mlsanityData to 7 Oct 2026

CheckThat! at CLEF 2020: Enabling the Automatic Identification and Verification of Claims in Social Media

arXiv 2001.08546 ↗First posted 21 Jan 2020cs.CL cs.AI cs.IRcited by 25

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Abstract (arXiv)

We describe the third edition of the CheckThat! Lab, which is part of the 2020 Cross-Language Evaluation Forum (CLEF). CheckThat! proposes four complementary tasks and a related task from previous lab editions, offered in English, Arabic, and Spanish. Task 1 asks to predict which tweets in a Twitter stream are worth fact-checking. Task 2 asks to determine whether a claim posted in a tweet can be verified using a set of previously fact-checked claims. Task 3 asks to retrieve text snippets from a given set of Web pages that would be useful for verifying a target tweet's claim. Task 4 asks to predict the veracity of a target tweet's claim using a set of Web pages and potentially useful snippets in them. Finally, the lab offers a fifth task that asks to predict the check-worthiness of the claims made in English political debates and speeches. CheckThat! features a full evaluation framework. The evaluation is carried out using mean average precision or precision at rank k for ranking tasks, and F1 for classification tasks.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jan 2020.

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Citing papers per year: 2021: 11; 2022: 5; 2023: 4; 2024: 3; 2025: 1; 2026: 1.

25 papers.

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  1. 2021Automated Fact-Checking for Assisting Human Fact-Checkerscited by 144
  2. 2021SemEval-2021 Task 6: Detection of Persuasion Techniques in Texts and Imagescited by 49
  3. 2021Claim Matching Beyond English to Scale Global Fact-Checkingcited by 42
  4. 2021LESA: Linguistic Encapsulation and Semantic Amalgamation Based Generalised Claim Detection from Online Contentcited by 22
  5. 2021Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Documentcited by 19
  6. 2022Empowering the Fact-checkers! Automatic Identification of Claim Spans on Twittercited by 19
  7. 2024Generative Large Language Models in Automated Fact-Checking: A Surveycited by 19
  8. 2021The Role of Context in Detecting Previously Fact-Checked Claimscited by 17
  9. 2023From Chaos to Clarity: Claim Normalization to Empower Fact-Checkingcited by 17
  10. 2023Reliability Check: An Analysis of GPT-3's Response to Sensitive Topics and Prompt Wordingcited by 13
  11. 2024Similarity over Factuality: Are we making progress on multimodal out-of-context misinformation detection?cited by 11
  12. 2022CrowdChecked: Detecting Previously Fact-Checked Claims in Social Mediacited by 9
13 more
  1. 2021DESYR: Definition and Syntactic Representation Based Claim Detection on the Webcited by 7
  2. 2022Autonomation, Not Automation: Activities and Needs of European Fact-checkers as a Basis for Designing Human-Centered AI Systemscited by 7
  3. 2024TruthEval: A Dataset to Evaluate LLM Truthfulness and Reliabilitycited by 7
  4. 2022PHEMEPlus: Enriching Social Media Rumour Verification with External Evidencecited by 6
  5. 2021The Case for Claim Difficulty Assessment in Automatic Fact Checkingcited by 5
  6. 2021A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatarcited by 5
  7. 2022Harnessing Abstractive Summarization for Fact-Checked Claim Detectioncited by 5
  8. 2023Overview of the CLAIMSCAN-2023: Uncovering Truth in Social Media through Claim Detection and Identification of Claim Spanscited by 4
  9. 2021WhatTheWikiFact: Fact-Checking Claims Against Wikipediacited by 3
  10. 2025ViClaim: A Multilingual Multilabel Dataset for Automatic Claim Detection in Videoscited by 3
  11. 2023Multilingual and Multi-topical Benchmark of Fine-tuned Language models and Large Language Models for Check-Worthy Claim Detectioncited by 2
  12. 2021REMOD: Relation Extraction for Modeling Online Discoursecited by 0
  13. 2026ClaimPT: A Portuguese Dataset of Annotated Claims in News Articlescited by 0