Name: ArCovidVac
Description: the largest manually annotated Arabic tweet dataset, ArCovidVac, for the COVID-19 vaccination campaign, covering many countries in the Arab region
Provider: QCRI
Paper Title: ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination
Tasks: informativeness, text classification, stance detection
Venue Name: Language Resources and Evaluation Conference
Authors: Hamdy Mubarak, Sabit Hassan, Shammur Absar Chowdhury, Firoj Alam
Abstract: 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 COVID-19 pandemic and at the same time to disseminate information.
  The content in social media consisted not only health related advise, plans, and informative news from policymakers, but also
  contains conspiracies and rumors. It became important to identify such information as soon as they are posted to make an
  actionable decision (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.