ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination

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 advice, 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 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.
Anthology ID:
2022.lrec-1.344
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
3220–3230
Language:
URL:
https://aclanthology.org/2022.lrec-1.344
DOI:
Bibkey:
Cite (ACL):
Hamdy Mubarak, Sabit Hassan, Shammur Absar Chowdhury, and Firoj Alam. 2022. ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 3220–3230, Marseille, France. European Language Resources Association.
Cite (Informal):
ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination (Mubarak et al., LREC 2022)
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PDF:
https://aclanthology.org/2022.lrec-1.344.pdf