# Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Research

arXiv: https://arxiv.org/abs/2401.11969 · HTML: https://mlsanity.com/paper/2401.11969/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 93 references that are in the map.
- First posted on arXiv 22 Jan 2024.

## Builds on (most cited first, 30 of 46)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [AdapterHub: A Framework for Adapting Transformers](https://mlsanity.com/paper/2007.07779/) (2020)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Explainable Automated Fact-Checking for Public Health Claims](https://mlsanity.com/paper/2010.09926/) (2020)
- [AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing](https://mlsanity.com/paper/2108.05542/) (2021)
- [Detection and Resolution of Rumours in Social Media: A Survey](https://mlsanity.com/paper/1704.00656/) (2017)
- [Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey](https://mlsanity.com/paper/1711.04305/) (2017)
- [Explainable Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2011.03870/) (2020)
- [That is a Known Lie: Detecting Previously Fact-Checked Claims](https://mlsanity.com/paper/2005.06058/) (2020)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society](https://mlsanity.com/paper/2005.00033/) (2020)
- [MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset](https://mlsanity.com/paper/2202.11684/) (2022)
- [Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection](https://mlsanity.com/paper/1809.08193/) (2018)
- [Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2109.11427/) (2021)
- [Claim Matching Beyond English to Scale Global Fact-Checking](https://mlsanity.com/paper/2106.00853/) (2021)
- [Overview of the CLEF-2018 CheckThat! Lab on Automatic Identification and Verification of Political Claims. Task 1: Check-Worthiness](https://mlsanity.com/paper/1808.05542/) (2018)
- [Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms](https://mlsanity.com/paper/2007.07996/) (2020)
- [Multilingual Previously Fact-Checked Claim Retrieval](https://mlsanity.com/paper/2305.07991/) (2023)
- [ClaimRank: Detecting Check-Worthy Claims in Arabic and English](https://mlsanity.com/paper/1804.07587/) (2018)
- [Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document](https://mlsanity.com/paper/2109.07410/) (2021)

## Built on by (most cited first, 30 of 35)

- [Multilingual Large Language Model: A Survey of Resources, Taxonomy and Frontiers](https://mlsanity.com/paper/2404.04925/) (2024)
- [ReportBench: Evaluating Deep Research Agents via Academic Survey Tasks](https://mlsanity.com/paper/2508.15804/) (2025)
- [Claim Verification in the Age of Large Language Models: A Survey](https://mlsanity.com/paper/2408.14317/) (2024)
- [Surprising Efficacy of Fine-Tuned Transformers for Fact-Checking over Larger Language Models](https://mlsanity.com/paper/2402.12147/) (2024)
- [FactCheck Editor: Multilingual Text Editor with End-to-End fact-checking](https://mlsanity.com/paper/2404.19482/) (2024)
- [MultiClaimNet: A Massively Multilingual Dataset of Fact-Checked Claim Clusters](https://mlsanity.com/paper/2503.22280/) (2025)
- [FactFinders at CheckThat! 2024: Refining Check-worthy Statement Detection with LLMs through Data Pruning](https://mlsanity.com/paper/2406.18297/) (2024)
- [A Survey on Automatic Credibility Assessment Using Textual Credibility Signals in the Era of Large Language Models](https://mlsanity.com/paper/2410.21360/) (2024)
- [Entity-aware Cross-lingual Claim Detection for Automated Fact-checking](https://mlsanity.com/paper/2503.15220/) (2025)
- [ViClaim: A Multilingual Multilabel Dataset for Automatic Claim Detection in Videos](https://mlsanity.com/paper/2504.12882/) (2025)
- [A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination](https://mlsanity.com/paper/2505.00008/) (2025)
- [IRIS: An Iterative and Integrated Framework for Verifiable Causal Discovery in the Absence of Tabular Data](https://mlsanity.com/paper/2510.09217/) (2025)
- [A Comparative Study of Translation Bias and Accuracy in Multilingual Large Language Models for Cross-Language Claim Verification](https://mlsanity.com/paper/2410.10303/) (2024)
- [Word2winners at SemEval-2025 Task 7: Multilingual and Crosslingual Fact-Checked Claim Retrieval](https://mlsanity.com/paper/2503.09011/) (2025)
- [Multilingual vs Crosslingual Retrieval of Fact-Checked Claims: A Tale of Two Approaches](https://mlsanity.com/paper/2505.22118/) (2025)
- [RAVE: Retrieval and Scoring Aware Verifiable Claim Detection](https://mlsanity.com/paper/2509.15793/) (2025)
- [The Psychology of Falsehood: A Human-Centric Survey of Misinformation Detection](https://mlsanity.com/paper/2509.15896/) (2025)
- [MultiCaption: Detecting disinformation using multilingual visual claims](https://mlsanity.com/paper/2601.11220/) (2026)
- [When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms](https://mlsanity.com/paper/2604.16767/) (2026)
- [From Articles to Premises: Building PrimeFacts, an Extraction Methodology and Resource for Fact-Checking Evidence](https://mlsanity.com/paper/2605.06006/) (2026)
- [Multilingual Fact-Checking at Scale: Fine-Tuned Compact Models vs LLMs](https://mlsanity.com/paper/2606.08605/) (2026)
- [Enhancing Multi-Hop Fact Verification with Structured Knowledge-Augmented Large Language Models](https://mlsanity.com/paper/2503.08495/) (2025)
- [TurQUaz at CheckThat! 2025: Debating Large Language Models for Scientific Web Discourse Detection](https://mlsanity.com/paper/2508.08265/) (2025)
- [Exploring Content and Social Connections of Fake News with Explainable Text and Graph Learning](https://mlsanity.com/paper/2508.10040/) (2025)
- [PledgeTracker: A System for Monitoring the Fulfilment of Pledges](https://mlsanity.com/paper/2509.11804/) (2025)
- [Automatic Fact-checking in English and Telugu](https://mlsanity.com/paper/2509.26415/) (2025)
- [Agent-based Automated Claim Matching with Instruction-following LLMs](https://mlsanity.com/paper/2510.23924/) (2025)
- [Multilingual, Multimodal Pipeline for Creating Authentic and Structured Fact-Checked Claim Dataset](https://mlsanity.com/paper/2601.07985/) (2026)
- [ClaimPT: A Portuguese Dataset of Annotated Claims in News Articles](https://mlsanity.com/paper/2601.19490/) (2026)
- [MultiCW: A Large-Scale Balanced Benchmark Dataset for Training Robust Check-Worthiness Detection Models](https://mlsanity.com/paper/2602.16298/) (2026)

## Abstract (arXiv)

Automated fact-checking has drawn considerable attention over the past few decades due to the increase in the diffusion of misinformation on online platforms. This is often carried out as a sequence of tasks comprising (i) the detection of sentences circulating in online platforms which constitute claims needing verification, followed by (ii) the verification process of those claims. This survey focuses on the former, by discussing existing efforts towards detecting claims needing fact-checking, with a particular focus on multilingual data and methods. This is a challenging and fertile direction where existing methods are yet far from matching human performance due to the profoundly challenging nature of the issue. Especially, the dissemination of information across multiple social platforms, articulated in multiple languages and modalities demands more generalized solutions for combating misinformation. Focusing on multilingual misinformation, we present a comprehensive survey of existing multilingual claim detection research. We present state-of-the-art multilingual claim detection research categorized into three key factors of the problem, verifiability, priority, and similarity. Further, we present a detailed overview of the existing multilingual datasets along with the challenges and suggest possible future advancements.