  The emergence of the COVID-19 pandemic and the first global infodemic have
changed our lives in many different ways. We relied on social media to get the
latest information about the COVID-19 pandemic and at the same time to
disseminate information. The content in social media consisted not only health
related advises, plans, and informative news from policy makers, but also
contains conspiracies and rumors. It became important to identify such
information as soon as they are posted to make actionable decisions (e.g.,
debunking rumors, or taking certain measures for traveling). To address this
challenge, we develop and publicly release the first largest manually annotated
Arabic tweet dataset, ArCovidVac, for the COVID-19 vaccination campaign,
covering many countries in the Arab region. The dataset is enriched with
different layers of annotation, including, (i) Informativeness (more vs. less
importance of the tweets); (ii) fine-grained tweet content types (e.g., advice,
rumors, restriction, authenticate news/information); and (iii) stance towards
vaccination (pro-vaccination, neutral, anti-vaccination). Further, we performed
in-depth analysis of the data, exploring the popularity of different vaccines,
trending hashtags, topics and presence of offensiveness in the tweets. We
studied the data for individual types of tweets and temporal changes in stance
towards vaccine. We benchmarked the ArCovidVac dataset using transformer
architectures for informativeness, content types, and stance detection.
