Transformers to Fight the COVID-19 Infodemic
This work addresses the spread of misinformation during the COVID-19 pandemic for social media users and researchers, but it is incremental as it applies an existing method to a new dataset.
The paper tackled the problem of detecting false information in tweets related to COVID-19 across Arabic, Bulgarian, and English languages using transformers, achieving mean F1 scores of 0.707, 0.578, and 0.864 respectively and ranking 4th in a shared task.
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.