1. INTRODUCTION
⌅It is a normal human tendency to form social groups to interchange ideas, share understandings, and offer support (Anglade et al. , 2019Anglade, C., Le Dorze, G., & Croteau, C. (2019). Service encounter interactions of people living with moderate-to-severe post-stroke aphasia in their community. Aphasiology, 33(9), 1061-1082. DOI: https://doi.org/10.1080/02687038.2018.1532068 ). As the Internet has evolved and developed, new networks have sprouted up to address the population’s requirements (Erfani & Abedin, 2018Erfani, S. S., & Abedin, B. (2018). Impacts of the use of social network sites on users’ psychological well‐being: A systematic review. Journal of the Association for Information Science and Technology, 69(7), 900-912. DOI: https://doi.org/10.1002/asi.24015 ).
During recent times, online social media have become a key node for individuals to collaborate and communicate, (Penni, 2017Penni, J. (2017). The future of online social networks (OSN): A measurement analysis using social media tools and application. Telematics and Informatics, 34(5), 498-517. DOI: https://doi.org/10.1016/j.tele.2016.10.009 ) share views with other people on ideas and content (Anwar et al. , 2019Anwar, M. M., Liu, C., & Li, J. (2019). Discovering and tracking query oriented active online social groups in dynamic information network. World Wide Web, 22(4), 1819-1854. DOI: https://doi.org/10.1007/s11280-018-0627-5 ; Body and Ellison, 2007Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer‐Mediated Communication, 13(1), 210-230. DOI: https://doi.org/10.1111/j.1083-6101.2007.00393.x ). The competitive design of these platforms, which has helped to develop user relationships (Tajeuna et al. , 2018Tajeuna, E. G., Bouguessa, M., & Wang, S. (2018). Modeling and predicting community structure changes in time-evolving social networks. IEEE Transactions on Knowledge and Data Engineering, 31(6), 1166-1180. DOI: https://doi.org/10.1109/TKDE.2018.2851586 ; Elbanna et al. , 2019Elbanna, A., Bunker, D., Levine, L., & Sleigh, A. (2019). Emergency management in the changing world of social media: Framing the research agenda with the stakeholders through engaged scholarship. International Journal of Information Management, 47, 112-120. DOI: https://doi.org/10.1016/j.ijinfomgt.2019.01.011 ), has led to their exponential growth and the stability of these media. While people often use those networks to meet new people, people who have common aspirations or interests appear to communicate with each other even when strangers (Dokuka et al. , 2017Dokuka, S., Krekhovets, E., & Priymak, M. (2017, July). Health, grades and friendship: How socially constructed characteristics influence the social network structure. In International Conference on Analysis of Images, Social Networks and Texts, 381-391. Springer, Cham. DOI: https://doi.org/10.1007/978-3-319-73013-4_35 ). Disease states and medication bring unknown people together on social media.
An important motivation for these emerging social networks is to understand people’s or societies’ well-being (Romano et al. , 2018Romano, V., Shen, M., Pansanel, J., MacIntosh, A. J., & Sueur, C. (2018). Social transmission in networks: global efficiency peaks with intermediate levels of modularity. Behavioral Ecology and Sociobiology, 72(9), 1-10. DOI: https://doi.org/10.1007/s00265-018-2564-9 ). Netizens are both active and passive players in these digitally interconnected networks (Cohen et al. , 2018Cohen, R., Newton-John, T., & Slater, A. (2018). ‘Selfie’-objectification: The role of selfies in self-objectification and disordered eating in young women. Computers in Human Behavior, 79, 68-74. DOI: https://doi.org/10.1016/j.chb.2017.10.027 ). Depression and other mental health issues, as well as physical disorders like sexual infections, and now COVID are the hot topics among the netizens (Villanti et al. , 2017Villanti, A. C., Johnson, A. L., Ilakkuvan, V., Jacobs, M. A., Graham, A. L., & Rath, J. M. (2017). Social media use and access to digital technology in US young adults in 2016. Journal of Medical Internet Research, 19(6), e7303. DOI: https://doi.org/10.2196/jmir.7303 ; Błachnio et al. , 2015Błachnio, A., Przepiórka, A., & Pantic, I. (2015). Internet use, Facebook intrusion, and depression: Results of a cross-sectional study. European Psychiatry, 30(6), 681-684. DOI: https://doi.org/10.1016/j.eurpsy.2015.04.002 ; Ballester-Arnal et al. , 2016Ballester-Arnal, R., Giménez-García, C., Gil-Llario, M. D., & Castro-Calvo, J. (2016). Cybersex in the “Net generation”: Online sexual activities among Spanish adolescents. Computers in Human Behavior, 57, 261-266. DOI: https://doi.org/10.1016/j.chb.2015.12.036 ).
