{"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":1772119998387,"version":"3.50.1"},"reference-count":58,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T00:00:00Z","timestamp":1707955200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T00:00:00Z","timestamp":1707955200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Med Syst"],"DOI":"10.1007\/s10916-024-02047-1","type":"journal-article","created":{"date-parts":[[2024,2,15]],"date-time":"2024-02-15T08:02:47Z","timestamp":1707984167000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Text-Mining and Video Analytics of COVID-19 Narratives Shared by Patients on YouTube"],"prefix":"10.1007","volume":"48","author":[{"given":"Ranganathan","family":"Chandrasekaran","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karthik","family":"Konaraddi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sakshi S.","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Evangelos","family":"Moustakas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,2,15]]},"reference":[{"key":"2047_CR1","doi-asserted-by":"publisher","unstructured":"Tsao S-F, Chen H, Tisseverasinghe T, Yang Y, Li L, Butt ZA. What social media told us in the time of COVID-19: a scoping review. Lancet Digit Health [Internet]. 2021;3:e175\u201394. Available from: https:\/\/doi.org\/10.1016\/S2589-7500(20)30315-0","DOI":"10.1016\/S2589-7500(20)30315-0"},{"key":"2047_CR2","doi-asserted-by":"crossref","unstructured":"Saud M, Mashud M \u2019in, Ida R. Usage of social media during the pandemic: Seeking support and awareness about COVID-19 through social media platforms. J Public Aff [Internet]. 2020;e02417. Available from: https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1002\/pa.2417","DOI":"10.1002\/pa.2417"},{"key":"2047_CR3","doi-asserted-by":"publisher","unstructured":"Chan AKM, Nickson CP, Rudolph JW, Lee A, Joynt GM. Social media for rapid knowledge dissemination: early experience from the COVID-19 pandemic. Anaesthesia [Internet]. 2020;75:1579\u201382. Available from: https:\/\/doi.org\/10.1111\/anae.15057","DOI":"10.1111\/anae.15057"},{"key":"2047_CR4","doi-asserted-by":"publisher","unstructured":"D\u2019Souza RS, D\u2019Souza S, Strand N, Anderson A, Vogt MNP, Olatoye O. YouTube as a source of medical information on the novel coronavirus 2019 disease (COVID-19) pandemic. Glob Public Health [Internet]. 2020;15:935\u201342. Available from: https:\/\/doi.org\/10.1080\/17441692.2020.1761426","DOI":"10.1080\/17441692.2020.1761426"},{"key":"2047_CR5","doi-asserted-by":"publisher","unstructured":"Szmuda T, Syed MT, Singh A, Ali S, \u00d6zdemir C, S\u0142oniewski P. YouTube as a source of patient information for Coronavirus Disease (COVID-19): A content-quality and audience engagement analysis. Rev Med Virol [Internet]. 2020;30:e2132. Available from: https:\/\/doi.org\/10.1002\/rmv.2132","DOI":"10.1002\/rmv.2132"},{"key":"2047_CR6","doi-asserted-by":"publisher","unstructured":"Parabhoi L, Sahu RR, Dewey RS, Verma MK, Kumar Seth A, Parabhoi D. YouTube as a source of information during the Covid-19 pandemic: a content analysis of YouTube videos published during January to March 2020. BMC Med Inform Decis Mak [Internet]. 2021;21:255. Available from: https:\/\/doi.org\/10.1186\/s12911-021-01613-8","DOI":"10.1186\/s12911-021-01613-8"},{"key":"2047_CR7","doi-asserted-by":"publisher","unstructured":"Basch CE, Basch CH, Hillyer GC, Meleo-Erwin ZC, Zagnit EA. YouTube Videos and Informed Decision-Making About COVID-19 Vaccination: Successive Sampling Study. JMIR Public Health Surveill [Internet]. 