SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval
This addresses the global challenge of online disinformation by focusing on multilingual settings and low-resource languages, though it is incremental as it builds on existing shared task frameworks.
The paper tackled the problem of multilingual and crosslingual fact-checked claim retrieval by conducting a shared task at SemEval 2025, which involved 179 participants and 52 test submissions, reporting the best-performing systems and common approaches.
The rapid spread of online disinformation presents a global challenge, and machine learning has been widely explored as a potential solution. However, multilingual settings and low-resource languages are often neglected in this field. To address this gap, we conducted a shared task on multilingual claim retrieval at SemEval 2025, aimed at identifying fact-checked claims that match newly encountered claims expressed in social media posts across different languages. The task includes two subtracks: (1) a monolingual track, where social posts and claims are in the same language, and (2) a crosslingual track, where social posts and claims might be in different languages. A total of 179 participants registered for the task contributing to 52 test submissions. 23 out of 31 teams have submitted their system papers. In this paper, we report the best-performing systems as well as the most common and the most effective approaches across both subtracks. This shared task, along with its dataset and participating systems, provides valuable insights into multilingual claim retrieval and automated fact-checking, supporting future research in this field.