# Multilingual Previously Fact-Checked Claim Retrieval

arXiv: https://arxiv.org/abs/2305.07991 · HTML: https://mlsanity.com/paper/2305.07991/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 45 references that are in the map.
- First posted on arXiv 13 May 2023.

## Builds on (most cited first, 15 of 15)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [That is a Known Lie: Detecting Previously Fact-Checked Claims](https://mlsanity.com/paper/2005.06058/) (2020)
- [Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News](https://mlsanity.com/paper/2010.03159/) (2020)
- [Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media](https://mlsanity.com/paper/2007.07997/) (2020)
- [Claim Matching Beyond English to Scale Global Fact-Checking](https://mlsanity.com/paper/2106.00853/) (2021)
- [The Rise of Guardians: Fact-checking URL Recommendation to Combat Fake News](https://mlsanity.com/paper/1806.07516/) (2018)
- [Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document](https://mlsanity.com/paper/2109.07410/) (2021)
- [The Role of Context in Detecting Previously Fact-Checked Claims](https://mlsanity.com/paper/2104.07423/) (2021)
- [Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims](https://mlsanity.com/paper/2112.10322/) (2021)
- [Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic](https://mlsanity.com/paper/2106.11702/) (2021)
- [CrowdChecked: Detecting Previously Fact-Checked Claims in Social Media](https://mlsanity.com/paper/2210.04447/) (2022)
- [Autonomation, Not Automation: Activities and Needs of European Fact-checkers as a Basis for Designing Human-Centered AI Systems](https://mlsanity.com/paper/2211.12143/) (2022)
- [Harnessing Abstractive Summarization for Fact-Checked Claim Detection](https://mlsanity.com/paper/2209.04612/) (2022)

## Built on by (most cited first, 30 of 32)

- [Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Research](https://mlsanity.com/paper/2401.11969/) (2024)
- [Claim Verification in the Age of Large Language Models: A Survey](https://mlsanity.com/paper/2408.14317/) (2024)
- [Generative Large Language Models in Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2407.02351/) (2024)
- [Similarity over Factuality: Are we making progress on multimodal out-of-context misinformation detection?](https://mlsanity.com/paper/2407.13488/) (2024)
- [SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval](https://mlsanity.com/paper/2505.10740/) (2025)
- [A Guide to Misinformation Detection Data and Evaluation](https://mlsanity.com/paper/2411.05060/) (2024)
- [MultiClaimNet: A Massively Multilingual Dataset of Fact-Checked Claim Clusters](https://mlsanity.com/paper/2503.22280/) (2025)
- [Large Language Models for Multilingual Previously Fact-Checked Claim Detection](https://mlsanity.com/paper/2503.02737/) (2025)
- [Soft Language Prompts for Language Transfer](https://mlsanity.com/paper/2407.02317/) (2024)
- [MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models](https://mlsanity.com/paper/2402.13606/) (2024)
- [HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claims](https://mlsanity.com/paper/2502.11753/) (2025)
- [When Scale Meets Diversity: Evaluating Language Models on Fine-Grained Multilingual Claim Verification](https://mlsanity.com/paper/2507.20700/) (2025)
- [Multilingual Models for Check-Worthy Social Media Posts Detection](https://mlsanity.com/paper/2408.06737/) (2024)
- [BLUFF: Benchmarking the Detection of False and Synthetic Content across 58 Low-Resource Languages](https://mlsanity.com/paper/2603.00634/) (2026)
- [Word2winners at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval](https://mlsanity.com/paper/2503.09011/) (2025)
- [A Generative-AI-Driven Claim Retrieval System Capable of Detecting and Retrieving Claims from Social Media Platforms in Multiple Languages](https://mlsanity.com/paper/2504.20668/) (2025)
- [Multilingual vs Crosslingual Retrieval of Fact-Checked Claims: A Tale of Two Approaches](https://mlsanity.com/paper/2505.22118/) (2025)
- [Investigating Language and Retrieval Bias in Multilingual Previously Fact-Checked Claim Detection](https://mlsanity.com/paper/2509.25138/) (2025)
- [MultiCaption: Detecting disinformation using multilingual visual claims](https://mlsanity.com/paper/2601.11220/) (2026)
- [DIA-HARM: Dialectal Disparities in Harmful Content Detection Across 50 English Dialects](https://mlsanity.com/paper/2604.05318/) (2026)
- [ExU: AI Models for Examining Multilingual Disinformation Narratives and Understanding their Spread](https://mlsanity.com/paper/2406.15443/) (2024)
- [MlingConf: A Comprehensive Study of Multilingual Confidence Estimation on Large Language Models](https://mlsanity.com/paper/2410.12478/) (2024)
- [TIFIN India at SemEval-2025: Harnessing Translation to Overcome Multilingual IR Challenges in Fact-Checked Claim Retrieval](https://mlsanity.com/paper/2504.16627/) (2025)
- [Duluth at SemEval-2025 Task 7: TF-IDF with Optimized Vector Dimensions for Multilingual Fact-Checked Claim Retrieval](https://mlsanity.com/paper/2505.12616/) (2025)
- [QUST_NLP at SemEval-2025 Task 7: A Three-Stage Retrieval Framework for Monolingual and Crosslingual Fact-Checked Claim Retrieval](https://mlsanity.com/paper/2506.17272/) (2025)
- [fact check AI at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-checked Claim Retrieval](https://mlsanity.com/paper/2508.03475/) (2025)
- [UWBa at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval](https://mlsanity.com/paper/2508.09517/) (2025)
- [o-MEGA: Optimized Methods for Explanation Generation and Analysis](https://mlsanity.com/paper/2510.00288/) (2025)
- [Agent-based Automated Claim Matching with Instruction-following LLMs](https://mlsanity.com/paper/2510.23924/) (2025)
- [MultiMind at SemEval-2025 Task 7: Crosslingual Fact-Checked Claim Retrieval via Multi-Source Alignment](https://mlsanity.com/paper/2512.20950/) (2025)

## Abstract (arXiv)

Fact-checkers are often hampered by the sheer amount of online content that needs to be fact-checked. NLP can help them by retrieving already existing fact-checks relevant to the content being investigated. This paper introduces a new multilingual dataset -- MultiClaim -- for previously fact-checked claim retrieval. We collected 28k posts in 27 languages from social media, 206k fact-checks in 39 languages written by professional fact-checkers, as well as 31k connections between these two groups. This is the most extensive and the most linguistically diverse dataset of this kind to date. We evaluated how different unsupervised methods fare on this dataset and its various dimensions. We show that evaluating such a diverse dataset has its complexities and proper care needs to be taken before interpreting the results. We also evaluated a supervised fine-tuning approach, improving upon the unsupervised method significantly.