2021;7:e28352. Available from: https:\/\/doi.org\/10.2196\/28352","DOI":"10.2196\/28352"},{"key":"2047_CR8","doi-asserted-by":"publisher","unstructured":"Basch CH, Hillyer GC, Meleo-Erwin ZC, Jaime C, Mohlman J, Basch CE. Preventive Behaviors Conveyed on YouTube to Mitigate Transmission of COVID-19: Cross-Sectional Study. JMIR Public Health Surveill [Internet]. 2020;6:e18807. Available from: https:\/\/doi.org\/10.2196\/18807","DOI":"10.2196\/18807"},{"key":"2047_CR9","doi-asserted-by":"publisher","unstructured":"Kessler SH, Humprecht E. COVID-19 misinformation on YouTube: An analysis of its impact and subsequent online information searches for verification. Digit Health [Internet]. 2023;9:20552076231177131. Available from: https:\/\/doi.org\/10.1177\/20552076231177131","DOI":"10.1177\/20552076231177131"},{"key":"2047_CR10","doi-asserted-by":"crossref","unstructured":"Li HO-Y, Bailey A, Huynh D, Chan J. YouTube as a source of information on COVID-19: a pandemic of misinformation? BMJ Glob Health [Internet]. 2020;5:e002604. Available from: https:\/\/gh.bmj.com\/content\/5\/5\/e002604.abstract","DOI":"10.1136\/bmjgh-2020-002604"},{"key":"2047_CR11","doi-asserted-by":"publisher","unstructured":"Li HO-Y, Pastukhova E, Brandts-Longtin O, Tan MG, Kirchhof MG. YouTube as a source of misinformation on COVID-19 vaccination: a systematic analysis. BMJ Glob Health [Internet]. 2022;7. Available from: https:\/\/doi.org\/10.1136\/bmjgh-2021-008334","DOI":"10.1136\/bmjgh-2021-008334"},{"key":"2047_CR12","doi-asserted-by":"publisher","unstructured":"Quinn EK, Fenton S, Ford-Sahibzada CA, Harper A, Marcon AR, Caulfield T, et al. COVID-19 and Vitamin D Misinformation on YouTube: Content Analysis. JMIR Infodemiology [Internet]. 2022;2:e32452. Available from: https:\/\/doi.org\/10.2196\/32452","DOI":"10.2196\/32452"},{"key":"2047_CR13","doi-asserted-by":"publisher","unstructured":"Boon-Itt S, Skunkan Y. Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study. JMIR Public Health Surveill [Internet]. 2020;6:e21978. Available from: https:\/\/doi.org\/10.2196\/21978","DOI":"10.2196\/21978"},{"key":"2047_CR14","doi-asserted-by":"publisher","unstructured":"Chandrasekaran R, Mehta V, Valkunde T, Moustakas E. Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study. J Med Internet Res [Internet]. 2020;22:e22624. Available from: https:\/\/doi.org\/10.2196\/22624","DOI":"10.2196\/22624"},{"key":"2047_CR15","doi-asserted-by":"crossref","unstructured":"Ridhwan KM, Hargreaves CA. Leveraging Twitter Data to Understand Public Sentiment for the COVID-19 Outbreak in Singapore. International Journal of Information Management Data Insights [Internet]. 2021;100021. Available from: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2667096821000148","DOI":"10.1016\/j.jjimei.2021.100021"},{"key":"2047_CR16","doi-asserted-by":"crossref","unstructured":"Sesagiri Raamkumar A, Tan SG, Wee HL. Use of Health Belief Model-based deep learning classifiers for COVID-19 social media content to examine public perceptions of physical distancing: Model development and case study. JMIR Public Health Surveill [Internet]. 2020 [cited 2023 Jul 15];6:e20493. Available from: https:\/\/publichealth.jmir.org\/2020\/3\/e20493\/","DOI":"10.2196\/20493"},{"key":"2047_CR17","doi-asserted-by":"publisher","unstructured":"Sear RF, Velasquez N, Leahy R, Restrepo NJ, Oud SE, Gabriel N, et al. Quantifying COVID-19 Content in the Online Health Opinion War Using Machine Learning. IEEE Access [Internet]. 2020;8:91886\u201393. Available from: https:\/\/doi.org\/10.1109\/ACCESS.2020.2993967","DOI":"10.1109\/ACCESS.2020.2993967"},{"key":"2047_CR18","doi-asserted-by":"publisher","unstructured":"Niknam F, Samadbeik M, Fatehi F, Shirdel M, Rezazadeh M, Bastani P. COVID-19 on Instagram: A content analysis of selected accounts. Health Policy Technol [Internet]. 