A Systematic Survey of Claim Verification: Corpora, Systems, and Case Studies (2025)

Abstract: Automated Claim Verification (CV)—the task of assessing a claim’s veracity against explicitly provided evidence—is a critical tool in the fight against growing misinformation. This survey offers a comprehensive analysis of 198 studies published between January 2022 and March 2025, synthesizing recent advances in CV corpus creation and system design. Through two in-depth case studies, we illuminate persistent challenges in veracity annotation, limitations of conventional CV pipelines, and pitfalls in recent claim decomposition approaches. We conclude by identifying key unresolved challenges and proposing productive directions for future research.

## introduction the growing scale of misinformation has led to a surge of research in automated fact-checking and claim verification (cv), which assess whether a given claim is supported by accompanying references. a key milestone in this field was the release of fever @xcite , a synthetic dataset for cv which sparked the development of other synthetic datasets such as xfever @xcite , feverous @xcite and many more. since then, shared tasks like averitec @xcite have further advanced research by providing standardized datasets and evaluation frameworks for verifying claims against textual evidence. many recent surveys have reviewed designs of cv systems from different angles, including system overviews @xcite @xcite , justification generation @xcite , llm integration @xcite , and multimodal approaches @xcite . several surveys touch upon some elements in cv datasets such as size, input, and output format @xcite @xcite , but few have examined the corpus creation process and its impact on system design. we fill this gap by providing a review of recent corpus-creation practices, together with system design across key components. in this study, we conduct a systematic survey of recent studies on cv in order to answer the following research questions: (1) what corpora are available for cv research and how are they created? (2) what are common approaches in building cv systems? (3) what are the main issues and challenges in corpus construction and system development and what are some future directions to address the issues? we will answer the first two questions in section 4-5 and the last question in section 6-8 with two in-depth case studies. ## task setting the input to a cv system consists of a claim and one or more reference documents (reference in short). the latter is called evidence or context in some previous studies. to avoid confusion, in this study, we use the term evidence-bearing sentences to refer to sentences in the reference that support or refute a claim. the output of a cv system includes a veracity label and optionally a justification to provide support or explanation to the veracity label. a related task is called fact checking (aka opendomain fact-checking), where only a claim is provided as input and the system needs to retrieve relevant documents (i.e. references) from external sources such as the internet. in this survey, we will focus on cv, not fact checking, because one can easily build a fact checking system on top of a cv system by adding a document-retrieval module. also, we want to study the relationship between claims and references and its effect on corpus creation and system development. ## paper selection to ground our analysis, we first collected a set of research papers on claim verification. ## system development of the 198 papers in our survey, 156 build or evaluate cv systems. in this section, we report on the traditional pipeline adopted by many systems and other strategies that go beyond the pipeline. ## case study #1: claim, selected sentences, and veracity as discussed in section 5.1, 68 out of 156 system development papers in our survey included a sentence selection/ranking module, which identifies evidence-bearing sentences (ebss) in the references. once ebss are identified, the veracity label module is a classifier that predicts the label at either the sentence level or the instance level. that is, the input to the classifier is either a single ebs or all the ebss together, plus the claim. the majority of the studies (e.g., @xcite @xcite ) to better understand the need of sentence selection in the cv pipeline and the difficulty of accurate veracity prediction with ebss only, in our first case study (cs1), we look into the following questions: ## case study #2: claim decomposition as discussed in section 5.2, a common strategy to handle complex claims is to decompose the original claims into subclaims; subclaims are then verified in order to obtain a veracity label for the original claim. while this approach can potentially improve system performance and interpretability of system output, the quality of decomposition remains a key bottleneck @xcite . in our second case study (cs2), we examine the following questions: (cs2-q1) what is the average number of subclaims per claim in existing cv corpora? (cs2-q2) how are subclaims generated and used in current cv systems? (cs2-q3) what is the quality of decomposition? the first two questions are easy to answer, and the last one requires a close examination. ## challenges and future directions this survey has revealed a number of challenges in corpus creation and system development. in this section, we focus on the most pressing ones. ## related work our main collection of studies includes eight survey papers. three of them @xcite @xcite provided overviews of the cv systems. two surveys adopted a more focused perspective: eldifrawi et al. (2024) specifically examined methods on justification generation; dmonte et al. (2024) concentrated on the integration of llms into cv systems. another two surveys @xcite examined cv systems in non-english and region-specific contexts and one additional survey @xcite focused on multimodal verification approaches. the scope and focus of our survey differ from previous work; it systematically reviews literature pertaining to both corpora construction and system development. to ground this review, we also conducted two case studies that elucidate outstanding research challenges. ## conclusion our survey of 198 papers (january 2022 -march 2025) provides a detailed analysis of recent advancements in claim verification (cv), focusing on both corpus creation and system design. through two case studies, we first highlight the difficulties of veracity annotation and the limitations of traditional cv pipelines, and then identify common decomposition strategies along with their associated pitfalls. our analysis culminates in a discussion of remaining challenges and proposed future directions for the field. in contrast to the predominant focus in the nlp field on novel system design, our findings underscore the critical importance of data-centric analysis-meticulously examining, annotating, and understanding the data itself. our case studies demonstrate how such analysis reveals fundamental limitations in existing methodologies. addressing these limitations will be a primary focus of our future research.