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Title: :  An Approach to Quantify the COVID-19 Content in Online Health Opinion Conflicts
PaperId: :  16933
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 8 Issue 3 2022
DUI:    16.0415/IJARIIE-16933
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Varada AkashDepartment of Information Technology, Malla Reddy Engineering College
Reddy Srinitya, Department of Information Technology, Malla Reddy Engineering College

Abstract

Computer Science & Engineering
LDA Model, Anti Vaccine, Pro-Vaccine, COVID-19, Facebook, CNN, Random Forest.
A huge amount of potentially dangerous COVID-19 misinformation is appearing online. Here we use machine learning to quantify COVID-19 content among online opponents of establishment health guidance, in particular vaccinations (“anti-vax”). We find that the anti-vax community is developing a less focused debate around COVID-19 than its counterpart, the pro-vaccination (“pro-vax”) community. However, the anti-vax community exhibits a broader range of ``favors'' of COVID-19 topics, and hence can appeal to a broader cross-section of individuals seeking COVID-19 guidance online, e.g. individuals wary of a mandatory fast-tracked COVID-19 vaccine or those seeking alternative remedies. Hence the anti-vax community looks better positioned to attract fresh support going forward than the pro-vax community. This is concerning since a widespread lack of adoption of a COVID-19 vaccine will mean the world falls short of providing herd immunity, leaving countries open to future COVID-19 resurgences. We provide a mechanistic model that interprets these results and could help in assessing the likely efficacy of intervention strategies. Our approach is scalable and hence tackles the urgent problem facing social media platforms of having to analyze huge volumes of online health misinformation and disinformation

Citations

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IJARIIE Varada Akash, and Reddy Srinitya, . "An Approach to Quantify the COVID-19 Content in Online Health Opinion Conflicts" International Journal Of Advance Research And Innovative Ideas In Education Volume 8 Issue 3 2022 Page 2127-2133
MLA Varada Akash, and Reddy Srinitya, . "An Approach to Quantify the COVID-19 Content in Online Health Opinion Conflicts." International Journal Of Advance Research And Innovative Ideas In Education 8.3(2022) : 2127-2133.
APA Varada Akash, & Reddy Srinitya, . (2022). An Approach to Quantify the COVID-19 Content in Online Health Opinion Conflicts. International Journal Of Advance Research And Innovative Ideas In Education, 8(3), 2127-2133.
Chicago Varada Akash, and Reddy Srinitya, . "An Approach to Quantify the COVID-19 Content in Online Health Opinion Conflicts." International Journal Of Advance Research And Innovative Ideas In Education 8, no. 3 (2022) : 2127-2133.
Oxford Varada Akash, and Reddy Srinitya, . 'An Approach to Quantify the COVID-19 Content in Online Health Opinion Conflicts', International Journal Of Advance Research And Innovative Ideas In Education, vol. 8, no. 3, 2022, p. 2127-2133. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/An_Approach_to_Quantify_the_COVID_19_Content_in_Online_Health_Opinion_Conflicts_ijariie16933.pdf (Accessed : 15 December 2023).
Harvard Varada Akash, and Reddy Srinitya, . (2022) 'An Approach to Quantify the COVID-19 Content in Online Health Opinion Conflicts', International Journal Of Advance Research And Innovative Ideas In Education, 8(3), pp. 2127-2133IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/An_Approach_to_Quantify_the_COVID_19_Content_in_Online_Health_Opinion_Conflicts_ijariie16933.pdf (Accessed : 15 December 2023)
IEEE Varada Akash, and Reddy Srinitya, , "An Approach to Quantify the COVID-19 Content in Online Health Opinion Conflicts," International Journal Of Advance Research And Innovative Ideas In Education, vol. 8, no. 3, pp. 2127-2133, May-Jun 2022. [Online]. Available: https://ijariie.com/AdminUploadPdf/An_Approach_to_Quantify_the_COVID_19_Content_in_Online_Health_Opinion_Conflicts_ijariie16933.pdf [Accessed : 15 December 2023].
Turabian Varada Akash, and Reddy Srinitya, . "An Approach to Quantify the COVID-19 Content in Online Health Opinion Conflicts." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 8 number 3 (15 December 2023).
Vancouver Varada Akash, and Reddy Srinitya, . An Approach to Quantify the COVID-19 Content in Online Health Opinion Conflicts. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2022 [Cited : 15 December 2023]; 8(3) : 2127-2133. Available from: https://ijariie.com/AdminUploadPdf/An_Approach_to_Quantify_the_COVID_19_Content_in_Online_Health_Opinion_Conflicts_ijariie16933.pdf
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