2021;10:165\u201373. Available from: https:\/\/doi.org\/10.1016\/j.hlpt.2020.10.016","DOI":"10.1016\/j.hlpt.2020.10.016"},{"key":"2047_CR19","doi-asserted-by":"publisher","unstructured":"Lucibello KM, Vani MF, Koulanova A, deJonge ML, Ashdown-Franks G, Sabiston CM. #quarantine15: A content analysis of Instagram posts during COVID-19. Body Image [Internet]. 2021;38:148\u201356. Available from: https:\/\/doi.org\/10.1016\/j.bodyim.2021.04.002","DOI":"10.1016\/j.bodyim.2021.04.002"},{"key":"2047_CR20","doi-asserted-by":"publisher","unstructured":"Osman W, Mohamed F, Elhassan M, Shoufan A. Is YouTube a reliable source of health-related information? A systematic review. BMC Med Educ [Internet]. 2022;22:382. Available from: https:\/\/doi.org\/10.1186\/s12909-022-03446-z","DOI":"10.1186\/s12909-022-03446-z"},{"key":"2047_CR21","doi-asserted-by":"publisher","unstructured":"Rodriguez-Rodriguez AM, Blanco-Diaz M, de la Fuente-Costa M, Hernandez-Sanchez S, Escobio-Prieto I, Casa\u00f1a J. Review of the Quality of YouTube Videos Recommending Exercises for the COVID-19 Lockdown. Int J Environ Res Public Health [Internet]. 2022;19. Available from: https:\/\/doi.org\/10.3390\/ijerph19138016","DOI":"10.3390\/ijerph19138016"},{"key":"2047_CR22","doi-asserted-by":"publisher","unstructured":"Chan C, Sounderajah V, Daniels E, Acharya A, Clarke J, Yalamanchili S, et al. The Reliability and Quality of YouTube Videos as a Source of Public Health Information Regarding COVID-19 Vaccination: Cross-sectional Study. JMIR Public Health Surveill [Internet]. 2021;7:e29942. Available from: https:\/\/doi.org\/10.2196\/29942","DOI":"10.2196\/29942"},{"key":"2047_CR23","doi-asserted-by":"publisher","unstructured":"Dutta A, Beriwal N, Van Breugel LM, Sachdeva S, Barman B, Saikia H, et al. 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. Cureus [Internet]. 2020;12:e8622. Available from: https:\/\/doi.org\/10.7759\/cureus.8622","DOI":"10.7759\/cureus.8622"},{"key":"2047_CR24","doi-asserted-by":"crossref","unstructured":"Moon H, Lee GH. Evaluation of Korean-Language COVID-19\u2013Related Medical Information on YouTube: Cross-Sectional Infodemiology Study. J Med Internet Res [Internet]. 2020 [cited 2023 Jul 30];22:e20775. Available from: https:\/\/www.jmir.org\/2020\/8\/e20775\/","DOI":"10.2196\/20775"},{"key":"2047_CR25","doi-asserted-by":"publisher","unstructured":"Rieder B, Matamoros-Fern\u00e1ndez A, Coromina \u00d2. From ranking algorithms to \u201cranking cultures\u201d: Investigating the modulation of visibility in YouTube search results. Convergence [Internet]. 2018;24:50\u201368. Available from: https:\/\/doi.org\/10.1177\/1354856517736982","DOI":"10.1177\/1354856517736982"},{"key":"2047_CR26","doi-asserted-by":"publisher","unstructured":"Arthurs J, Drakopoulou S, Gandini A. Researching YouTube. Convergence [Internet]. 2018;24:3\u201315. Available from: https:\/\/doi.org\/10.1177\/1354856517737222","DOI":"10.1177\/1354856517737222"},{"key":"2047_CR27","unstructured":"Pytube [Internet]. PyPI. [cited 2023 Aug 4]. Available from: https:\/\/pypi.org\/project\/pytube\/"},{"key":"2047_CR28","unstructured":"Thelwall M. Social web text analytics with Mozdeh. Mozdeh [Internet]. 2018;1\u201335. Available from: http:\/\/mozdeh.wlv.ac.uk\/resources\/SocialWebResearchWithMozdeh.pdf"},{"key":"2047_CR29","doi-asserted-by":"crossref","unstructured":"Deori M, Verma MK, Kumar V. Sentiment analysis of users\u2019 comments on Indian Hindi news channels using Mozdeh: An evaluation based on YouTube videos. J Creat Commun [Internet]. 