# Claim2Vec: Embedding Fact-Check Claims for Multilingual Similarity and Clustering

arXiv: https://arxiv.org/abs/2604.09812 · HTML: https://mlsanity.com/paper/2604.09812/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 30 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 11 of 11)

- [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Efficient Natural Language Response Suggestion for Smart Reply](https://mlsanity.com/paper/1705.00652/) (2017)
- [MMTEB: Massive Multilingual Text Embedding Benchmark](https://mlsanity.com/paper/2502.13595/) (2025)
- [MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset](https://mlsanity.com/paper/2202.11684/) (2022)
- [Claim Matching Beyond English to Scale Global Fact-Checking](https://mlsanity.com/paper/2106.00853/) (2021)
- [SciClops: Detecting and Contextualizing Scientific Claims for Assisting Manual Fact-Checking](https://mlsanity.com/paper/2110.13090/) (2021)
- [MultiClaimNet: A Massively Multilingual Dataset of Fact-Checked Claim Clusters](https://mlsanity.com/paper/2503.22280/) (2025)
- [Analyzing Misinformation Claims During the 2022 Brazilian General Election on WhatsApp, Twitter, and Kwai](https://mlsanity.com/paper/2401.02395/) (2024)
- [SynDy: Synthetic Dynamic Dataset Generation Framework for Misinformation Tasks](https://mlsanity.com/paper/2405.10700/) (2024)

## Abstract (arXiv)

Recurrent claims present a major challenge for automated fact-checking systems designed to combat misinformation, especially in multilingual settings. While tasks such as claim matching and fact-checked claim retrieval aim to address this problem by linking claim pairs, the broader challenge of effectively representing groups of similar claims that can be resolved with the same fact-check via claim clustering remains relatively underexplored. To address this gap, we introduce Claim2Vec, the first multilingual embedding model optimized to represent fact-check claims as vectors in an improved semantic embedding space. We fine-tune a multilingual encoder using contrastive learning with similar multilingual claim pairs. Experiments on the claim clustering task using three datasets, 14 multilingual embedding models, and 7 clustering algorithms demonstrate that Claim2Vec significantly improves clustering performance. Specifically, it enhances both cluster label alignment and the geometric structure of the embedding space across different cluster configurations. Our multilingual analysis shows that clusters containing multiple languages benefit from fine-tuning, demonstrating cross-lingual knowledge transfer.