The ClaimReview Project

eLife Claim Trees — eLife Claim Trees

Panel-level claim graphs for reproducibility — eLife Claim Trees

Most arguments scatter across papers, posts, and threads — and evaporate. A claim tree gives them a stable structure to gather against, the way a cathedral gathers centuries of work into a single, standing thing.

Scientific Web Claims: A survey of definitions, tasks, datasets and methods

Scientific web claims are seen as scientific claims as observed on the Web, across social media, online news, and other platforms. The growing prevalence of scientific discussions on the Web has intensified the need to process and assess this specific type of claims. Unlike claims from scientific publications, scientific web claims are expressed in lay terms, are often decontextualized, and typically lack proper citations, which poses unique challenges for their identification, verification, and communication. Nevertheless, the correct processing of scientific web claims is crucial to keeping online science discussions accurate and informed, for instance through fact-checking. This survey provides the first systematic overview dedicated specifically to scientific web claims. We review and compare existing definitions, task formulations, datasets, and methodological approaches across three major perspectives: (1) Scientific fact-checking on the Web, (2) Scientific citations on the Web, and (3) Science communication on the Web. Our interdisciplinary analysis integrates insights from natural language processing, information retrieval, artificial intelligence, social sciences, and science communication. We identify major methodological challenges, including the lack of unified definitions, domain-agnostic corpora, and foundational models tailored to science-related online discourse. We also discuss challenges related to the existing interplay between emotions and distortions of science online. By mapping current research efforts and highlighting open problems, this survey lays the groundwork for developing robust datasets, methods, and evaluation frameworks to advance the automated processing of scientific web claims, a necessary capability for strengthening the reliability of science-related online discourse at scale.

NLnet; Nanoarguments

Scientific knowledge is currently scattered across papers, repositories, and disconnected platforms, with no structured way to trace how claims connect to evidence or how arguments develop. Nanoarguments builds a framework and tools for creating, browsing, and contributing to a global, federated graph of scientific discourse and evidence. Researchers and their communities can collaboratively structure claims, evidence chains, and discussion as nanopublications, which are small, cryptographically signed Linked Data snippets with precise provenance and authorship, published to a decentralized peer-to-peer network. The project builds upon the Nanodash interface to help users browse, edit, and aggregate discourse and evidence graphs, and integrates with dokieli to enable in-context authoring of nanopublications as inline annotations while reading or writing a document. A bidirectional ActivityPub connector bridges the nanopublication network and the fediverse, allowing discourse threads to start as social exchanges and crystallize into persistent, machine-readable evidence records. The project will be piloted with early adopter research groups in discourse and evidence modeling. All components will be released as open-source modules that other systems can build upon.

Quarterly AI False Claim Monitor — January 2026

To download this NewsGuard report, please fill out your details below and you will be redirected to it. If you'd like to learn more about working with NewsGuard, email [email protected].

AI False Claims Monitor

As the domain experts in data reliability in the topic of news and information, NewsGuard provides the leading red-teaming analysis for information reliability. AI models continue to face significant challenges in ensuring their models provide safe, accurate responses to prompts instead of spreading false claims on the internet or refusing to respond to topics in the news.

ViewSift

The web's evidence-based discussion platform. Built on ATProto!

ViewSift

The web's evidence-based discussion platform. Built on ATProto!

TheoremGraph: Bridging Formal and Informal Mathematics

Mathematical knowledge is organized around statements and their dependencies, but this structure is exposed unevenly: informal papers cite mostly at the document level, while formal libraries record fine-grained dependencies over a much smaller body of mathematics. We introduce TheoremGraph, a unified statement-level dependency graph spanning both informal and formal mathematics. On the informal side, we parse 11.7M theorem-like environments from mathematics arXiv and recover 18.3M candidate directed dependencies, each labeled by the extractor that proposed it so downstream users can trade coverage for precision. On the formal side, we release LeanGraph, a Lean 4 elaborator-level extractor producing 388,105 declaration nodes and 11.3M typed edges across 25 Lean projects. We bridge the two graphs by embedding generated natural-language slogans into a shared semantic space, linking related statements across papers and across the informal/formal divide; an LLM judge affirms 47,952 such matches above a 0.8 cosine floor, with the judge-acceptance rate rising from 48% across the floor to 87% in the >=0.9 tier. On formal concept retrieval, our name-and-signature representation with graph expansion comes within 0.5pp of LeanSearch v2's reranked Recall@10 (0.775 vs. 0.780) without an LM reranker. We release the dataset, extractors, HTTP API, and MCP interface as infrastructure for mathematical search, attribution, and retrieval-augmented reasoning, available at theoremsearch.com and huggingface.co/datasets/uw-math-ai/theorem-matching.

Distinct representational properties of cues and contexts shape fear and reversal learning — eLife Claim Trees

Panel-level claim graphs for reproducibility — eLife Claim Trees

OpenStreetMap Taginfo

Reports show the tag data from different angles. They often bring together data from several sources in interesting ways. Some of the reports can help with finding specific errors.

River Computer

Perceptual system for verified claims about the state of the world

RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checking

Large Language Models (LLMs) hold significant potential for advancing fact-checking by leveraging their capabilities in reasoning, evidence retrieval, and explanation generation. However, existing benchmarks fail to comprehensively evaluate LLMs and Multimodal Large Language Models (MLLMs) in realistic misinformation scenarios. To bridge this gap, we introduce RealFactBench, a comprehensive benchmark designed to assess the fact-checking capabilities of LLMs and MLLMs across diverse real-world tasks, including Knowledge Validation, Rumor Detection, and Event Verification. RealFactBench consists of 6K high-quality claims drawn from authoritative sources, encompassing multimodal content and diverse domains. Our evaluation framework further introduces the Unknown Rate (UnR) metric, enabling a more nuanced assessment of models' ability to handle uncertainty and balance between over-conservatism and over-confidence. Extensive experiments on 7 representative LLMs and 4 MLLMs reveal their limitations in real-world fact-checking and offer valuable insights for further research. RealFactBench is publicly available at https://github.com/kalendsyang/RealFactBench.git.