mlsanityData to 7 Oct 2026

Transformers to Fight the COVID-19 Infodemic

arXiv 2104.12201 ↗First posted 25 Apr 2021cs.CL cs.LG cs.SIcited by 4

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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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 19 of its 38 references that are in the map. First posted on arXiv 25 Apr 2021.

Builds on

19 of 38 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2019How to Fine-Tune BERT for Text Classification?cited by 409
  5. 2020AraBERT: Transformer-based Model for Arabic Language Understandingcited by 359
  6. 2019End-to-End Open-Domain Question Answering with BERTserinicited by 152
  7. 2020Multilingual Offensive Language Identification with Cross-lingual Embeddingscited by 71
  8. 2016Stance and Sentiment in Tweetscited by 62
  9. 2020Offensive Language Identification in Greekcited by 49
  10. 20161.5 billion words Arabic Corpuscited by 35
  11. 2021MUDES: Multilingual Detection of Offensive Spanscited by 30
  12. 2021Comparing Approaches to Dravidian Language Identificationcited by 11
7 more
  1. 2020BRUMS at SemEval-2020 Task 12 : Transformer based Multilingual Offensive Language Identification in Social Mediacited by 10
  2. 2020InfoMiner at WNUT-2020 Task 2: Transformer-based Covid-19 Informative Tweet Extractioncited by 9
  3. 2020WLV-RIT at HASOC-Dravidian-CodeMix-FIRE2020: Offensive Language Identification in Code-switched YouTube Commentscited by 9
  4. 2020BRUMS at SemEval-2020 Task 3: Contextualised Embeddings for Predicting the (Graded) Effect of Context in Word Similaritycited by 8
  5. 2020Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directionscited by 6
  6. 2021WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spanscited by 6
  7. 2021TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguationcited by 3

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 1.

4 papers.

  1. 2021Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detectioncited by 14
  2. 2022DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domaincited by 4
  3. 2023Can Model Fusing Help Transformers in Long Document Classification? An Empirical Studycited by 3
  4. 2022Transformer-based Detection of Multiword Expressions in Flower and Plant Namescited by 0