Abstract
Background. In the current era, Twitter is an increasingly popular tool for the dissemination of information as a social media voice. Social media is a valid, but underutilized, education tool at medical education institutions. Social media technologies provide opportunities for the presentation of information in alternative and multiple formats to enhance engagement, content creation, and motivation for individual and collaborative learning.
Objective. This study examined the type of social structure and sub-clusters do exist regarding “MedEd” on the Twitter network. Additionally, it determined the top opinion leaders in these networks and which type of topics generates users’ interest regarding “MedEd”.
Methods. This study applied NodeXL to analyze the results and retrieved Twitter data on November 1, 2022 by using the keywords “MedEd”. The data were saved and interpreted in the “vertices” and “edges” on the NodeXL worksheets.
Results. We found that the top opinion leader (vertex) “Cryptovitas” had the highest in- betweenness and out-degree centrality. “Innov_medicine” had the in-degree centrality for networks. “In-Degree” and “Out-Degree” are the count of Tweets an opinion leader gets and forwards messages out, correspondingly. The study found that although “Cryptovitas” had the highest in-betweenness centrality, “taylorswift13” had the maximum number of followers (91,523,045) with in-betweenness centrality of 0.0. This indicates that the vertex having maximum influence with the largest number of in-betweenness centrality has not linked with several followers.
Conclusions. Using Twitter embodies a potential prospect to engage the medical education community. The content of top networks’ tweets was around the number of “MedEd” innovations with the potential to significantly improve medical education delivery and innovative technologies in healthcare services. There is no link between the number of followers and in-betweenness centrality to influence the strength of social media voice. Although clinical and social tweets were there, not much was discussed regarding the curriculum reforms, continued professional development, technical issues in MedEd, and assessments. This triggers the insistence for rapid and innovative adaptations to the new learning environments and remarkable revolutions in medical education, including the encouragement of evidence-based education. The Twitter discussions promoted a research network circulating a wide range of informative innovations and collaborations.
References
Sreepathy P, Gough S, Pastapur A, Shroff AR. Abstract 11509: Social media is not just for posting selfies anymore: social media as a medical educational tool among medical trainees. Circulation. 2021;144(Suppl_1):A11509. Available from: https://doi.org/10.1161/circ.144.suppl_1.11509
Colbert JA, Chokshi DA. Technology in medical education—Osler Meets Watson. Journal of General Internal Medicine. 2014;29(12):1584–1585. Available from: https://doi.org/10.1007/s11606-014-2975-x
Gaeta Gazzola M, Swallow MA, Wijesekera TP. Middle school meets MedEd: Five K-12 teaching strategies medical educators should know. Medical Teacher. 2022;44(5):567–569. Available from: https://doi.org/10.1080/0142159X.2022.2039605
Jamil K, Dunnan L, Gul RF, Shehzad MU, Gillani SHM, Awan FH. Role of Social media marketing activities in influencing customer intentions: a perspective of a new emerging era. Frontiers in Psychology. 2022;12:808525. Available from: https://doi.org/10.3389/fpsyg.2021.808525
Farsi D, Martinez-Menchaca HR, Ahmed M, Farsi N. Social media and health care (Part II): narrative review of social media use by patients. Journal of Medical Internet Research. 2022;24(1):e30379. Available from: https://doi.org/10.2196/30379
Park HW, Park S, Chong M. Conversations and medical news frames on Twitter: infodemiological study on COVID-19 in South Korea. Journal of Medical Internet Research. 2020;22(5):e18897. Available from: https://doi.org/10.2196/18897
Malik A, Heyman-Schrum C, Johri A. Use of Twitter across educational settings: a review of the literature. International Journal of Educational Technology in Higher Education. 2019;16(1):36. Available from: https://doi.org/10.1186/s41239-019-0166-x