2021;097325862110492. Available from: https:\/\/journals.sagepub.com\/doi\/abs\/10.1177\/09732586211049232","DOI":"10.1177\/09732586211049232"},{"key":"2047_CR30","unstructured":"Big Data Text Analysis [Internet]. [cited 2023 Aug 4]. Available from: http:\/\/mozdeh.wlv.ac.uk\/"},{"key":"2047_CR31","doi-asserted-by":"publisher","unstructured":"Gallagher RJ, Reing K, Kale D, Ver Steeg G. Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge. Transactions of the Association for Computational Linguistics [Internet]. 2017;5:529\u201342. Available from: https:\/\/doi.org\/10.1162\/tacl_a_00078","DOI":"10.1162\/tacl_a_00078"},{"key":"2047_CR32","unstructured":"Reing K, Kale DC, Ver Steeg G, Galstyan A. Toward Interpretable Topic Discovery via Anchored Correlation Explanation [Internet]. arXiv [stat.ML]. 2016. Available from: http:\/\/arxiv.org\/abs\/1606.07043"},{"key":"2047_CR33","doi-asserted-by":"publisher","unstructured":"Shankar K, Chandrasekaran R, Jeripity Venkata P, Miketinas D. Investigating the Role of Nutrition in Enhancing Immunity During the COVID-19 Pandemic: Twitter Text-Mining Analysis. J Med Internet Res [Internet]. 2023;25:e47328. Available from: https:\/\/doi.org\/10.2196\/47328","DOI":"10.2196\/47328"},{"key":"2047_CR34","doi-asserted-by":"publisher","unstructured":"Chandrasekaran R, Bapat P, Jeripity Venkata P, Moustakas E. Do Patients Assess Physicians Differently in Video Visits as Compared with In-Person Visits? Insights from Text-Mining Online Physician Reviews. Telemed J E Health [Internet]. 2023; Available from: https:\/\/doi.org\/10.1089\/tmj.2022.0507","DOI":"10.1089\/tmj.2022.0507"},{"key":"2047_CR35","doi-asserted-by":"publisher","unstructured":"Zhou S, Zhao Y, Bian J, Haynos AF, Zhang R. Exploring Eating Disorder Topics on Twitter: Machine Learning Approach. JMIR Med Inform [Internet]. 2020;8:e18273. Available from: https:\/\/doi.org\/10.2196\/18273","DOI":"10.2196\/18273"},{"key":"2047_CR36","doi-asserted-by":"publisher","unstructured":"Zhang S, Liu M, Li Y, Chung JE. Teens\u2019 Social Media Engagement during the COVID-19 Pandemic: A Time Series Examination of Posting and Emotion on Reddit. Int J Environ Res Public Health [Internet]. 2021;18. Available from: https:\/\/doi.org\/10.3390\/ijerph181910079","DOI":"10.3390\/ijerph181910079"},{"key":"2047_CR37","doi-asserted-by":"publisher","unstructured":"Abu-Salih B, Alhabashneh M, Zhu D, Awajan A, Alshamaileh Y, Al-Shboul B, et al. Emotion detection of social data: APIs comparative study. Heliyon [Internet]. 2023;9:e15926. Available from: https:\/\/doi.org\/10.1016\/j.heliyon.2023.e15926","DOI":"10.1016\/j.heliyon.2023.e15926"},{"key":"2047_CR38","doi-asserted-by":"publisher","unstructured":"Di Sotto S, Viviani M. Health Misinformation Detection in the Social Web: An Overview and a Data Science Approach. Int J Environ Res Public Health [Internet]. 2022;19. Available from: https:\/\/doi.org\/10.3390\/ijerph19042173","DOI":"10.3390\/ijerph19042173"},{"key":"2047_CR39","unstructured":"Shenk J. fer: Facial Expression Recognition with a deep neural network as a PyPI package [Internet]. Github; [cited 2023 Jul 29]. Available from: https:\/\/github.com\/JustinShenk\/fer"},{"key":"2047_CR40","doi-asserted-by":"publisher","unstructured":"Huang C-W, Wu BCY, Nguyen PA, Wang H-H, Kao C-C, Lee P-C, et al. Emotion recognition in doctor-patient interactions from real-world clinical video database: Initial development of artificial empathy. Comput Methods Programs Biomed [Internet]. 2023;233:107480. Available from: https:\/\/doi.org\/10.1016\/j.cmpb.2023.107480","DOI":"10.1016\/j.cmpb.2023.107480"},{"key":"2047_CR41","doi-asserted-by":"publisher","unstructured":"Dalvi C, Rathod M, Patil S, Gite S, Kotecha K. A Survey of AI-Based Facial Emotion Recognition: Features, ML & DL Techniques, Age-Wise Datasets and Future Directions. IEEE Access [Internet]. 