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Towards Immunizing Infodemic: Comprehensive Study on Assessing the Role of Artificial Intelligence and COVID-19 Pandemic

DOI: 10.4236/jilsa.2022.143003, PP. 25-41

Keywords: Artificial Intelligence, Infodemic, Disinformation, COVID-19 Pandemic

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Abstract:

Artificial Intelligence (AI) technologies have intentionally and unintentionally been used to spread false information on all different types of subjects. Throughout the COVID-19 pandemic, there was a pool of different information that was being presented to the public, a lot of it contradicting one another. False information spreads regardless of whether there is intent to mislead or misinform whereas AI is not able to decipher what type of information it is pushing to the public is correct and what is not. This mass spread of information through online platforms has been coined as an Infodemic where it is considered a massive volume of information, both online and offline. It includes deliberate attempts to disseminate false information to undermine the public health response and advance alternative agendas of groups or individuals. An infodemic can be incredibly dangerous to society greatly affecting the ability of communities, societies, and countries to control and stop the pandemic due to the abundance of different information in combating the health crisis. This article assesses and evaluates the role of Artificial Intelligence (AI) technologies in helping to spread disinformation during the COVID-19 pandemic. It reviews and evaluates the information curation in modern media, the relationship between AI and disinformation, and the challenges of disinformation campaigns. It further outlines the impact of social media platforms on infodemic and their influence in spreading disinformation during the COVID-19 pandemic. This article analyzes several data mining studies that used different machine learning techniques to identify the influence of disinformation tactics on the COVID-19 pandemic associated with the Twitter platform. It further continues exploring the investigation of the number of influential tweets, the type of users, the levels of credibility of URLs, and the type and effect of social media bots. Finally, the authors assess and conclude how disinformation is widely prevalent throughout social media during the COVID-19 pandemic as well as illustrate the surveys that categorize the prevalence of users involved in the conversation about disinformation separated by country including the percentage of users posting tweets and retweeting news URLs, and the future work in combating the rapid disinformation campaigns and their ethical implication impact.