This topic has recently been investigated about social and health networks as both for prevention and training mechanisms, as well as risk factors (Tajeuna et al. , 2018Tajeuna, E. G., Bouguessa, M., & Wang, S. (2018). Modeling and predicting community structure changes in time-evolving social networks. IEEE Transactions on Knowledge and Data Engineering, 31(6), 1166-1180. DOI: https://doi.org/10.1109/TKDE.2018.2851586 ; Aiello, 2017Aiello, A. E. (2017). Invited commentary: evolution of social networks, health, and the role of epidemiology. American Journal of Epidemiology, 185(11), 1089-1092. DOI: https://doi.org/10.1093/aje/kwx076 ). In this respect, researchers investigated the negative impact of social media on health (Boyd et al. , 2007Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer‐Mediated Communication, 13(1), 210-230. DOI: https://doi.org/10.1111/j.1083-6101.2007.00393.x ; Shensa et al. , 2017Shensa, A., Escobar-Viera, C. G., Sidani, J. E., Bowman, N. D., Marshal, M. P., & Primack, B. A. (2017). Problematic social media use and depressive symptoms among US young adults: A nationally-representative study. Social Science & Medicine, 182, 150-157. DOI: https://doi.org/10.1016/j.socscimed.2017.03.061 ), as well as their mental side effects such as depression, stress, and eating disorders (Ainin et al. ,2015Ainin, S., Naqshbandi, M. M., Moghavvemi, S., & Jaafar, N. I. (2015). Facebook usage, socialization and academic performance. Computers & Education, 83, 64-73. DOI: https://doi.org/10.1016/j.compedu.2014.12.018 , Huang & Su, 2018Huang, Y. T., & Su, S. F. (2018). Motives for Instagram use and topics of interest among young adults. Future Internet, 10(8), 77. DOI: https://doi.org/10.3390/fi10080077 ).
Other studies have looked into their utility for health interventions (Alhuwail & Abdulsalam, 2019Alhuwail, D., & Abdulsalam, Y. (2019). Assessing electronic health literacy in the state of Kuwait: Survey of internet users from an Arab state. Journal of Medical Internet Research, 21(5), e11174. DOI: https://doi.org/10.2196/11174 ; Ridout & Campbell, 2018Ridout, B., & Campbell, A. (2018). The use of social networking sites in mental health interventions for young people: systematic review. Journal of medical Internet research, 20(12), e12244. DOI: https://doi.org/10.2196/12244 ), especially health education (Ilakkuvan et al. , 2019Ilakkuvan, V., Johnson, A., Villanti, A. C., Evans, W. D., & Turner, M. (2019). Patterns of social media use and their relationship to health risks among young adults. Journal of Adolescent Health, 64(2), 158-164. DOI: https://doi.org/10.1016/j.jadohealth.2018.06.025 ). Patients’ participation in health-care communities is also a research subject that often reflects on specific health or social welfare problems (Shen et al. , 2018Shen, J., Zhu, P., & Xu, M. (2018). Knowledge sharing of online health community based on cognitive neuroscience. NeuroQuantology, 16(5). DOI: https://doi.org/10.14704/nq.2018.16.5.1351 ). In general, based on the fitness and actions of the individual social media tends to have been used in several ways (Barton et al. , 2019Barton, K. S., Wingerson, A., Barzilay, J. R., & Tabor, H. K. (2019). “Before Facebook and before social media… we did not know anybody else that had this”: parent perspectives on internet and social media use during the pediatric clinical genetic testing process. Journal of Community Genetics, 10(3), 375-383. DOI: https://doi.org/10.1007/s12687-018-0400-6 ; More & Lingam, 2019More, J. S., & Lingam, C. (2019). A SI model for social media influencer maximization. Applied Computing and Informatics, 15(2), 102-108. DOI: https://doi.org/10.1016/j.aci.2017.11.001 ).