2021;9:165806\u201340. Available from: https:\/\/doi.org\/10.1109\/ACCESS.2021.3131733","DOI":"10.1109\/ACCESS.2021.3131733"},{"key":"2047_CR42","doi-asserted-by":"crossref","unstructured":"Khan AR. Facial Emotion Recognition Using Conventional Machine Learning and Deep Learning Methods: Current Achievements, Analysis and Remaining Challenges. Information [Internet]. 2022 [cited 2023 Jul 29];13:268. Available from: https:\/\/www.mdpi.com\/2078-2489\/13\/6\/268","DOI":"10.3390\/info13060268"},{"key":"2047_CR43","doi-asserted-by":"publisher","unstructured":"Barlas T, Ecem Avci D, Cinici B, Ozkilicaslan H, Muhittin Yalcin M, Eroglu Altinova A. The quality and reliability analysis of YouTube videos about insulin resistance. Int J Med Inform [Internet]. 2023;170:104960. Available from: https:\/\/doi.org\/10.1016\/j.ijmedinf.2022.104960","DOI":"10.1016\/j.ijmedinf.2022.104960"},{"key":"2047_CR44","doi-asserted-by":"crossref","unstructured":"Hilbe JM. Negative Binomial Regression [Internet]. Cambridge University Press; 2011. Available from: https:\/\/play.google.com\/store\/books\/details?id=0Q_ijxOEBjMC","DOI":"10.1017\/CBO9780511973420"},{"key":"2047_CR45","doi-asserted-by":"publisher","unstructured":"Devendorf A, Bender A, Rottenberg J. Depression presentations, stigma, and mental health literacy: A critical review and YouTube content analysis. Clin Psychol Rev [Internet]. 2020;78:101843. Available from: https:\/\/doi.org\/10.1016\/j.cpr.2020.101843","DOI":"10.1016\/j.cpr.2020.101843"},{"key":"2047_CR46","doi-asserted-by":"publisher","unstructured":"Zhang J, Wang Y, Shi M, Wang X. Factors Driving the Popularity and Virality of COVID-19 Vaccine Discourse on Twitter: Text Mining and Data Visualization Study. JMIR Public Health Surveill [Internet]. 2021;7:e32814. Available from: https:\/\/doi.org\/10.2196\/32814","DOI":"10.2196\/32814"},{"key":"2047_CR47","doi-asserted-by":"publisher","unstructured":"Adikari A, Nawaratne R, De Silva D, Ranasinghe S, Alahakoon O, Alahakoon D. Emotions of COVID-19: Content Analysis of Self-Reported Information Using Artificial Intelligence. J Med Internet Res [Internet]. 2021;23:e27341. Available from: https:\/\/doi.org\/10.2196\/27341","DOI":"10.2196\/27341"},{"key":"2047_CR48","doi-asserted-by":"crossref","unstructured":"Kaur S, Kaul P, Zadeh PM. Monitoring the Dynamics of Emotions during COVID-19 Using Twitter Data. Procedia Comput Sci [Internet]. 2020;177:423\u201330. Available from: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877050920323243","DOI":"10.1016\/j.procs.2020.10.056"},{"key":"2047_CR49","doi-asserted-by":"crossref","unstructured":"Le\u00e3o T, Amorim M, Fraga S, Barros H. What doubts, concerns and fears about COVID-19 emerged during the first wave of the pandemic? Patient Educ Couns [Internet]. 2021;104:235\u201341. Available from: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0738399120306005","DOI":"10.1016\/j.pec.2020.11.002"},{"key":"2047_CR50","doi-asserted-by":"crossref","unstructured":"Xie W, Damiano A, Jong C-H. Emotional appeals and social support in organizational YouTube videos during COVID-19. Telematics and Informatics Reports [Internet]. 2022;8:100028. Available from: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2772503022000263","DOI":"10.1016\/j.teler.2022.100028"},{"key":"2047_CR51","doi-asserted-by":"publisher","unstructured":"Stolow JA, Moses LM, Lederer AM, Carter R. How Fear Appeal Approaches in COVID-19 Health Communication May Be Harming the Global Community. Health Educ Behav [Internet]. 