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Overview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News

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arxiv 2109.12987 v1 pith:A65RBONW submitted 2021-09-23 cs.CL cs.IRcs.LGcs.SI

Overview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake News

Preslav Nakov , Giovanni Da San Martino , Tamer Elsayed , Alberto Barr\'on-Cede\~no , Rub\'en M\'iguez , Shaden Shaar , Firoj Alam , Fatima Haouari
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Maram Hasanain Watheq Mansour Bayan Hamdan Zien Sheikh Ali Nikolay Babulkov Alex Nikolov Gautam Kishore Shahi Julia Maria Stru{\ss} Thomas Mandl Mucahid Kutlu Yavuz Selim Kartal
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classification cs.CL cs.IRcs.LGcs.SI
keywords tasksasksclaimsenglishtaskarabiccheckthatevaluation
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We describe the fourth edition of the CheckThat! Lab, part of the 2021 Conference and Labs of the Evaluation Forum (CLEF). The lab evaluates technology supporting tasks related to factuality, and covers Arabic, Bulgarian, English, Spanish, and Turkish. Task 1 asks to predict which posts in a Twitter stream are worth fact-checking, focusing on COVID-19 and politics (in all five languages). Task 2 asks to determine whether a claim in a tweet can be verified using a set of previously fact-checked claims (in Arabic and English). Task 3 asks to predict the veracity of a news article and its topical domain (in English). The evaluation is based on mean average precision or precision at rank k for the ranking tasks, and macro-F1 for the classification tasks. This was the most popular CLEF-2021 lab in terms of team registrations: 132 teams. Nearly one-third of them participated: 15, 5, and 25 teams submitted official runs for tasks 1, 2, and 3, respectively.

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Cited by 2 Pith papers

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  1. From Articles to Premises: Building PrimeFacts, an Extraction Methodology and Resource for Fact-Checking Evidence

    cs.CL 2026-05 unverdicted novelty 6.0

    PrimeFacts extracts decontextualized premises from fact-check articles, raising evidence retrieval MRR by up to 30% and verdict prediction Macro-F1 by 10-20 points over baselines.

  2. From Articles to Premises: Building PrimeFacts, an Extraction Methodology and Resource for Fact-Checking Evidence

    cs.CL 2026-05 conditional novelty 6.0

    Decontextualized, hyperlink-anchored evidence premises from fact-check articles improve retrieval and verdict prediction, released as the PrimeFacts resource.