In the event of emergencies or disease outbreaks, digital health can be the answer to achieving long-term viability (Wang & Liu, 2005Wang, W. T., & Liu, C. Y. (2005). The application of the technology acceptance model: A new way to evaluate information system success. In Proceedings of the 23rd International System Dynamics Conference, 1-23. Boston, Massachusetts, USA: Massachusetts Institute of Technology.). This technology is expected to help meet global health targets by including Internet connectivity, patient health and non-health data, and data from the community (Boman & Kruse, 2017Boman, M., & Kruse, E. (2017). Supporting global health goals with information and communications technology. Global Health Action, 10(sup3), 1321904. DOI: https://doi.org/10.1080/16549716.2017.1321904 ). Telemedicine, mobile technology and apps, connected systems, and remote tracking sensors are examples of technologies that help stakeholders to control their wellbeing and service delivery (Widmer et al. , 2015Widmer, R. J., Collins, N. M., Collins, C. S., West, C. P., Lerman, L. O., & Lerman, A. (2015). Digital health interventions for the prevention of cardiovascular disease: a systematic review and meta-analysis. In Mayo Clinic Proceedings, 90(4), 469-480). Elsevier. DOI: https://doi.org/10.1016/j.mayocp.2014.12.026 ; Vijayakumar et al. , 2017Vijayakumar, P., Pandiaraja, P., Karuppiah, M., & Deborah, L. J. (2017). An efficient secure communication for healthcare system using wearable devices. Computers & Electrical Engineering, 63, 232-245. DOI: https://doi.org/10.1016/j.compeleceng.2017.04.014 ).
The objective of this paper is to develop better insight of literature on social media based health information using bibliometric analysis in the context of India. This study is undertaken because health information has profound impact on health care outcomes, both positively and negatively. It is duly noted that health care information is still illegal in most countries for the stakeholders other than the healthcare providers. A lot of research exist on positive and negative effects of health information available on internet. Social media based healthcare information need exploration because these are recent and are more advanced in nature than the earlier non-interactive websites on internet. The direction that research on social media based health information needs to be explored for helping the stakeholders, particularly the regulatory bodies and healthcare professionals to identify the gaps, trends and come up with appropriate responses to the challenges faced in this respect.
2. RESEARCH METHODOLOGY
⌅The study is based on bibliometric analysis. The use of statistical techniques to calculate the content and quantity of books, papers, and other publications are referred to as bibliometrics (Durieux & Gevenois, 2010Durieux, V., & Gevenois, P. A. (2010). Bibliometric indicators: quality measurements of scientific publication. Radiology, 255(2), 342-351. DOI: https://doi.org/10.1148/radiol.09090626 ; Sweileh et al. , 2017Sweileh, W. M., Al-Jabi, S. W., AbuTaha, A. S., Sa’ed, H. Z., Anayah, F. M., & Sawalha, A. F. (2017). Bibliometric analysis of worldwide scientific literature in mobile-health: 2006-2016. BMC Medical Informatics and Decision Making, 17(1), 1-12. DOI: https://doi.org/10.1186/s12911-017-0476-7 ). It’s been used in crisis analysis (Ardito et al. , 2019Ardito, L., Scuotto, V., Del Giudice, M., & Petruzzelli, A. M. (2019). A bibliometric analysis of research on Big Data analytics for business and management. Management Decision. DOI: https://doi.org/10.1108/MD-07-2018-0754 ; Chiu & Ho, 2007Chiu, W. T., & Ho, Y. S. (2007). Bibliometric analysis of tsunami research. Scientometrics, 73(1), 3-17. DOI: https://doi.org/10.1007/s11192-005-1523-1 ; Jiang et al. , 2019; Lee & Kim, 2016Lee, J. Y., & Kim, S. (2016). A bibliometric analysis of research trends on disaster in Korea. Journal of the Korean Society for Information Management, 33(4), 103-124. DOI: https://doi.org/10.3743/KOSIM.2016.33.4.103 ; Sweileh, 