ClaimPT: A Portuguese Dataset of Annotated Claims in News Articles

2026-01-27

AI Summary

This work addresses the scarcity of high-quality, publicly available annotated datasets for factuality assessment in Portuguese news, which has hindered fact-checking research for this low-resource language. To bridge this gap, the authors introduce ClaimPT—the first large-scale dataset of factual claims derived from European Portuguese news, comprising 1,308 authentic articles from the Portuguese news agency LUSA and 6,875 human-annotated factual statements. The project employs a dedicated annotation protocol featuring dual independent annotators followed by expert adjudication to ensure data quality, and releases the first baseline model for claim identification in Portuguese. ClaimPT fills a critical void in Portuguese-language fact-checking resources, offering a foundational benchmark for multilingual NLP and automated fact verification systems.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Misinformation & Fake News
• Knowledge Representation and Reasoning: Reasoning with Beliefs

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

Fact-checking remains a demanding and time-consuming task, still largely dependent on manual verification and unable to match the rapid spread of misinformation online. This is particularly important because debunking false information typically takes longer to reach consumers than the misinformation itself; accelerating corrections through automation can therefore help counter it more effectively. Although many organizations perform manual fact-checking, this approach is difficult to scale given the growing volume of digital content. These limitations have motivated interest in automating fact-checking, where identifying claims is a crucial first step. However, progress has been uneven across languages, with English dominating due to abundant annotated data. Portuguese, like other languages, still lacks accessible, licensed datasets, limiting research, NLP developments and applications. In this paper, we introduce ClaimPT, a dataset of European Portuguese news articles annotated for factual claims, comprising 1,308 articles and 6,875 individual annotations. Unlike most existing resources based on social media or parliamentary transcripts, ClaimPT focuses on journalistic content, collected through a partnership with LUSA, the Portuguese News Agency. To ensure annotation quality, two trained annotators labeled each article, with a curator validating all annotations according to a newly proposed scheme. We also provide baseline models for claim detection, establishing initial benchmarks and enabling future NLP and IR applications. By releasing ClaimPT, we aim to advance research on low-resource fact-checking and enhance understanding of misinformation in news media.

Problem

• fact-checking
• claim detection
• Portuguese dataset
• misinformation
• news articles

Innovation

• fact-checking
• claim detection
• Portuguese NLP
• annotated dataset
• low-resource language