{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:33:18Z","timestamp":1772119998136,"version":"3.50.1"},"reference-count":104,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T00:00:00Z","timestamp":1707177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>The work presented in this paper makes multiple scientific contributions with a specific focus on the analysis of misinformation about COVID-19 on YouTube. First, the results of topic modeling performed on the video descriptions of YouTube videos containing misinformation about COVID-19 revealed four distinct themes or focus areas\u2014Promotion and Outreach Efforts, Treatment for COVID-19, Conspiracy Theories Regarding COVID-19, and COVID-19 and Politics. Second, the results of topic-specific sentiment analysis revealed the sentiment associated with each of these themes. For the videos belonging to the theme of Promotion and Outreach Efforts, 45.8% were neutral, 39.8% were positive, and 14.4% were negative. For the videos belonging to the theme of Treatment for COVID-19, 38.113% were positive, 31.343% were neutral, and 30.544% were negative. For the videos belonging to the theme of Conspiracy Theories Regarding COVID-19, 46.9% were positive, 31.0% were neutral, and 22.1% were negative. For the videos belonging to the theme of COVID-19 and Politics, 35.70% were positive, 32.86% were negative, and 31.44% were neutral. Third, topic-specific language analysis was performed to detect the various languages in which the video descriptions for each topic were published on YouTube. This analysis revealed multiple novel insights. For instance, for all the themes, English and Spanish were the most widely used and second most widely used languages, respectively. Fourth, the patterns of sharing these videos on other social media channels, such as Facebook and Twitter, were also investigated. The results revealed that videos containing video descriptions in English were shared the highest number of times on Facebook and Twitter. Finally, correlation analysis was performed by taking into account multiple characteristics of these videos. The results revealed that the correlation between the length of the video title and the number of tweets and the correlation between the length of the video title and the number of Facebook posts were statistically significant.<\/jats:p>","DOI":"10.3390\/computation12020028","type":"journal-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T05:36:43Z","timestamp":1707197803000},"page":"28","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Investigation of the Misinformation about COVID-19 on YouTube Using Topic Modeling, Sentiment Analysis, and Language Analysis"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3225-1870","authenticated-orcid":false,"given":"Nirmalya","family":"Thakur","sequence":"first","affiliation":[{"name":"Department of Computer Science, Emory University, Atlanta, GA 30322, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0522-8094","authenticated-orcid":false,"given":"Shuqi","family":"Cui","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Emory University, Atlanta, GA 30322, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3126-5637","authenticated-orcid":false,"given":"Victoria","family":"Knieling","sequence":"additional","affiliation":[{"name":"Program in Linguistics, Emory University, Atlanta, GA 30322, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1842-4583","authenticated-orcid":false,"given":"Karam","family":"Khanna","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Emory University, Atlanta, GA 30322, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0354-4265","authenticated-orcid":false,"given":"Mingchen","family":"Shao","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Emory University, Atlanta, GA 30322, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"727","DOI":"10.1056\/NEJMoa2001017","article-title":"A Novel Coronavirus from Patients with Pneumonia in China, 2019","volume":"382","author":"Zhu","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1038\/s41586-020-2012-7","article-title":"A Pneumonia Outbreak Associated with a New Coronavirus of Probable Bat Origin","volume":"579","author":"Zhou","year":"2020","journal-title":"Nature"},{"key":"ref_3","unstructured":"(2023, December 09). WHO Coronavirus (COVID-19) Dashboard. Available online: https:\/\/covid19.who.int\/."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Miraz, M.H., Ali, M., Excell, P.S., and Picking, R. (2015, January 8\u201311). A Review on Internet of Things (IoT), Internet of Everything (IoE) and Internet of Nano Things (IoNT). Proceedings of the 2015 Internet Technologies and Applications (ITA), Wrexham, UK.","DOI":"10.1109\/ITechA.2015.7317398"},{"key":"ref_5","unstructured":"Bujnowska-Fedak, M.M., Walig\u00f3ra, J., and Mastalerz-Migas, A. (2019). Advances in Experimental Medicine and Biology, Springer."