References

[1]  Pretz, K. (2021) Stop Calling Everything AI, Machine-Learning Pioneer Says Michael I. Jordan Explains Why Today’s Artificial-Intelligence Systems Aren’t Intelligent. IEEE Spectrum.
https://spectrum.ieee.org/stop-calling-everything-ai-machinelearning-pioneer-says
[2]  Van Scoy, L.J., Miller, E.L., Snyder, B., Wasserman, E., Chinchilli, V.M., Zgierska, A.E., Rabago, D., Lennon, C.L., Lipnick, D., Toyobo, O., Ruffin 4th, M.T. and Lennon, R.P. (2021) Knowledge, Perceptions, and Preferred Information Sources Related to COVID-19 Among Central Pennsylvania Adults Early in the Pandemic: A Mixed Methods Cross-Sectional Survey. The Annals of Family Medicine, 19, 293-301.
https://doi.org/10.1370/afm.2674
[3]  Fleming, N. (2020) Coronavirus False Information, and How Scientists Can Help to Fight It. Nature, 583, 155-156.
https://doi.org/10.1038/d41586-020-01834-3
https://www.nature.com/articles/d41586-020-01834-3.
[4]  Derrat, M. (2019) The Most Profound Moment in Gaming History.
https://www.youtube.com/watch?v=jIYBod0ge3Y&ab_channel=MaxDerrat
[5]  Dick, S. (2019) Artificial Intelligence. Harvard Data Science Review, No. 1.1.
https://doi.org/10.1162/99608f92.92fe150c
[6]  Kreps, S. (2020) The Role of Technology in Online False Information.
https://www.brookings.edu/wp-content/uploads/2020/06/The-role-of-technology-in-online-misinformation.pdf
[7]  Twitter Safety (2021) Updates to Our Work on the COVID-19 Vaccine False Information.
https://blog.twitter.com/en_us/topics/company/2021/updates-to-our-work-on-COVID-19-vaccine-misinformation
[8]  Lian, P. (2019) AI Is Helping Spread False Information Faster. How Can We Deal with That? ITU News.
https://aiforgood.itu.int/ai-is-helping-spread-misinformation-faster-how-can-we-deal-with-that/
[9]  Meta AI (2020) Here’s How We’re Using AI to Help Detect False Information.
https://ai.facebook.com/blog/heres-how-were-using-ai-to-help-detect-misinformation/
[10]  Slapakova, L. (2021) Towards an AI-Based Counter-Disinformation Framework.
https://www.rand.org/blog/2021/03/towards-an-ai-based-counter-disinformation-framework.html
[11]  Cox, K., Ogden, T., Jordan, V. and Paille, P. (2021) COVID-19, Disinformation and Hateful Extremism Literature Review Report. Prepared for the Commission for Countering Extremism (CCE). RAND Europe, Cambridge.
https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/993841/RAND_Europe_Final_Report_Hateful_Extremism_During_COVID-19_Final_accessible.pdf
[12]  Goel, A. and Gupta, L. (2020) Social Media in the Times of COVID-19. JCR: Journal of Clinical Rheumatology, 26, 220-223.
https://doi.org/10.1097/RHU.0000000000001508
[13]  Krittanawong, C., Narasimhan, B., Virk, H.U.H., Narasimhan, H., Hahn, J., Wang, Z., et al. (2020) Misinformation Dissemination in Twitter in the COVID-19 Era. The American Journal of Medicine, 133, 1367-1369.
https://doi.org/10.1016/j.amjmed.2020.07.012
[14]  Jamison, A.M., Broniatowski, D.A., Dredze, M., Sangraula, A., Smith, M.C. and Quinn, S.C. (2020) Not Just Conspiracy Theories: Vaccine Opponents and Proponents Add to the COVID-19 ‘Infodemic’ on Twitter. Harvard Kennedy School Misinformation Review.
https://doi.org/10.37016/mr-2020-38
[15]  Davis, C.A., Varol, O., Ferrara, E., Flammini, A. and Menczer, F. (2016) BotOrNot: A System to Evaluate Social Bots. Proceedings of the 25th International Conference Companion on World Wide Web (WWW’16 Companion), Montréal, 11-15 April 2016, 273-274.
https://doi.org/10.1145/2872518.2889302
[16]  Nuzhath, T., Tasnim, S., Sanjwal, R.K., Trisha, N. F., Rahman, M., Mahmud, S., et al. (2020, December 11). COVID-19 Vaccination Hesitancy, Misinformation and Conspiracy Theories on Social Media: A Content Analysis of Twitter Data. SocArXiv.
https://doi.org/10.31235/osf.io/vc9jb
[17]  Blei, D.M., Ng, A.Y. and Jordan, M.I. (2003) Latent dirichlet allocation. Journal of machine Learning Research, 18, 993-1022.
[18]  Surian, D., Nguyen, D.Q., Kennedy, G., Johnson, M., Coiera, E. and Dunn, A.G. (2016) Characterizing Twitter Discussions about HPV Vaccines Using Topic Modeling and Community Detection. Journal of Medical Internet Research, 18, e232.
https://doi.org/10.2196/jmir.6045
[19]  Shearer, E. (2021) More than Eight-in-Ten Americans Get News from Digital Devices. Pew Research Center.
https://pewrsr.ch/2MZqns7
[20]  Brown, S. (2020). MIT Sloan Research about Social Media, False Information, and Elections. MIT Sloan.
https://mitsloan.mit.edu/ideas-made-to-matter/mit-sloan-research-about-social-media-misinformation-and-elections
[21]  Allcott, H., Gentzkow, M. and Yu, C. (2019) Trends in the Diffusion of False Information on Social Media. Research & Politics, 6, Article ID: 205316801984855.
https://doi.org/10.1177/2053168019848554
[22]  Grinberg, N., Joseph, K., Friedland, L., Swire-Thompson, B. and Lazer, D. (2019) Fake News on Twitter during the 2016 U.S. Presidential Election. Science, 363, 374-378.
https://doi.org/10.1126/science.aau2706
[23]  PolitiFact (2017) Politifact’s Guide to Fake News Websites and What They Peddle. PolitiFact.
http://www.politifact.com/punditfact/article/2017/apr/20/politifacts-guide-fake-news-websites-and-what-they/
[24]  Silverman, C. (2016) Here Are 50 of the Biggest Fake News Hits on Facebook from 2016. BuzzFeed News.
http://www.buzzfeednews.com/article/craigsilverman/top-fake-news-of-2016
[25]  Silverman, C., Singer-Vine, J. and Vo, L.T. (2017) In Spite of the Crackdown, Fake News Publishers Are Still Earning Money from Major ad Networks. BuzzFeed News.
http://www.buzzfeednews.com/article/craigsilverman/fake-news-real-ads
[26]  Guess, A., Nyhan, B. and Reifler, J. (2018) Selective Exposure to Misinformation: Evidence from the Consumption of Fake News during the 2016 U.S. Presidential Campaign. Working Paper, European Research Council, Brussels.
https://about.fb.com/wp-content/uploads/2018/01/fake-news-2016.pdf
[27]  Schaedel, S. (2017) Websites That Post Fake and Satirical Stories. FactCheck.
http://www.factcheck.org/2017/07/websites-post-fake-satirical-stories/
[28]  Huang, B. and Carley, K.M. (2006) Disinformation and Misinformation on Twitter during the Novel Coronavirus Outbreak. ArXiv: abs/2006.04278.

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