Understanding COVID-19 Vaccine Campaign on Facebook using Minimal Supervision
October 18, 2022 ยท Declared Dead ยท ๐ 2022 IEEE International Conference on Big Data (Big Data)
"No code URL or promise found in abstract"
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Authors
Tunazzina Islam, Dan Goldwasser
arXiv ID
2210.10031
Category
cs.CL: Computation & Language
Cross-listed
cs.CY,
cs.LG,
cs.SI
Citations
13
Venue
2022 IEEE International Conference on Big Data (Big Data)
Last Checked
5 months ago
Abstract
In the age of social media, where billions of internet users share information and opinions, the negative impact of pandemics is not limited to the physical world. It provokes a surge of incomplete, biased, and incorrect information, also known as an infodemic. This global infodemic jeopardizes measures to control the pandemic by creating panic, vaccine hesitancy, and fragmented social response. Platforms like Facebook allow advertisers to adapt their messaging to target different demographics and help alleviate or exacerbate the infodemic problem depending on their content. In this paper, we propose a minimally supervised multi-task learning framework for understanding messaging on Facebook related to the COVID vaccine by identifying ad themes and moral foundations. Furthermore, we perform a more nuanced thematic analysis of messaging tactics of vaccine campaigns on social media so that policymakers can make better decisions on pandemic control.
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