# Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twitter

arXiv: https://arxiv.org/abs/2210.04710 · HTML: https://mlsanity.com/paper/2210.04710/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 63 references that are in the map.
- First posted on arXiv 10 Oct 2022.

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

- [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)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [Bidirectional Attention Flow for Machine Comprehension](https://mlsanity.com/paper/1611.01603/) (2016)
- [A Decomposable Attention Model for Natural Language Inference](https://mlsanity.com/paper/1606.01933/) (2016)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [Dissecting Contextual Word Embeddings: Architecture and Representation](https://mlsanity.com/paper/1808.08949/) (2018)
- [Parsing Argumentation Structures in Persuasive Essays](https://mlsanity.com/paper/1604.07370/) (2016)
- [SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles](https://mlsanity.com/paper/2009.02696/) (2020)
- [UKP-Athene: Multi-Sentence Textual Entailment for Claim Verification](https://mlsanity.com/paper/1809.01479/) (2018)
- [BERT for Evidence Retrieval and Claim Verification](https://mlsanity.com/paper/1910.02655/) (2019)
- [Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations](https://mlsanity.com/paper/2005.08866/) (2020)
- [What is the Essence of a Claim? Cross-Domain Claim Identification](https://mlsanity.com/paper/1704.07203/) (2017)
- [ClaimRank: Detecting Check-Worthy Claims in Arabic and English](https://mlsanity.com/paper/1804.07587/) (2018)
- [IMHO Fine-Tuning Improves Claim Detection](https://mlsanity.com/paper/1905.07000/) (2019)
- [Fine-Grained Argument Unit Recognition and Classification](https://mlsanity.com/paper/1904.09688/) (2019)
- [CheckThat! at CLEF 2020: Enabling the Automatic Identification and Verification of Claims in Social Media](https://mlsanity.com/paper/2001.08546/) (2020)
- [LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Content](https://mlsanity.com/paper/2101.11891/) (2021)
- [Claim Detection in Biomedical Twitter Posts](https://mlsanity.com/paper/2104.11639/) (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)
- [Team Alex at CLEF CheckThat! 2020: Identifying Check-Worthy Tweets With Transformer Models](https://mlsanity.com/paper/2009.02931/) (2020)
- [DESYR: Definition and Syntactic Representation Based Claim Detection on the Web](https://mlsanity.com/paper/2108.08759/) (2021)
- [NLRG at SemEval-2021 Task 5: Toxic Spans Detection Leveraging BERT-based Token Classification and Span Prediction Techniques](https://mlsanity.com/paper/2102.12254/) (2021)

## Built on by (most cited first, 19 of 19)

- [Factuality Challenges in the Era of Large Language Models](https://mlsanity.com/paper/2310.05189/) (2023)
- [From Chaos to Clarity: Claim Normalization to Empower Fact-Checking](https://mlsanity.com/paper/2310.14338/) (2023)
- [Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatments](https://mlsanity.com/paper/2212.09683/) (2022)
- [What Makes Medical Claims (Un)Verifiable? Analyzing Entity and Relation Properties for Fact Verification](https://mlsanity.com/paper/2402.01360/) (2024)
- [Lost in Translation, Found in Spans: Identifying Claims in Multilingual Social Media](https://mlsanity.com/paper/2310.18205/) (2023)
- [Document-level Claim Extraction and Decontextualisation for Fact-Checking](https://mlsanity.com/paper/2406.03239/) (2024)
- [An Entity-based Claim Extraction Pipeline for Real-world Biomedical Fact-checking](https://mlsanity.com/paper/2304.05268/) (2023)
- [Overview of the CLAIMSCAN-2023: Uncovering Truth in Social Media through Claim Detection and Identification of Claim Spans](https://mlsanity.com/paper/2310.19267/) (2023)
- [ViClaim: A Multilingual Multilabel Dataset for Automatic Claim Detection in Videos](https://mlsanity.com/paper/2504.12882/) (2025)
- [Leveraging Social Discourse to Measure Check-worthiness of Claims for Fact-checking](https://mlsanity.com/paper/2309.09274/) (2023)
- [Multilingual and Multi-topical Benchmark of Fine-tuned Language models and Large Language Models for Check-Worthy Claim Detection](https://mlsanity.com/paper/2311.06121/) (2023)
- [Overview of the HASOC Subtrack at FIRE 2023: Identification of Tokens Contributing to Explicit Hate in English by Span Detection](https://mlsanity.com/paper/2311.09834/) (2023)
- [Towards Detecting Cascades of Biased Medical Claims on Twitter](https://mlsanity.com/paper/2312.15040/) (2023)
- [The Psychology of Falsehood: A Human-Centric Survey of Misinformation Detection](https://mlsanity.com/paper/2509.15896/) (2025)
- [When Misinformation Speaks and Converses: Rethinking Fact-Checking in Audio Platforms](https://mlsanity.com/paper/2604.16767/) (2026)
- [DS@GT at CheckThat! 2025: A Simple Retrieval-First, LLM-Backed Framework for Claim Normalization](https://mlsanity.com/paper/2508.17402/) (2025)
- [The Alignment Bottleneck in Decomposition-Based Claim Verification](https://mlsanity.com/paper/2602.10380/) (2026)
- [Towards Expectation Detection in Language: A Case Study on Treatment Expectations in Reddit](https://mlsanity.com/paper/2602.15504/) (2026)
- [MultiCW: A Large-Scale Balanced Benchmark Dataset for Training Robust Check-Worthiness Detection Models](https://mlsanity.com/paper/2602.16298/) (2026)

## Abstract (arXiv)

The widespread diffusion of medical and political claims in the wake of COVID-19 has led to a voluminous rise in misinformation and fake news. The current vogue is to employ manual fact-checkers to efficiently classify and verify such data to combat this avalanche of claim-ridden misinformation. However, the rate of information dissemination is such that it vastly outpaces the fact-checkers' strength. Therefore, to aid manual fact-checkers in eliminating the superfluous content, it becomes imperative to automatically identify and extract the snippets of claim-worthy (mis)information present in a post. In this work, we introduce the novel task of Claim Span Identification (CSI). We propose CURT, a large-scale Twitter corpus with token-level claim spans on more than 7.5k tweets. Furthermore, along with the standard token classification baselines, we benchmark our dataset with DABERTa, an adapter-based variation of RoBERTa. The experimental results attest that DABERTa outperforms the baseline systems across several evaluation metrics, improving by about 1.5 points. We also report detailed error analysis to validate the model's performance along with the ablation studies. Lastly, we release our comprehensive span annotation guidelines for public use.