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e34790","DOI":"10.2196\/34790","article-title":"Online Health Information Seeking Behaviors among Older Adults: Systematic Scoping Review","volume":"24","author":"Zhao","year":"2022","journal-title":"J. Med. Internet Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1177\/1460458213512220","article-title":"Healthcare Information on YouTube: A Systematic Review","volume":"21","author":"Madathil","year":"2015","journal-title":"Health Inform. J."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e6","DOI":"10.2196\/ijmr.2465","article-title":"Identifying Measures Used for Assessing Quality of YouTube Videos with Patient Health Information: A Review of Current Literature","volume":"2","author":"Gabarron","year":"2013","journal-title":"Interact. J. Med. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e002604","DOI":"10.1136\/bmjgh-2020-002604","article-title":"YouTube as a Source of Information on COVID-19: A Pandemic of Misinformation?","volume":"5","author":"Li","year":"2020","journal-title":"BMJ Glob. Health"},{"key":"ref_10","unstructured":"Tomlein, M., Pecher, B., Simko, J., Srba, I., Moro, R., Stefancova, E., Kompan, M., Hrckova, A., Podrouzek, J., and Bielikova, M. (2021). Proceedings of the Fifteenth ACM Conference on Recommender Systems, ACM."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3568392","article-title":"Auditing YouTube\u2019s Recommendation Algorithm for Misinformation Filter Bubbles","volume":"1","author":"Srba","year":"2023","journal-title":"ACM Trans. Recomm. Syst."},{"key":"ref_12","unstructured":"Kirdemir, B., and Agarwal, N. (2022). Complex Networks & Their Applications X, Springer."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Osman, W., Mohamed, F., Elhassan, M., and Shoufan, A. (2022). Is YouTube a Reliable Source of Health-Related Information? A Systematic Review. BMC Med. Educ., 22.","DOI":"10.1186\/s12909-022-03446-z"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1177\/22925503211064382","article-title":"Entering the Misinformation Age: Quality and Reliability of YouTube for Patient Information on Liposuction","volume":"31","author":"Chawla","year":"2023","journal-title":"Plast. Surg. (Oakv.)"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"85","DOI":"10.3233\/BD-200445","article-title":"YouTube: Searching for Answers about Breast Cancer","volume":"39","author":"Brachtenbach","year":"2020","journal-title":"Breast Dis."},{"key":"ref_16","unstructured":"Hou, R., Perez-Rosas, V., Loeb, S., and Mihalcea, R. (2019). Proceedings of the 2019 International Conference on Multimodal Interaction, ACM."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1007\/s00296-021-04813-7","article-title":"YouTube as a Source of Information on Gout: A Quality Analysis","volume":"41","author":"Onder","year":"2021","journal-title":"Rheumatol. Int."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1007\/s15010-020-01516-2","article-title":"COVID-19 Outbreak: History, Mechanism, Transmission, Structural Studies and Therapeutics","volume":"49","author":"Yesudhas","year":"2021","journal-title":"Infection"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"779","DOI":"10.1126\/science.abb7498","article-title":"Structure of the RNA-Dependent RNA Polymerase from COVID-19 Virus","volume":"368","author":"Gao","year":"2020","journal-title":"Science"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.jare.2020.03.005","article-title":"COVID-19 Infection: Emergence, Transmission, and Characteristics of Human Coronaviruses","volume":"24","author":"Shereen","year":"2020","journal-title":"J. Adv. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1016\/j.jinf.2020.02.018","article-title":"Characteristics of COVID-19 Infection in Beijing","volume":"80","author":"Tian","year":"2020","journal-title":"J. Infect."},{"key":"ref_22","first-page":"E304","article-title":"Determine the Most Common Clinical Symptoms in COVID-19 Patients: A Systematic Review and Meta-Analysis","volume":"61","author":"Alimohamadi","year":"2020","journal-title":"J. Prev. Med. Hyg."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"473","DOI":"10.3389\/fpubh.2020.00473","article-title":"Modeling the Onset of Symptoms of COVID-19","volume":"8","author":"Larsen","year":"2020","journal-title":"Front. Public Health"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1080\/10584609.2020.1716500","article-title":"Defining Misinformation and Understanding Its Bounded Nature: Using Expertise and Evidence for Describing Misinformation","volume":"37","author":"Vraga","year":"2020","journal-title":"Polit. Commun."