In the context of fact-checking, claims are often repeated across various platforms and in different languages, which can benefit from a process that reduces this redundancy. While retrieving previously fact-checked claims has been investigated as a solution, the growing number of unverified claims and expanding size of fact-checked databases calls for alternative, more efficient solutions. A promising solution is to group claims that discuss the same underlying facts into clusters to improve claim retrieval and validation. However, research on claim clustering is hindered by the lack of suitable datasets. To bridge this gap, we introduce MultiClaimNet, a collection of three multilingual claim cluster datasets containing claims in 86 languages across diverse topics. Claim clusters are formed automatically from claim-matching pairs with limited manual intervention. We leverage two existing claim-matching datasets to form the smaller datasets within MultiClaimNet. To build the larger dataset, we propose and validate an approach involving retrieval of approximate nearest neighbors to form candidate claim pairs and an automated annotation of claim similarity using large language models. This larger dataset contains 85.3K fact-checked claims written in 78 languages. We further conduct extensive experiments using various clustering techniques and sentence embedding models to establish baseline performance. Our datasets and findings provide a strong foundation for scalable claim clustering, contributing to efficient fact-checking pipelines.

Retrieving previously fact-checked claims from verified databases has become a crucial area of research in automated fact-checking, given the impracticality of manual verification of massive online content. To address this challenge, SemEval 2025 Task 7 focuses on multilingual previously fact-checked claim retrieval. This paper presents the experiments conducted for this task, evaluating the effectiveness of various sentence transformer models—ranging from 22M to 9B parameters—in conjunction with retrieval strategies such as nearest neighbor search and reranking techniques. Further, we explore the impact of learning context-specific text representation via finetuning these models. Our results demonstrate that smaller and medium-sized models, when optimized with effective finetuning and reranking, can achieve retrieval accuracy comparable to larger models, highlighting their potential for scalable and efficient misinformation detection.