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#COVID-19 Misinformation: Saudi Arabia as a Use Case

descriptionPublicationkeyboard_double_arrow_right Conference object , Other literature type 09 Jun 2020Publisher:Zenodo

Authors: Alasmari, Ashwag; Addawood, Aseel; Nouh, Mariam; Feldman, Philip; Al-Wabil, Areej;

doi: 10.5281/zenodo.3932069 , 10.5281/zenodo.3932070 , 10.5281/zenodo.3932081

#COVID-19 Misinformation: Saudi Arabia as a Use Case

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Abstract

In this research, we aim to gain deep insights into information behavior when discussing epidemics on Twitter. We are more specifically interested in identifying misinformation spread regarding epidemics around the world and in particular, in Saudi Arabia. We are also interested in understanding the effects of governmental laws as a way to prevent the spread of misinformation. Questions to be answered include: Which types of misinformation spread the most in pandemics? How does misinformation evolve over time? What is the effect of governmental laws in the spread of misinformation? We propose a mixed method study to determine if information-exchanging behaviors can be used to minimize the effects of emergent misinformation. To do this, Twitter data was collected for the period beginning in December 2019 to the present day of April 10, 2020 using several keywords related to the pandemic in Arabic. In addition to twitter data, we also created a short survey to collect rumors and fake news that spread in the community. To study the effect of governmental Saudi laws in the spread of misinformation, the misinformation laws were collected.

Related Organizations

University of Maryland, Baltimore
United States
Imam Muhammad ibn Saud Islamic University
Saudi Arabia
King Abdulaziz City for Science and Technology
Saudi Arabia

Keywords

Misinformation, Saudi Arabia, Governmental Laws

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	1
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average