ArCorona: Analyzing Arabic Tweets in the Early Days of Coronavirus (COVID-19) Pandemic
This work is significant for policymakers and the public in the Arab region to understand public sentiment and combat misinformation related to COVID-19.
This paper analyzes Arabic tweets during the early COVID-19 pandemic to understand public behavior and identify misinformation. It presents the largest manually annotated dataset of Arabic COVID-19 tweets and develops machine learning and transformer-based models for classification.
Over the past few months, there were huge numbers of circulating tweets and discussions about Coronavirus (COVID-19) in the Arab region. It is important for policy makers and many people to identify types of shared tweets to better understand public behavior, topics of interest, requests from governments, sources of tweets, etc. It is also crucial to prevent spreading of rumors and misinformation about the virus or bad cures. To this end, we present the largest manually annotated dataset of Arabic tweets related to COVID-19. We describe annotation guidelines, analyze our dataset and build effective machine learning and transformer based models for classification.