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

Authors :
Tamer Elsayed
Fatima Haouari
Maram Hasanain
Bayan Hamdan
Alberto Barrón-Cedeño
Zien Sheikh Ali
Nikolay Babulkov
Preslav Nakov
Reem Suwaileh
Giovanni Da San Martino
Shaden Shaar
Alex Nikolov
Barrón-Cedeño, Alberto
Elsayed, Tamer
Nakov, Preslav
Da San Martino, Giovanni
Hasanain, Maram
Suwaileh, Reem
Haouari, Fatima
Babulkov, Nikolay
Hamdan, Bayan
Nikolov, Alex
Shaar, Shaden
Ali, Zien Sheikh
Source :
Lecture Notes in Computer Science ISBN: 9783030582180, CLEF (Working Notes)
Publication Year :
2020
Publisher :
Springer Science and Business Media Deutschland GmbH, 2020.

Abstract

We present an overview of the third edition of the CheckThat! Lab at CLEF 2020. The lab featured five tasks in two different languages: English and Arabic. The first four tasks compose the full pipeline of claim verification in social media: Task 1 on check-worthiness estimation, Task 2 on retrieving previously fact-checked claims, Task 3 on evidence retrieval, and Task 4 on claim verification. The lab is completed with Task 5 on check-worthiness estimation in political debates and speeches. A total of 67 teams registered to participate in the lab (up from 47 at CLEF 2019), and 23 of them actually submitted runs (compared to 14 at CLEF 2019). Most teams used deep neural networks based on BERT, LSTMs, or CNNs, and achieved sizable improvements over the baselines on all tasks. Here we describe the tasks setup, the evaluation results, and a summary of the approaches used by the participants, and we discuss some lessons learned. Last but not least, we release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research in the important tasks of check-worthiness estimation and automatic claim verification.<br />Comment: Check-Worthiness Estimation, Fact-Checking, Veracity, Evidence-based Verification, Detecting Previously Fact-Checked Claims, Social Media Verification, Computational Journalism, COVID-19

Details

Language :
English
ISBN :
978-3-030-58218-0
ISBNs :
9783030582180
Database :
OpenAIRE
Journal :
Lecture Notes in Computer Science ISBN: 9783030582180, CLEF (Working Notes)
Accession number :
edsair.doi.dedup.....c96f6698d411abe7685fdd6913225375