},{"key":"ref_25","unstructured":"Cook, J., Ecker, U., and Lewandowsky, S. (2015). Emerging Trends in the Social and Behavioral Sciences, John Wiley & Sons, Inc."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1038\/s41591-022-01713-6","article-title":"Misinformation: Susceptibility, Spread, and Interventions to Immunize the Public","volume":"28","year":"2022","journal-title":"Nat. Med."},{"key":"ref_27","unstructured":"Almaliki, M. (2019). Proceedings of the 2019 3rd International Conference on Information System and Data Mining, ACM."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1073\/pnas.1517441113","article-title":"The Spreading of Misinformation Online","volume":"113","author":"Bessi","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_29","unstructured":"Proctor, R., and Schiebinger, L.L. (2008). Agnotology: The Making and Unmaking of Ignorance, Stanford University Press."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1093\/eurpub\/ckn139","article-title":"Denialism: What Is It and How Should Scientists Respond?","volume":"19","author":"Diethelm","year":"2008","journal-title":"Eur. J. Public Health"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"205630512211504","DOI":"10.1177\/20563051221150412","article-title":"Misinformation on Misinformation: Conceptual and Methodological Challenges","volume":"9","author":"Altay","year":"2023","journal-title":"Soc. Media Soc."},{"key":"ref_32","unstructured":"(2023, December 10). Biggest Social Media Platforms. Available online: https:\/\/www.statista.com\/statistics\/272014\/global-social-networks-ranked-by-number-of-users\/."},{"key":"ref_33","unstructured":"(2023, December 10). Top Websites in the World\u2014Top Rankings October 2023. Available online: https:\/\/www.semrush.com\/website\/top\/."},{"key":"ref_34","unstructured":"Mohsin, M. (2023, December 10). 10 YouTube Statistics That You Need to Know in 2023. Available online: https:\/\/www.oberlo.com\/blog\/youtube-statistics."},{"key":"ref_35","unstructured":"(2023, December 10). Countries with the Highest Monthly Traffic Volume to Youtube.com. Available online: https:\/\/www.statista.com\/statistics\/1357163\/youtube-global-monthly-visits-by-country\/."},{"key":"ref_36","unstructured":"Blogger, G.M.I. (2023, December 10). YouTube Statistics 2023 [Users by Country + Demographics]. Available online: https:\/\/www.globalmediainsight.com\/blog\/youtube-users-statistics\/."},{"key":"ref_37","unstructured":"(2023, December 10). Average Daily Time Spent on Social Media (Latest 2023 Data). Available online: https:\/\/www.broadbandsearch.net\/blog\/average-daily-time-on-social-media."},{"key":"ref_38","unstructured":"(2023, December 10). YouTube App User Engagement in Selected Markets. Available online: https:\/\/www.statista.com\/statistics\/1287283\/time-spent-youtube-app-selected-countries\/."},{"key":"ref_39","unstructured":"(2023, December 10). ChannelMeter YouTube\u2019s Top Countries. Available online: https:\/\/medium.com\/@ChannelMeter\/youtubes-top-countries-47b0d26dded."},{"key":"ref_40","unstructured":"(2023, December 10). Global YouTube User Distribution by Gender. Available online: https:\/\/www.statista.com\/statistics\/1287032\/distribution-youtube-users-gender."},{"key":"ref_41","unstructured":"(2023, December 10). Global YouTube User Age & Gender Distribution. Available online: https:\/\/www.statista.com\/statistics\/1287137\/youtube-global-users-age-gender-distribution\/."},{"key":"ref_42","unstructured":"(2023, December 10). YouTube Penetration in Selected Countries and Territories. Available online: https:\/\/www.statista.com\/statistics\/1219589\/youtube-penetration-worldwide-by-country\/."},{"key":"ref_43","unstructured":"Posner, M. (2023, December 10). YouTube Amplifies Misinformation and Hatred, but Here\u2019s What We Can Do about It. Available online: https:\/\/www.forbes.com\/sites\/michaelposner\/2022\/06\/17\/youtube-amplifies-misinformation-and-hatred-but-heres-what-we-can-do-about-it\/?sh=496312b51592."},{"key":"ref_44","unstructured":"Milmo, D. (2023, December 10). YouTube Is Major Conduit of Fake News, Factcheckers Say. Available online: https:\/\/www.theguardian.com\/technology\/2022\/jan\/12\/youtube-is-major-conduit-of-fake-news-factcheckers-say."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e1","DOI":"10.1016\/j.amepre.2009.11.007","article-title":"YouTube as a Source of Information on the H1N1 Influenza Pandemic","volume":"38","author":"Pandey","year":"2010","journal-title":"Am. J. Prev. Med."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"306","DOI":"10.4103\/1947-2714.161244","article-title":"Youtube as a Source of Information on Ebola Virus Disease","volume":"7","author":"Pathak","year":"2015","journal-title":"N. Am. J. Med. