Exploring Social Media Misinformation in the COVID-19 Pandemic Using a Convolutional Neural Network

Alexander J. Little, Zhijie Sasha Dong (), Andrew H. Little and Guo Qiu
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Alexander J. Little: Ingram School of Engineering, Texas State University
Zhijie Sasha Dong: Ingram School of Engineering, Texas State University
Andrew H. Little: Network Surveillance Engineering, Consolidated Communications
Guo Qiu: Ingram School of Engineering, Texas State University

A chapter in AI and Analytics for Public Health, 2022, pp 443-452 from Springer

Abstract: Abstract Misinformation is rampant in the modern information age and understanding how social media misinformation diffuses can provide vital insight on how to combat it. With social media becoming a major information source, it is increasingly important to address this concern. Social media misinformation has negatively impacted healthcare response in the past and may have played a major role in how to respond to COVID-19. Understanding how misinformation diffuses through online social networks can provide help healthcare and government entities information on how to mitigate the associated negative impact. This paper proposes a data set as criterion for identifying pandemic specific misinformation and develops a Convolution Neural Network model and. A case study is then conducted to illustrate how diffusion can be explored using labelled misinformation. The work shows a decrease of COVID-19 misinformation over time and a pattern that does not depend on regional geographic location.

Keywords: COVID-19 pandemic; Social media misinformation; Deep learning; Artificial intelligence (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-030-75166-1_33

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DOI: 10.1007/978-3-030-75166-1_33

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