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

arXiv: https://arxiv.org/abs/2109.12986 · HTML: https://mlsanity.com/paper/2109.12986/

## Key facts

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

## Builds on (most cited first, 30 of 30)

- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://mlsanity.com/paper/2101.01785/) (2020)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [Automated Fact Checking: Task formulations, methods and future directions](https://mlsanity.com/paper/1806.07687/) (2018)
- [KUISAIL at SemEval-2020 Task 12: BERT-CNN for Offensive Speech Identification in Social Media](https://mlsanity.com/paper/2007.13184/) (2020)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society](https://mlsanity.com/paper/2005.00033/) (2020)
- [The COVID-19 Social Media Infodemic](https://mlsanity.com/paper/2003.05004/) (2020)
- [Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detection](https://mlsanity.com/paper/1809.08193/) (2018)
- [ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research](https://mlsanity.com/paper/2006.05557/) (2020)
- [Fighting the COVID-19 Infodemic in Social Media: A Holistic Perspective and a Call to Arms](https://mlsanity.com/paper/2007.07996/) (2020)
- [Detecting East Asian Prejudice on Social Media](https://mlsanity.com/paper/2005.03909/) (2020)
- [A Survey on Truth Discovery](https://mlsanity.com/paper/1505.02463/) (2015)
- [A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration](https://mlsanity.com/paper/2004.03688/) (2020)
- [A Survey on Predicting the Factuality and the Bias of News Media](https://mlsanity.com/paper/2103.12506/) (2021)
- [GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information](https://mlsanity.com/paper/2005.11177/) (2020)
- [ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detection](https://mlsanity.com/paper/2010.08768/) (2020)
- [Large Arabic Twitter Dataset on COVID-19](https://mlsanity.com/paper/2004.04315/) (2020)
- [Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions](https://mlsanity.com/paper/2005.08820/) (2020)
- [Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19](https://mlsanity.com/paper/2005.06012/) (2020)
- [Challenges in Combating COVID-19 Infodemic -- Data, Tools, and Ethics](https://mlsanity.com/paper/2005.13691/) (2020)
- [ArCorona: Analyzing Arabic Tweets in the Early Days of Coronavirus (COVID-19) Pandemic](https://mlsanity.com/paper/2012.01462/) (2020)
- [Transformers to Fight the COVID-19 Infodemic](https://mlsanity.com/paper/2104.12201/) (2021)
- [The Velocity of Censorship: High-Fidelity Detection of Microblog Post Deletions](https://mlsanity.com/paper/1303.0597/) (2013)
- [Analysis of misinformation during the COVID-19 outbreak in China: cultural, social and political entanglements](https://mlsanity.com/paper/2005.10414/) (2020)
- [Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus](https://mlsanity.com/paper/2006.03354/) (2020)
- [Fighting the COVID-19 Infodemic with a Holistic BERT Ensemble](https://mlsanity.com/paper/2104.05745/) (2021)
- [Linguistic Fingerprints of Internet Censorship: the Case of SinaWeibo](https://mlsanity.com/paper/2001.08845/) (2020)

## Built on by (most cited first, 14 of 14)

- [Claim Detection for Automated Fact-checking: A Survey on Monolingual, Multilingual and Cross-Lingual Research](https://mlsanity.com/paper/2401.11969/) (2024)
- [Overview of the WANLP 2022 Shared Task on Propaganda Detection in Arabic](https://mlsanity.com/paper/2211.10057/) (2022)
- [Challenges and Opportunities in Information Manipulation Detection: An Examination of Wartime Russian Media](https://mlsanity.com/paper/2205.12382/) (2022)
- [A Guide to Misinformation Detection Data and Evaluation](https://mlsanity.com/paper/2411.05060/) (2024)
- [Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions](https://mlsanity.com/paper/2110.13980/) (2021)
- [Monolingual and Multilingual Misinformation Detection for Low-Resource Languages: A Comprehensive Survey](https://mlsanity.com/paper/2410.18390/) (2024)
- [ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination](https://mlsanity.com/paper/2201.06496/) (2022)
- [Toxic language detection: a systematic review of Arabic datasets](https://mlsanity.com/paper/2312.07228/) (2023)
- [ViClaim: A Multilingual Multilabel Dataset for Automatic Claim Detection in Videos](https://mlsanity.com/paper/2504.12882/) (2025)
- [Cross-lingual Transfer Learning for Check-worthy Claim Identification over Twitter](https://mlsanity.com/paper/2211.05087/) (2022)
- [Z-Index at CheckThat! Lab 2022: Check-Worthiness Identification on Tweet Text](https://mlsanity.com/paper/2207.07308/) (2022)
- [ClaimPT: A Portuguese Dataset of Annotated Claims in News Articles](https://mlsanity.com/paper/2601.19490/) (2026)
- [ContextClaim: A Context-Driven Paradigm for Verifiable Claim Detection](https://mlsanity.com/paper/2603.30025/) (2026)
- [Multilingual and Cross-Lingual Citation Needed Detection on Wikipedia for Lower-Resource Languages](https://mlsanity.com/paper/2605.31136/) (2026)

## 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.