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1080\/20477724.2018.1507784","article-title":"Are Internet Videos Useful Sources of Information during Global Public Health Emergencies? A Case Study of YouTube Videos during the 2015\u201316 Zika Virus Pandemic","volume":"112","author":"Bora","year":"2018","journal-title":"Pathog. Glob. Health"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1080\/17441692.2020.1761426","article-title":"YouTube as a Source of Medical Information on the Novel Coronavirus 2019 Disease (COVID-19) Pandemic","volume":"15","author":"Strand","year":"2020","journal-title":"Glob. Public Health"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"e29942","DOI":"10.2196\/29942","article-title":"The Reliability and Quality of YouTube Videos as a Source of Public Health Information Regarding COVID-19 Vaccination: Cross-Sectional Study","volume":"7","author":"Chan","year":"2021","journal-title":"JMIR Public Health Surveill."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3392854","article-title":"Measuring Misinformation in Video Search Platforms: An Audit Study on YouTube","volume":"4","author":"Hussein","year":"2020","journal-title":"Proc. ACM Hum. Comput. Interact."},{"key":"ref_51","unstructured":"Juneja, P., Bhuiyan, M.M., and Mitra, T. (2023). Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems, ACM."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e39571","DOI":"10.2196\/39571","article-title":"Understanding the Social Mechanism of Cancer Misinformation Spread on YouTube and Lessons Learned: Infodemiological Study","volume":"24","author":"Yoon","year":"2022","journal-title":"J. Med. Internet Res."},{"key":"ref_53","first-page":"19","article-title":"Comparative Approaches to Mis\/Disinformation| Fighting Zika with Honey: An Analysis of YouTube\u2019s Video Recommendations on Brazilian YouTube","volume":"15","author":"Kaiser","year":"2021","journal-title":"Int. J. Commun."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"e23262","DOI":"10.2196\/23262","article-title":"\u201cDown the Rabbit Hole\u201d of Vaccine Misinformation on YouTube: Network Exposure Study","volume":"23","author":"Tang","year":"2021","journal-title":"J. Med. Internet Res."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1111\/bju.14971","article-title":"Information on Surgical Treatment of Benign Prostatic Hyperplasia on YouTube Is Highly Biased and Misleading","volume":"125","author":"Betschart","year":"2020","journal-title":"BJU Int."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Qi, J., Trang, T., Doong, J., Kang, S., and Chien, A.L. (2016). Misinformation Is Prevalent in Psoriasis-Related YouTube Videos. Dermatol. Online J., 22.","DOI":"10.5070\/D32211033142"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1016\/j.eururo.2018.10.056","article-title":"Dissemination of Misinformative and Biased Information about Prostate Cancer on YouTube","volume":"75","author":"Loeb","year":"2019","journal-title":"Eur. Urol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1513\/AnnalsATS.201809-644OC","article-title":"YouTube Videos as a Source of Misinformation on Idiopathic Pulmonary Fibrosis","volume":"16","author":"Goobie","year":"2019","journal-title":"Ann. Am. Thorac. Soc."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"e37546","DOI":"10.2196\/37546","article-title":"Misinformation about the Human Gut Microbiome in YouTube Videos: Cross-Sectional Study","volume":"6","author":"Chidambaram","year":"2022","journal-title":"JMIR Form. Res."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1007\/s00345-021-03623-7","article-title":"An Analysis of Misleading YouTube Videos on Urological Conditions: What to Do about the Danger of Spreading Misinformation of the YouTube Videos?","volume":"40","author":"Selvi","year":"2022","journal-title":"World J. Urol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1007\/s43545-021-00065-1","article-title":"The Momo Challenge: Measuring the Extent to Which YouTube Portrays Harmful and Helpful Depictions of a Suicide Game","volume":"1","author":"Kobilke","year":"2021","journal-title":"SN Soc. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1177\/1750481321989838","article-title":"Ambient Affiliation, Misinformation and Moral Panic: Negotiating Social Bonds in a YouTube Internet Hoax","volume":"15","author":"Inwood","year":"2021","journal-title":"Discourse Commun."