# Leveraging Social Discourse to Measure Check-worthiness of Claims for Fact-checking arXiv: https://arxiv.org/abs/2309.09274 ยท HTML: https://mlsanity.com/paper/2309.09274/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 29 of its 71 references that are in the map. - First posted on arXiv 17 Sep 2023. ## Builds on (most cited first, 29 of 29) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013) - [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019) - [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019) - [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018) - [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020) - [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020) - [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018) - [MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims](https://mlsanity.com/paper/1909.03242/) (2019) - [The Fact Extraction and VERification (FEVER) Shared Task](https://mlsanity.com/paper/1811.10971/) (2018) - [Parsing Argumentation Structures in Persuasive Essays](https://mlsanity.com/paper/1604.07370/) (2016) - [The science of fake news](https://mlsanity.com/paper/2307.07903/) (2023) - [Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection](https://mlsanity.com/paper/1809.08193/) (2018) - [Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media](https://mlsanity.com/paper/2007.07997/) (2020) - [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) - [ClaimRank: Detecting Check-Worthy Claims in Arabic and English](https://mlsanity.com/paper/1804.07587/) (2018) - [Overview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News](https://mlsanity.com/paper/2109.12987/) (2021) - [LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content](https://mlsanity.com/paper/2101.11891/) (2021) - [Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter](https://mlsanity.com/paper/2210.04710/) (2022) - [Overview of the CLEF-2019 CheckThat!: Automatic Identification and Verification of Claims](https://mlsanity.com/paper/2109.15118/) (2021) - [Accenture at CheckThat! 2020: If you say so: Post-hoc fact-checking of claims using transformer-based models](https://mlsanity.com/paper/2009.02431/) (2020) - [It Takes Nine to Smell a Rat: Neural Multi-Task Learning for Check-Worthiness Prediction](https://mlsanity.com/paper/1908.07912/) (2019) - [Team Alex at CLEF CheckThat! 2020: Identifying Check-Worthy Tweets With Transformer Models](https://mlsanity.com/paper/2009.02931/) (2020) - [Check_square at CheckThat! 2020: Claim Detection in Social Media via Fusion of Transformer and Syntactic Features](https://mlsanity.com/paper/2007.10534/) (2020) - [DESYR: Definition and Syntactic Representation Based Claim Detection on the Web](https://mlsanity.com/paper/2108.08759/) (2021) - [Accenture at CheckThat! 2021: Interesting claim identification and ranking with contextually sensitive lexical training data augmentation](https://mlsanity.com/paper/2107.05684/) (2021) ## Built on by (most cited first, 2 of 2) - [The Psychology of Falsehood: A Human-Centric Survey of Misinformation Detection](https://mlsanity.com/paper/2509.15896/) (2025) - [MultiMind at SemEval-2025 Task 7: Crosslingual Fact-Checked Claim Retrieval via Multi-Source Alignment](https://mlsanity.com/paper/2512.20950/) (2025) ## Abstract (arXiv) The expansion of online social media platforms has led to a surge in online content consumption. However, this has also paved the way for disseminating false claims and misinformation. As a result, there is an escalating demand for a substantial workforce to sift through and validate such unverified claims. Currently, these claims are manually verified by fact-checkers. Still, the volume of online content often outweighs their potency, making it difficult for them to validate every single claim in a timely manner. Thus, it is critical to determine which assertions are worth fact-checking and prioritize claims that require immediate attention. Multiple factors contribute to determining whether a claim necessitates fact-checking, encompassing factors such as its factual correctness, potential impact on the public, the probability of inciting hatred, and more. Despite several efforts to address claim check-worthiness, a systematic approach to identify these factors remains an open challenge. To this end, we introduce a new task of fine-grained claim check-worthiness, which underpins all of these factors and provides probable human grounds for identifying a claim as check-worthy. We present CheckIt, a manually annotated large Twitter dataset for fine-grained claim check-worthiness. We benchmark our dataset against a unified approach, CheckMate, that jointly determines whether a claim is check-worthy and the factors that led to that conclusion. We compare our suggested system with several baseline systems. Finally, we report a thorough analysis of results and human assessment, validating the efficacy of integrating check-worthiness factors in detecting claims worth fact-checking.