking in Turkish](https://mlsanity.com/paper/2403.00411/) (2024)
- [Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey](https://mlsanity.com/paper/2410.18390/) (2024)
- [When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Edits](https://mlsanity.com/paper/2503.03417/) (2025)
- [The Facade of Truth: Uncovering and Mitigating LLM Susceptibility to Deceptive Evidence](https://mlsanity.com/paper/2601.05478/) (2026)
- [Query Rewriting for Effective Misinformation Discovery](https://mlsanity.com/paper/2210.07467/) (2022)
- [Decoding Climate Disagreement: A Graph Neural Network-Based Approach to Understanding Social Media Dynamics](https://mlsanity.com/paper/2407.07038/) (2024)
- [Agent-based Automated Claim Matching with Instruction-following LLMs](https://mlsanity.com/paper/2510.23924/) (2025)

## Abstract (arXiv)

An important challenge for news fact-checking is the effective dissemination of existing fact-checks. This in turn brings the need for reliable methods to detect previously fact-checked claims. In this paper, we focus on automatically finding existing fact-checks for claims made in social media posts (tweets). We conduct both classification and retrieval experiments, in monolingual (English only), multilingual (Spanish, Portuguese), and cross-lingual (Hindi-English) settings using multilingual transformer models such as XLM-RoBERTa and multilingual embeddings such as LaBSE and SBERT. We present promising results for "match" classification (86% average accuracy) in four language pairs. We also find that a BM25 baseline outperforms or is on par with state-of-the-art multilingual embedding models for the retrieval task during our monolingual experiments. We highlight and discuss NLP challenges while addressing this problem in different languages, and we introduce a novel curated dataset of fact-checks and corresponding tweets for future research.