Katz M, Nandi N. Social media and medical education in the context of the COVID-19 pandemic: scoping review. JMIR Medical Education. 2021;7(2):e25892. Available from: https://doi.org/10.2196/25892
Wilcha R-J. Effectiveness of virtual medical teaching during the COVID-19 crisis: systematic review. JMIR Medical Education. 2020;6(2):e20963. Available from: https://doi.org/10.2196/20963
Williams RT. An exploration into the pedagogical benefits of using social media: can educators incorporate social media into pedagogy successfully? ABC Journal of Advanced Research. 2020;9(2):69–78. Available from: https://doi.org/10.18034/abcjar.v9i2.532
Neely S, Eldredge C, Sanders R. Health information seeking behaviors on social media during the COVID-19 pandemic among American social networking site users: survey study. Journal of Medical Internet Research. 2021;23(6):e29802. Available from: https://doi.org/10.2196/29802
Rosenberg H, Syed S, Rezaie S. The Twitter pandemic: the critical role of Twitter in the dissemination of medical information and misinformation during the COVID-19 pandemic. CJEM. 2020;22(4):418–421. Available from: https://doi.org/10.1017/cem.2020.361
Dey N, Babo R, Ashour AS, Bhatnagar V, Bouhlel MS, editors. Social networks science: design, implementation, security, and challenges. Cham: Springer; 2018. Available from: https://doi.org/10.1007/978-3-319-90059-9
Madanick RD. Education becomes social: the intersection of social media and medical education. Gastroenterology. 2015;149(4):844–847. Available from: https://doi.org/10.1053/j.gastro.2015.08.037
Chan TM, Jordan J, Clarke SO, Lawson L, Coates WC, Yarris LM, et al. Beyond the CLAIM: a comprehensive needs assessment strategy for creating an Advanced Medical Education Research Training Program (ARMED‐MedEd). AEM Education and Training. 2022;6(1):e10720. Available from: https://doi.org/10.1002/aet2.10720
Byrne MHV, Ashcroft J, Alexander L, Wan JCM, Harvey A, et al. P78 MedEd Collaborative: a new research collaborative to promote medical education research. BJS Open. 2021;5(Supplement_1):zrab032.077. Available from: https://doi.org/10.1093/bjsopen/zrab032.077
Santen SA, Smith J, Shockley J, Cyrus JW, Lomis KD, Pusic M, et al. Social network analysis of publication collaboration of accelerating change in MedEd consortium. Medical Teacher. 2021;44(3):276–286. Available from: https://doi.org/10.1080/0142159X.2021.1985096
Saura JR, Ribeiro-Soriano D, Zegarra Saldaña P. Exploring the challenges of remote work on Twitter users’ sentiments: from digital technology development to a post-pandemic era. Journal of Business Research. 2022;142:242–254. Available from: https://doi.org/10.1016/j.jbusres.2021.12.052
Fathi M, Haghi Kashani M, Jameii SM, Mahdipour E. Big data analytics in weather forecasting: a systematic review. Archives of Computational Methods in Engineering. 2021;29(2):1247–1275. Available from: https://doi.org/10.1007/s11831-021-09616-4
Theocharis Y, Cardenal A, Jin S, Aalberg T, Hopmann DN, Strömbäck J, et al. Does the platform matter? Social media and COVID-19 conspiracy theory beliefs in 17 countries. New Media & Society. 2021;146144482110456. Available from: https://doi.org/10.1177/14614448211045666
Nugraha SA, Siregar MU. Application of The Naive Bayes classifier method in the sentiment analysis of Twitter user about the capital city relocation. Proceeding International Conference on Science and Engineering. 2021;4:171-175. Available from: https://sunankalijaga.org/prosiding/index.php/icse/article/view/654
Xu W, Sasahara K. A network-based approach to QAnon user dynamics and topic diversity during the COVID-19 infodemic. APSIPA Transactions on Signal and Information Processing. 2022;11(2):e17. Available from: https://doi.org/10.1561/116.00000055
Torous J, Bucci S, Bell IH, Kessing LV, Faurholt‐Jepsen M, Whelan P, et al. The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry. 2021;20(3):318–335. Available from: https://doi.org/10.1002/wps.20883
Gandotra S, Stewart NH, Khateeb D, Garcha PS, Carlos WG, Carroll CL, et al. Understanding the social in social media: an analysis of tweeting behaviors of pulmonary and critical care fellowship programs. A33 Advances in Medical Education in Pulmonary and Critical Care Medicine. 2019. Available from: https://doi.org/10.1164/ajrccm-conference.2019.199.1_MeetingAbstracts.A1363