2020;47:531\u20135. Available from: https:\/\/doi.org\/10.1177\/1090198120935073","DOI":"10.1177\/1090198120935073"},{"key":"2047_CR52","doi-asserted-by":"publisher","unstructured":"Paek H-J, Kim K, Hove T. Content analysis of antismoking videos on YouTube: message sensation value, message appeals, and their relationships with viewer responses. Health Educ Res [Internet]. 2010;25:1085\u201399. Available from: https:\/\/doi.org\/10.1093\/her\/cyq063","DOI":"10.1093\/her\/cyq063"},{"key":"2047_CR53","doi-asserted-by":"publisher","unstructured":"Yap JE, Zubcevic-Basic N, Johnson LW, Lodewyckx MA. Mental health message appeals and audience engagement: Evidence from Australia. Health Promot Int [Internet]. 2019;34:28\u201337. Available from: https:\/\/doi.org\/10.1093\/heapro\/dax062","DOI":"10.1093\/heapro\/dax062"},{"key":"2047_CR54","doi-asserted-by":"publisher","unstructured":"Tannenbaum MB, Hepler J, Zimmerman RS, Saul L, Jacobs S, Wilson K, et al. Appealing to fear: A meta-analysis of fear appeal effectiveness and theories. Psychol Bull [Internet]. 2015;141:1178\u2013204. Available from: https:\/\/doi.org\/10.1037\/a0039729","DOI":"10.1037\/a0039729"},{"key":"2047_CR55","doi-asserted-by":"publisher","unstructured":"Jin J, Lam S, Savas O, McCulloh I. Approaches for Quantifying Video Prominence, Narratives, & Discussion: Engagement on COVID-19 Related YouTube Videos. 2020 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) [Internet]. IEEE; 2020. p. 811\u20138. Available from: https:\/\/doi.org\/10.1109\/ASONAM49781.2020.9381362","DOI":"10.1109\/ASONAM49781.2020.9381362"},{"key":"2047_CR56","doi-asserted-by":"publisher","unstructured":"Ginossar T, Cruickshank IJ, Zheleva E, Sulskis J, Berger-Wolf T. Cross-platform spread: vaccine-related content, sources, and conspiracy theories in YouTube videos shared in early Twitter COVID-19 conversations. Hum Vaccin Immunother [Internet]. 2022;18:1\u201313. Available from: https:\/\/doi.org\/10.1080\/21645515.2021.2003647","DOI":"10.1080\/21645515.2021.2003647"},{"key":"2047_CR57","doi-asserted-by":"publisher","unstructured":"Gupta S, Jain G, Tiwari AA. Polarised social media discourse during COVID-19 pandemic: evidence from YouTube. Behav Inf Technol [Internet]. 2023;42:227\u201348. Available from: https:\/\/doi.org\/10.1080\/0144929X.2022.2059397","DOI":"10.1080\/0144929X.2022.2059397"},{"key":"2047_CR58","doi-asserted-by":"publisher","unstructured":"Koss J, Bohnet-Joschko S. Social Media Mining of Long-COVID Self-Medication Reported by Reddit Users: Feasibility Study to Support Drug Repurposing. JMIR Form Res [Internet]. 2022;6:e39582. Available from: https:\/\/doi.org\/10.2196\/39582","DOI":"10.2196\/39582"}],"container-title":["Journal of Medical Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-024-02047-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10916-024-02047-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10916-024-02047-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,14]],"date-time":"2025-01-14T07:26:49Z","timestamp":1736839609000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10916-024-02047-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,15]]},"references-count":58,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["2047"],"URL":"https:\/\/doi.org\/10.1007\/s10916-024-02047-1","relation":{},"ISSN":["1573-689X"],"issn-type":[{"value":"1573-689X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,15]]},"assertion":[{"value":"3 December 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 February 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interest"}}],"article-number":"21"}}