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Analyzing Sentiments and Diffusion Characteristics of COVID-19 Vaccine Misinformation Topics in Social Media: A Data Analytics Framework

Analyzing Sentiments and Diffusion Characteristics of COVID-19 Vaccine Misinformation Topics in Social Media: A Data Analytics Framework

Mohammad Daradkeh
Copyright: © 2022 |Volume: 9 |Issue: 3 |Pages: 22
ISSN: 2334-4547|EISSN: 2334-4555|EISBN13: 9781683182894|DOI: 10.4018/IJBAN.292056
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MLA

Daradkeh, Mohammad. "Analyzing Sentiments and Diffusion Characteristics of COVID-19 Vaccine Misinformation Topics in Social Media: A Data Analytics Framework." IJBAN vol.9, no.3 2022: pp.1-22. http://doi.org/10.4018/IJBAN.292056

APA

Daradkeh, M. (2022). Analyzing Sentiments and Diffusion Characteristics of COVID-19 Vaccine Misinformation Topics in Social Media: A Data Analytics Framework. International Journal of Business Analytics (IJBAN), 9(3), 1-22. http://doi.org/10.4018/IJBAN.292056

Chicago

Daradkeh, Mohammad. "Analyzing Sentiments and Diffusion Characteristics of COVID-19 Vaccine Misinformation Topics in Social Media: A Data Analytics Framework," International Journal of Business Analytics (IJBAN) 9, no.3: 1-22. http://doi.org/10.4018/IJBAN.292056

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Abstract

This study presents a data analytics framework that aims to analyze topics and sentiments associated with COVID-19 vaccine misinformation in social media. A total of 40,359 tweets related to COVID-19 vaccination were collected between January 2021 and March 2021. Misinformation was detected using multiple predictive machine learning models. Latent Dirichlet Allocation (LDA) topic model was used to identify dominant topics in COVID-19 vaccine misinformation. Sentiment orientation of misinformation was analyzed using a lexicon-based approach. An independent-samples t-test was performed to compare the number of replies, retweets, and likes of misinformation with different sentiment orientations. Based on the data sample, the results show that COVID-19 vaccine misinformation included 21 major topics. Across all misinformation topics, the average number of replies, retweets, and likes of tweets with negative sentiment was 2.26, 2.68, and 3.29 times higher, respectively, than those with positive sentiment.