Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media

Barrón-Cedeño, Alberto; Elsayed, Tamer; Nakov, Preslav; Martino, Giovanni Da San; Hasanain, Maram; Suwaileh, Reem; Haouari, Fatima; Babulkov, Nikolay; Hamdan, Bayan; Nikolov, Alex; Shaar, Shaden; Ali, Zien Sheikh

doi:10.1007/978-3-030-58219-7_17

Cited by 62 publications

(69 citation statements)

References 22 publications

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“…The 2019 edition [18,19] featured political debates and isolated claims, in conjunction with a closed set of Web documents to retrieve evidence from. In 2020 [8], the focus was on social media-in particular on Twitter -as information posted on this platform is not checked by an authoritative entity before publication and such information tends to disseminate very quickly. Moreover, social media posts lack context due to their short length and conversational nature; thus, identifying a claim's context is sometimes key for enabling effective fact-checking [14].…”

Section: Introductionmentioning

confidence: 99%

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

Nakov¹,

Martino

Elsayed

et al. 2021

Lecture Notes in Computer Science

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We describe the fourth edition of the CheckThat! Lab, part of the 2021 Cross-Language Evaluation Forum (CLEF). The lab evaluates technology supporting various tasks related to factuality, and it is offered in Arabic, Bulgarian, English, and Spanish. Task 1 asks to predict which tweets in a Twitter stream are worth fact-checking (focusing on COVID-19). Task 2 asks to determine whether a claim in a tweet can be verified using a set of previously fact-checked claims. Task 3 asks to predict the veracity of a target news article and its topical domain. The evaluation is carried out using mean average precision or precision at rank k for the ranking tasks, and F1 for the classification tasks.

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Section: Introductionmentioning

confidence: 99%

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

Nakov¹,

Martino

Elsayed

et al. 2021

Lecture Notes in Computer Science

Self Cite

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“…Researchers have developed valuable fact-checking datasets, pipelines and models, an effort which has also been supported by shared tasks, including RumourEval (Derczynski et al, 2017;Gorrell et al, 2018), CLEF CheckThat! ( Barron-Cedeno et al, 2020;Elsayed et al, 2019;Nakov et al, 2018), ClaimBuster (Hassan, Zhang et al, 2017), FEVER (Thorne et al, 2019;, SCIVER (Wadden et al, 2020), Fake News Challenge (Pomerleau & Rao, 2017) and HeroX fact checking challenge (Francis & Fact, 2016).…”

Section: Introductionmentioning

confidence: 99%

Automated fact‐checking: A survey

Zeng

Abumansour

Zubiaga

2021

Language and Linguist. Compass

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As online false information continues to grow, automated fact‐checking has gained an increasing amount of attention in recent years. Researchers in the field of Natural Language Processing (NLP) have contributed to the task by building fact‐checking datasets, devising automated fact‐checking pipelines and proposing NLP methods to further research in the development of different components. This article reviews relevant research on automated fact‐checking covering both the claim detection and claim validation components.

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“…Misinformation is on the rise, and people are fighting it with fact checking. However, most of the work in the current literature (Thorne et al, 2018;Zhang et al, 2019;Barrón-Cedeno et al, 2020;Hidey et al, 2020) focuses on automating factchecking for a single claim. In reality, a claim can be complex, and proposed as a conclusion of an article.…”

Section: Introductionmentioning

confidence: 99%

What is Your Article Based On? Inferring Fine-grained Provenance

Zhang¹,

Roth²

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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When evaluating an article and the claims it makes, a critical reader must be able to assess where the information presented comes from, and whether the various claims are mutually consistent and support the conclusion. This motivates the study of claim provenance, which seeks to trace and explain the origins of claims. In this paper, we introduce new techniques to model and reason about the provenance of multiple interacting claims, including how to capture fine-grained information about the context. Our solution hinges on first identifying the sentences that potentially contain important external information. We then develop a query generator with our novel rank-aware cross attention mechanism, which aims at generating metadata for the source article, based on the context and signals collected from a search engine. This establishes relevant search queries, and it allows us to obtain source article candidates for each identified sentence and propose an ILP based algorithm to infer the best sources. We experiment with a newly created evaluation dataset 1 , Politi-Prov, based on fact-checking articles from www.politifa ct.com; our experimental results show that our solution leads to a significant improvement over baselines.

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Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media

Cited by 62 publications

References 22 publications

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

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

Automated fact‐checking: A survey

What is Your Article Based On? Inferring Fine-grained Provenance

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