Nodexl Tool for Social Network Analysis [computer program]. Version 2021. Available from: https://nodexl.com/
Kydros D, Vrana V. A Twitter network analysis of European museums. Museum Management and Curatorship. 2021;36(6):569–589. Available from: https://doi.org/10.1080/09647775.2021.1894475
Ghorbani Z, Khasteh SH, Ghafouri S. Opinion leader detection in online social networks based on output and input links [Internet]. arXiv [Preprint]. 2022 [cited 20 Feb 2023]. Available from: https://arxiv.org/abs/2208.13161https://doi.org/10.48550/arXiv.2208.13161
Curado M, Rodriguez R, Terroso-Saenz F, Tortosa L, Vicent JF. A centrality model for directed graphs based on the Two-Way-Random Path and associated indices for characterizing the nodes. Journal of Computational Science. 2022;63:101819. Available from: https://doi.org/10.1016/j.jocs.2022.101819
Iqbal S, Ahmad S, Bano B, Akkour K, Alghamdi MAA, Alothri AM. A systematic review: role of artificial intelligence during the COVID-19 pandemic in the healthcare system. International Journal of Intelligent Information Technologies. 2021;17(1):1. Available from: https://doi.org/10.4018/IJIIT.2021010101
Iqbal S, Ahmad S, Akkour K, Wafa ANA, AlMutairi HM, Aldhufairi AM. Review article: Impact of Artificial Intelligence in Medical Education. MedEdPublish. 2021;10(1):41. Available from: https://doi.org/10.15694/mep.2021.000041.1
Hussain A, Tahir A, Hussain Z, Sheikh Z, Gogate M, Dashtipour K, et al. Artificial Intelligence–Enabled analysis of public attitudes on Facebook and Twitter toward COVID-19 vaccines in the United Kingdom and the United States: observational study. Journal of Medical Internet Research. 2021;23(4):e26627.Available from: https://doi.org/10.2196/26627
Chuan PM, Son LH, Ali M, Khang TD, Huong LT, Dey N. Link prediction in co-authorship networks based on hybrid content similarity metric. Applied Intelligence. 2017;48(8):2470–2486. Available from: https://doi.org/10.1007/s10489-017-1086-x
Bayrakdar S, Yucedag I, Simsek M, Dogru IA. Semantic analysis on social networks: a survey. International Journal of Communication Systems. 2020;33(11):e4424. Available from: https://doi.org/10.1002/dac.4424
Smith CM. Commercial Networks and Marks of Trade on Athenian Pots [Master's thesis]. Tucson (AZ): University of Arizona; 2022. Available from: http://hdl.handle.net/10150/667688
Garbrick AH. Factors influencing student engagement in an online asynchronous discussion forum measured by quantity, quality, survey, and social network analysis [dissertation]. Pennsylvania (PA): The Pennsylvania State University; 2018.
Dunne C. Measuring and improving the readability of network visualizations [dissertation]. Maryland: University of Maryland; 2013. Available from: http://hdl.handle.net/1903/14485
Ojo A, Guntuku SC, Zheng M, Beidas RS, Ranney ML. How health care workers wield influence through Twitter hashtags: retrospective cross-sectional study of the gun violence and COVID-19 public health crises. JMIR Public Health and Surveillance. 2021;7(1):e24562. Available from: https://doi.org/10.2196/24562
Papapanou M, Routsi E, Tsamakis K, Fotis L, Marinos G, Lidoriki I, et al. Medical education challenges and innovations during COVID-19 pandemic. Postgraduate Medical Journal. 2021;98(1159):321–327. Available from: https://doi.org/10.1136/postgradmedj-2021-140032
Yao Q, Li RYM, Song L, Crabbe MJC. Construction safety knowledge sharing on Twitter: a social network analysis. Safety Science. 2021;143:105411. Available from: https://doi.org/10.1016/j.ssci.2021.105411
Goh P-S, Roberts-Lieb S, Sandars J. Micro-Scholarship: An innovative approach for the first steps for Scholarship in Health Professions Education. Medical Teacher. 2022;45(3):307–312. Available from: https://doi.org/10.1080/0142159X.2022.2133689
Chen J, Wang Y. Social media use for health purposes: systematic review. Journal of Medical Internet Research. 2021;23(5):e17917. Available from: https://doi.org/10.2196/17917
Hill SS, Dore FJ, Em ST, McLoughlin RJ, Crawford AS, Sturrock PR, et al. Twitter use among departments of surgery with general surgery residency programs. Journal of Surgical Education. 2021;78(1):35–42. Available from: https://doi.org/10.1016/j.jsurg.2020.06.008
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.