mlsanityData to 7 Oct 2026

Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection

arXiv 2109.12986 ↗First posted 23 Sep 2021cs.CL cs.IR cs.LGcited by 14

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Abstract (arXiv)

We present the results and the main findings of the NLP4IF-2021 shared tasks. Task 1 focused on fighting the COVID-19 infodemic in social media, and it was offered in Arabic, Bulgarian, and English. Given a tweet, it asked to predict whether that tweet contains a verifiable claim, and if so, whether it is likely to be false, is of general interest, is likely to be harmful, and is worthy of manual fact-checking; also, whether it is harmful to society, and whether it requires the attention of policy makers. Task~2 focused on censorship detection, and was offered in Chinese. A total of ten teams submitted systems for task 1, and one team participated in task 2; nine teams also submitted a system description paper. Here, we present the tasks, analyze the results, and discuss the system submissions and the methods they used. Most submissions achieved sizable improvements over several baselines, and the best systems used pre-trained Transformers and ensembles. The data, the scorers and the leaderboards for the tasks are available at http://gitlab.com/NLP4IF/nlp4if-2021.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 30 of its 63 references that are in the map. First posted on arXiv 23 Sep 2021.

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30 of 63 references.

References · 30

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18 more
  1. 2020Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Armscited by 41
  2. 2020Detecting East Asian Prejudice on Social Mediacited by 40
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  18. 2020Linguistic Fingerprints of Internet Censorship: the Case of SinaWeibocited by 2

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

14 papers.

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