2019Sweileh, W. M. (2019). A bibliometric analysis of health-related literature on natural disasters from 1900 to 2017. Health Research Policy and Systems, 17(1), 1-11. DOI: https://doi.org/10.1186/s12961-019-0418-1 ) and information management (Chao et al. , 2007Chao, C. C., Yang, J. M., & Jen, W. Y. (2007). Determining technology trends and forecasts of RFID by a historical review and bibliometric analysis from 1991 to 2005. Technovation, 27(5), 268-279. DOI: https://doi.org/10.1016/j.technovation.2006.09.003 ; Cobo et al. , 2007Cobo, M. J., Martínez, M. Á., Gutiérrez-Salcedo, M., Fujita, H., & Herrera-Viedma, E. (2015). 25 years at knowledge-based systems: a bibliometric analysis. Knowledge-based systems, 80, 3-13. DOI: https://doi.org/10.1016/j.knosys.2014.12.035 ; Du et al. , 2017Du, H. S., Ke, X., Chu, S. K., & Chan, L. T. (2017). A bibliometric analysis of emergency management using information systems (2000-2016). Online Information Review. DOI: https://doi.org/10.1108/OIR-05-2017-0142 ). In keeping with the theme, because of the absence of published evidence across all subject areas. This study paper employs bibliometrics to analyze papers on social media health information in India; data for co-citation analysis, co-occurrence analysis, and other related studies of previous literature.
This paper is retrospective and descriptive bibliometric study is carried by reviewing the articles published in PubMed.
PubMed has been preferred as it is a free search engine which helps to access healthcare database.
The software used for bibliometric analysis is profile research networking software from Harvard University although several software are available like; PROFILES by UMassMed Center for Clinical and Translational Research for bibliometric analysis as it examines publications to categorize significant ideas and diverse areas of research. Profile research networking software from Harvard University self-populates a database of publication history, open to all and it is easy to use. Researchers like GM Weber (2011)Weber, G. M., Barnett, W., Conlon, M., Eichmann, D., Kibbe, W., Falk-Krzesinski, H., ... & Direct2Experts Collaboration. (2011). Direct2Experts: a pilot national network to demonstrate interoperability among research-networking platforms. Journal of the American Medical Informatics Association, 18(Supplement_1), i157-i160. DOI: https://doi.org/10.1136/amiajnl-2011-000200 , Alireza Ahmadvand (2019)Ahmadvand, A., Kavanagh, D., Clark, M., Drennan, J., & Nissen, L. (2019). Trends and visibility of “digital health” as a keyword in articles by JMIR publications in the new millennium: Bibliographic-bibliometric analysis. Journal of Medical Internet Research, 21(12), e10477. DOI: https://doi.org/10.2196/10477 have used this software for their study. Other software that we have used is Vosviwer. The “Visualization of Science (VOS)” mapping tool was used for the mapping of reference co-citation analysis and the document’s bibliometric mixture analysis for these techniques. VOSviewer also has text mining capabilities, which may be used to build and display co-occurrence networks of key phrases retrieved from scientific literature.
Advent of social media is traced back to 1997 (Ketizman et al. , 2011Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54(3), 241-251. DOI: https://doi.org/10.1016/j.bushor.2011.01.005 ). Orkut is the first social media platform in India (Statista, 2020Statista (2020). Social media usage in India - statistics & facts. https://www.statista.com/topics/5113/social-media-usage-in-india/ Retrieved December 7, 2020.). Healthcare based social media sites began to appear to India in the year 2004 as is obvious from the data base. The study therefore is for the period 2004-June, 2021. The keyword used for searching PubMed for this study are social Media, India, Health. In the first step the database of non-English documents were removed. Wherever full text was not available these papers are also discarded. This provided a list of 713 documents and which has been used as base for bibliometric analysis.