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"e49220","DOI":"10.2196\/49220","article-title":"Appraising Unmet Needs and Misinformation Spread about Polycystic Ovary Syndrome in 85,872 YouTube Comments over 12 Years: Big Data Infodemiology Study","volume":"25","author":"Malhotra","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.urology.2022.06.030","article-title":"Examination of Information and Misinformation about Urinary Tract Infections on TikTok and YouTube","volume":"168","author":"Tam","year":"2022","journal-title":"Urology"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1016\/j.euf.2019.11.011","article-title":"Fake News: Spread of Misinformation about Urological Conditions on Social Media","volume":"6","author":"Loeb","year":"2020","journal-title":"Eur. Urol. Focus"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"e229","DOI":"10.2196\/jmir.9959","article-title":"Misleading Claims about Tobacco Products in YouTube Videos: Experimental Effects of Misinformation on Unhealthy Attitudes","volume":"20","author":"Albarracin","year":"2018","journal-title":"J. Med. Internet Res."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"e008334","DOI":"10.1136\/bmjgh-2021-008334","article-title":"YouTube as a Source of Misinformation on COVID-19 Vaccination: A Systematic Analysis","volume":"7","author":"Li","year":"2022","journal-title":"BMJ Glob. Health"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"223","DOI":"10.15581\/003.35.2.223-238","article-title":"COVID-19 Vaccine Disinformation on YouTube: Analysis of a Viewing Network","volume":"35","author":"Calvo","year":"2022","journal-title":"Commun. Soc."},{"key":"ref_69","first-page":"e8622","article-title":"YouTube as a Source of Medical and Epidemiological Information during COVID-19 Pandemic: A Cross-Sectional Study of Content across Six Languages around the Globe","volume":"12","author":"Dutta","year":"2020","journal-title":"Cureus"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1654","DOI":"10.1080\/21645515.2018.1454572","article-title":"Misinformation on Vaccination: A Quantitative Analysis of YouTube Videos","volume":"14","author":"Donzelli","year":"2018","journal-title":"Hum. Vaccin. Immunother."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Tokojima Machado, D.F., Fioravante de Siqueira, A., Rallo Shimizu, N., and Gitahy, L. (2022). It-Which-Must-Not-Be-Named: COVID-19 Misinformation, Tactics to Profit from It and to Evade Content Moderation on YouTube. Front. Commun., 7.","DOI":"10.3389\/fcomm.2022.1037432"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"e28352","DOI":"10.2196\/28352","article-title":"YouTube Videos and Informed Decision-Making about COVID-19 Vaccination: Successive Sampling Study","volume":"7","author":"Basch","year":"2021","journal-title":"JMIR Public Health Surveill."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"e32452","DOI":"10.2196\/32452","article-title":"COVID-19 and Vitamin D Misinformation on YouTube: Content Analysis","volume":"2","author":"Quinn","year":"2022","journal-title":"JMIR Infodemiol."},{"key":"ref_74","unstructured":"Verspoor, K., Cohen, K.B., Dredze, M., Ferrara, E., May, J., Munro, R., Paris, C., and Wallace, B. (2020). Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, Association for Computational Linguistics."},{"key":"ref_75","unstructured":"Christodoulou, C., Salamanos, N., Leonidou, P., Papadakis, M., and Sirivianos, M. (2023). Identifying Misinformation on YouTube through Transcript Contextual Analysis with Transformer Models. arXiv."},{"key":"ref_76","unstructured":"Xie, J., Chai, Y., and Liu, X. (2022). Proceedings of the 55th Annual Hawaii International Conference on System Sciences, HICSS 2022, IEEE Computer Society."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"101582","DOI":"10.1016\/j.is.2020.101582","article-title":"A Review of Topic Modeling Methods","volume":"94","author":"Vayansky","year":"2020","journal-title":"Inf. Syst."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"e45108","DOI":"10.2196\/45108","article-title":"Mpox Panic, Infodemic, and Stigmatization of the Two-Spirit, Lesbian, Gay, Bisexual, Transgender, Queer or Questioning, Intersex, Asexual Community: Geospatial Analysis, Topic Modeling, and Sentiment Analysis of a Large, Multilingual Social Media Database","volume":"25","author":"Bragazzi","year":"2023","journal-title":"J. Med. Internet Res."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1093\/jamia\/ocz191","article-title":"Mining Twitter to Assess the Determinants of Health Behavior toward Human Papillomavirus Vaccination in the United States","volume":"27","author":"Zhang","year":"2020","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Pruss, D., Fujinuma, Y., Daughton, A.R., Paul, M.J., Arnot, B., Albers Szafir, D., and Boyd-Graber, J. (2019). Zika Discourse in the Americas: A Multilingual Topic Analysis of Twitter. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0216922"},{"key":"ref_81","first-page":"7","article-title":"What Can We Learn about the Middle East Respiratory Syndrome (MERS) Outbreak from Tweets?","volume":"2","author":"Odlum","year":"2017","journal-title":"Big Data Inf. Anal."