# Transformers to Fight the COVID-19 Infodemic

arXiv: https://arxiv.org/abs/2104.12201 · HTML: https://mlsanity.com/paper/2104.12201/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 25 Apr 2021.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [End-to-End Open-Domain Question Answering with BERTserini](https://mlsanity.com/paper/1902.01718/) (2019)
- [Multilingual Offensive Language Identification with Cross-lingual Embeddings](https://mlsanity.com/paper/2010.05324/) (2020)
- [Stance and Sentiment in Tweets](https://mlsanity.com/paper/1605.01655/) (2016)
- [Offensive Language Identification in Greek](https://mlsanity.com/paper/2003.07459/) (2020)
- [1.5 billion words Arabic Corpus](https://mlsanity.com/paper/1611.04033/) (2016)
- [MUDES: Multilingual Detection of Offensive Spans](https://mlsanity.com/paper/2102.09665/) (2021)
- [Comparing Approaches to Dravidian Language Identification](https://mlsanity.com/paper/2103.05552/) (2021)
- [BRUMS at SemEval-2020 Task 12 : Transformer based Multilingual Offensive Language Identification in Social Media](https://mlsanity.com/paper/2010.06278/) (2020)
- [InfoMiner at WNUT-2020 Task 2: Transformer-based Covid-19 Informative Tweet Extraction](https://mlsanity.com/paper/2010.05327/) (2020)
- [WLV-RIT at HASOC-Dravidian-CodeMix-FIRE2020: Offensive Language Identification in Code-switched YouTube Comments](https://mlsanity.com/paper/2011.00559/) (2020)
- [BRUMS at SemEval-2020 Task 3: Contextualised Embeddings for Predicting the (Graded) Effect of Context in Word Similarity](https://mlsanity.com/paper/2010.06269/) (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)
- [WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans](https://mlsanity.com/paper/2104.04630/) (2021)
- [TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation](https://mlsanity.com/paper/2104.04632/) (2021)

## Built on by (most cited first, 4 of 4)

- [Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection](https://mlsanity.com/paper/2109.12986/) (2021)
- [DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain](https://mlsanity.com/paper/2205.06025/) (2022)
- [Can Model Fusing Help Transformers in Long Document Classification? An Empirical Study](https://mlsanity.com/paper/2307.09532/) (2023)
- [Transformer-based Detection of Multiword Expressions in Flower and Plant Names](https://mlsanity.com/paper/2209.08016/) (2022)

## Abstract (arXiv)

The massive spread of false information on social media has become a global risk especially in a global pandemic situation like COVID-19. False information detection has thus become a surging research topic in recent months. NLP4IF-2021 shared task on fighting the COVID-19 infodemic has been organised to strengthen the research in false information detection where the participants are asked to predict seven different binary labels regarding false information in a tweet. The shared task has been organised in three languages; Arabic, Bulgarian and English. In this paper, we present our approach to tackle the task objective using transformers. Overall, our approach achieves a 0.707 mean F1 score in Arabic, 0.578 mean F1 score in Bulgarian and 0.864 mean F1 score in English ranking 4th place in all the languages.