IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i9p5451-d805796.html
   My bibliography  Save this article

Youtube TM Content Analysis as a Means of Information in Oral Medicine: A Systematic Review of the Literature

Author

Listed:
  • Antonio Romano

    (Multidisciplinary Department of Medical-Surgical and Dental Specialties, University of Campania “Luigi Vanvitelli”, Via L. de Crecchio 6, 80138 Naples, Italy)

  • Fausto Fiori

    (Multidisciplinary Department of Medical-Surgical and Dental Specialties, University of Campania “Luigi Vanvitelli”, Via L. de Crecchio 6, 80138 Naples, Italy)

  • Massimo Petruzzi

    (Interdisciplinary Department of Medicine, University of Bari “A. Moro”, 70124 Bari, Italy)

  • Fedora Della Vella

    (Interdisciplinary Department of Medicine, University of Bari “A. Moro”, 70124 Bari, Italy)

  • Rosario Serpico

    (Multidisciplinary Department of Medical-Surgical and Dental Specialties, University of Campania “Luigi Vanvitelli”, Via L. de Crecchio 6, 80138 Naples, Italy)

Abstract

Background: Oral medicine represents a complex branch of dentistry, involved in diagnosing and managing a wide range of disorders. Youtube TM offers a huge source of information for users and patients affected by oral diseases. This systematic review aims to evaluate the reliability of Youtube TM oral medicine-related content as a valid dissemination aid. Methods: The MeSH terms “Youtube TM ” and “oral” have been searched by three search engines (PubMed, ISI Web of Science, and the Cochrane Library), and a systematic review has been performed; the PRISMA checklist has been followed in the search operations. Results: Initial results were 210. Ten studies definitely met our selection criteria. Conclusions: Youtube TM represents a dynamic device capable of easy and rapid dissemination of medical-scientific content. Nevertheless, the most of information collected in the literature shows a lack of adequate knowledge and the need to utilize a peer-reviewing tool in order to avoid the spreading of misleading and dangerous content.

Suggested Citation

  • Antonio Romano & Fausto Fiori & Massimo Petruzzi & Fedora Della Vella & Rosario Serpico, 2022. "Youtube TM Content Analysis as a Means of Information in Oral Medicine: A Systematic Review of the Literature," IJERPH, MDPI, vol. 19(9), pages 1-8, April.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:9:p:5451-:d:805796
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/9/5451/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/9/5451/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hassan Hosseinzadeh & Zubair Ahmed Ratan & Kamrun Nahar & Ann Dadich & Abdullah Al-Mamun & Searat Ali & Marzieh Niknami & Iksheta Verma & Joseph Edwards & Mahmmoud Shnaigat & Md Abdul Malak & Md Musta, 2023. "Telemedicine Use and the Perceived Risk of COVID-19: Patient Experience," IJERPH, MDPI, vol. 20(4), pages 1-19, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. David H Chae & Sean Clouston & Mark L Hatzenbuehler & Michael R Kramer & Hannah L F Cooper & Sacoby M Wilson & Seth I Stephens-Davidowitz & Robert S Gold & Bruce G Link, 2015. "Association between an Internet-Based Measure of Area Racism and Black Mortality," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-12, April.
    2. Xiaoli Wang & Shuangsheng Wu & C Raina MacIntyre & Hongbin Zhang & Weixian Shi & Xiaomin Peng & Wei Duan & Peng Yang & Yi Zhang & Quanyi Wang, 2015. "Using an Adjusted Serfling Regression Model to Improve the Early Warning at the Arrival of Peak Timing of Influenza in Beijing," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-14, March.
    3. Ishani Chaudhuri & Parthajit Kayal, 2022. "Predicting Power of Ticker Search Volume in Indian Stock Market," Working Papers 2022-214, Madras School of Economics,Chennai,India.
    4. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    5. Kuchler, Theresa & Russel, Dominic & Stroebel, Johannes, 2022. "JUE Insight: The geographic spread of COVID-19 correlates with the structure of social networks as measured by Facebook," Journal of Urban Economics, Elsevier, vol. 127(C).
    6. Markowitz, Sara & Nesson, Erik & Robinson, Joshua J., 2019. "The effects of employment on influenza rates," Economics & Human Biology, Elsevier, vol. 34(C), pages 286-295.
    7. Bentzen, Jeanet Sinding, 2021. "In crisis, we pray: Religiosity and the COVID-19 pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 541-583.
    8. Jesse T. Richman & Ryan J. Roberts, 2023. "Assessing Spurious Correlations in Big Search Data," Forecasting, MDPI, vol. 5(1), pages 1-12, February.
    9. Linus Schiöler & Marianne Fris�n, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    10. Sasikiran Kandula & Jeffrey Shaman, 2019. "Reappraising the utility of Google Flu Trends," PLOS Computational Biology, Public Library of Science, vol. 15(8), pages 1-16, August.
    11. Yangkun Huang & Xiaoping Xu & Sini Su, 2021. "Diverging from News Media: An Exploratory Study on the Changing Dynamics between Media and Public Attention on Cancer in China from 2011–2020," IJERPH, MDPI, vol. 18(16), pages 1-13, August.
    12. Vosen, Simeon & Schmidt, Torsten, 2012. "A monthly consumption indicator for Germany based on Internet search query data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(7), pages 683-687.
    13. Klaus Ackermann & Simon D Angus & Paul A Raschky, 2017. "The Internet as Quantitative Social Science Platform: Insights from a Trillion Observations," Papers 1701.05632, arXiv.org.
    14. Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2018. "Big Data And Big Cities: The Promises And Limitations Of Improved Measures Of Urban Life," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 114-137, January.
    15. Sean Coogan & Zhixian Sui & David Raubenheimer, 2018. "Gluttony and guilt: monthly trends in internet search query data are comparable with national-level energy intake and dieting behavior," Palgrave Communications, Palgrave Macmillan, vol. 4(1), pages 1-9, December.
    16. Tobias Preis & Federico Botta & Helen Susannah Moat, 2020. "Sensing global tourism numbers with millions of publicly shared online photographs," Environment and Planning A, , vol. 52(3), pages 471-477, May.
    17. D'Amuri, Francesco & Marcucci, Juri, 2009. "‘Google it!’ Forecasting the US unemployment rate with a Google job search index," ISER Working Paper Series 2009-32, Institute for Social and Economic Research.
    18. Liwen Ling & Dabin Zhang & Shanying Chen & Amin W. Mugera, 2020. "Can online search data improve the forecast accuracy of pork price in China?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 671-686, July.
    19. Klaus Ackermann & Simon D Angus & Paul A Raschky, 2020. "Estimating Sleep and Work Hours from Alternative Data by Segmented Functional Classification Analysis, SFCA," SoDa Laboratories Working Paper Series 2020-04, Monash University, SoDa Laboratories.
    20. Aksoy, Cevat Giray & Ganslmeier, Michael & Poutvaara, Panu, 2020. "Public Attention and Policy Responses to COVID-19 Pandemic," IZA Discussion Papers 13427, Institute of Labor Economics (IZA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:9:p:5451-:d:805796. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.