},{"key":"ref_82","unstructured":"Missier, P., Romanovsky, A., Miu, T., Pal, A., Daniilakis, M., Garcia, A., Cedrim, D., and da Silva Sousa, L. (2016). Current Trends in Web Engineering, Springer."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Chen, L., Hossain, K.S.M.T., Butler, P., Ramakrishnan, N., and Prakash, B.A. (2014, January 14\u201317). Flu Gone Viral: Syndromic Surveillance of Flu on Twitter Using Temporal Topic Models. Proceedings of the 2014 IEEE International Conference on Data Mining, Shenzhen, China.","DOI":"10.1109\/ICDM.2014.137"},{"key":"ref_84","unstructured":"Knuutila, A. (2023, December 11). A Dataset of Covid-Related Misinformation Videos and Their Spread on Social Media. Available online: https:\/\/zenodo.org\/records\/4557828."},{"key":"ref_85","unstructured":"(2023, December 18). Package Google.Cloud.Translation.V3. Available online: https:\/\/cloud.google.com\/translate\/docs\/reference\/rpc\/google.cloud.translation.v3."},{"key":"ref_86","first-page":"993","article-title":"Latent Dirichlet Allocation","volume":"3","author":"Blei","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"15169","DOI":"10.1007\/s11042-018-6894-4","article-title":"Latent Dirichlet Allocation (LDA) and Topic Modeling: Models, Applications, a Survey. Multimed","volume":"78","author":"Jelodar","year":"2019","journal-title":"Tools Appl."},{"key":"ref_88","unstructured":"Wei, X., and Croft, W.B. (2006). Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM."},{"key":"ref_89","first-page":"857","article-title":"Stochastic neighbor embedding","volume":"15","author":"Hinton","year":"2003","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_90","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1007\/s10618-011-0238-6","article-title":"Survey on Mining Subjective Data on the Web","volume":"24","author":"Tsytsarau","year":"2012","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_92","unstructured":"Saberi, B., and Saad, S. (2023, December 11). Sentiment Analysis or Opinion Mining: A Review. Available online: https:\/\/core.ac.uk\/download\/pdf\/296919524.pdf."},{"key":"ref_93","unstructured":"Liu, B. (2022). Sentiment Analysis and Opinion Mining, Springer."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1093","DOI":"10.1016\/j.asej.2014.04.011","article-title":"Sentiment Analysis Algorithms and Applications: A Survey","volume":"5","author":"Medhat","year":"2014","journal-title":"Ain Shams Eng. J."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Wilson, T. (2005, January 6\u20138). Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis. Proceedings of the HLT\/EMNLP\u201905, Vancouver, BC, Canada.","DOI":"10.3115\/1220575.1220619"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.eswa.2018.10.003","article-title":"Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review","volume":"118","author":"Do","year":"2019","journal-title":"Expert Syst. Appl."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1109\/TAFFC.2020.2970399","article-title":"Issues and Challenges of Aspect-Based Sentiment Analysis: A Comprehensive Survey","volume":"13","author":"Nazir","year":"2022","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1609\/icwsm.v8i1.14550","article-title":"VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text","volume":"8","author":"Hutto","year":"2014","journal-title":"Proc. Int. AAAI Conf. Web Soc. Media"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Veena, G., Vinayak, A., and Nair, A.J. (2021, January 1\u20133). Sentiment Analysis Using Improved Vader and Dependency Parsing. Proceedings of the 2021 2nd Global Conference for Advancement in Technology (GCAT), Bangalore, India.","DOI":"10.1109\/GCAT52182.2021.9587829"},{"key":"ref_100","first-page":"7","article-title":"Comparative Study of Sentiment Analysis with Product Reviews Using Machine Learning and Lexicon-Based Approaches","volume":"1","author":"Nguyen","year":"2018","journal-title":"SMU Data Sci. Rev."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"706","DOI":"10.1016\/B978-0-12-809633-8.20358-0","article-title":"Correlation Analysis","volume":"Volume 1\u20133","author":"Franzese","year":"2019","journal-title":"Encyclopedia of Bioinformatics and Computational Biology"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"e4483","DOI":"10.1136\/bmj.e4483","article-title":"Pearson\u2019s Correlation Coefficient","volume":"345","author":"Sedgwick","year":"2012","journal-title":"BMJ"},{"key":"ref_103","unstructured":"Zhang, L., Tong, Y., and Ji, Q. (2008). Lecture Notes in Computer Science, Springer."},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Woods, D.D., Dekker, S., Cook, R., Johannesen, L., and Sarter, N. (2017). Behind Human Error, CRC Press. 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