2020/12/02 by Hamdy Mubarak, Mubarak, Hamdy, Sabit Hassan +1
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Social and Information Networks (cs.SI) #cs.CL #cs.SI
paper · pdf · doi:10.48550/arxiv.2012.01462
arxiv created 2021/03/01 · arxiv updated 2021/03/02
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.