The popular three words or abbreviations are Social Network, Social Media, and SMP (Social Media Promotion). For this analysis, we used the keyword ‘Social Media”. This study’s inclusion criteria are dependent on the terms ‘social media,’ ‘India,’ and ‘health.’ The terms were used to explore the interference of social networks in health, with the goals of this study in mind. The word “India” was also used to describe earlier studies on the region.
The field/discipline is based on the values provided to each journal by the NLM (National Library of Medicine). The headings in the broad journal are MeSH Descriptors which sum up the whole topics of a journal. Typically, there is more than one heading in the Broad Journal, therefore there may be more than once in the table below for a single edition. Therefore, the field NumPubs might add up to more than the number of publications. The ratio of RatioExpPubs is comparable to the projected year-to-year number of publications in the area.
From the Table I, it is clear that number of publication is much higher in the field of ‘public health’. In the field of ‘psychology’, publication has started much later in this research area. Ratio of average number of citations, in the field of ‘Psychiatry’ is highest.
| Field | NumPubs | %Pubs | Ratio ExpPubs | FirstYear | LastYear | Avg Cites | Exp Cites | Ratio Cites | Exp CitesPT | Ratio CitesPT |
|---|---|---|---|---|---|---|---|---|---|---|
| Public Health | 78 | 11.747 | 6.294 | 2004 | 2021 | 4.090 | 3.489 | 1.172 | 3.031 | 1.349 |
| Medicine | 60 | 9.036 | 1.335 | 2005 | 2021 | 4.333 | 2.787 | 1.555 | 4.282 | 1.012 |
| Health Services | 31 | 4.669 | 5.394 | 2004 | 2021 | 3.548 | 3.653 | 0.971 | 3.538 | 1.003 |
| Medical Informatics | 28 | 4.217 | 6.396 | 2014 | 2021 | 4.857 | 4.322 | 1.124 | 4.602 | 1.055 |
| Psychiatry | 24 | 3.614 | 2.426 | 2005 | 2021 | 14.708 | 2.802 | 5.249 | 2.566 | 5.732 |
| Science | 23 | 3.464 | 0.825 | 2004 | 2021 | 3.870 | 4.140 | 0.935 | 5.390 | 0.718 |
| Pediatrics | 21 | 3.163 | 1.617 | 2000 | 2021 | 2.952 | 1.878 | 1.572 | 3.482 | 0.848 |
| Psychology | 19 | 2.861 | 2.205 | 2011 | 2021 | 3.211 | 3.232 | 0.993 | 2.795 | 1.149 |
| Social Sciences | 18 | 2.711 | 7.919 | 2004 | 2020 | 3.000 | 3.353 | 0.895 | 3.094 | 0.970 |
| Health Services Research | 17 | 2.560 | 4.906 | 2004 | 2021 | 6.235 | 7.199 | 0.866 | 7.701 | 0.810 |
Summary Report
⌅The summary statistics for the selected collection of PubmedIDs are shown in Table II. The predicted value is compared to the article wise average number of authors and the average number of times the articles were mentioned. The expected values are the averages of all articles in PubMed that have been matched by journal and year of publication. The “PT” predicted values also influence the kind of publishing. Except as specifically stated, self-citations (an author referring to his or her work) are not included in the analysis.
| Variable | Value | |
|---|---|---|
| NumPubs | 708 | Number of recognized PubmedIDs |
| FirstYear | 2000 | Earliest article year |
| LastYear | 2021 | Latest article year |
| AvgAuthors | 6.001 | Average number of authors per article |
| ExpAuthors | 4.756 | Expected number of authors, matched on journal and year |
| RatioAuthors | 1.262 | Ratio of the average number of authors to the expected number |
| AvgCitesAll | 5.743 | Average number of times an article has been cited, including self-citations |
| AvgCites | 5.095 | Average number of times an article has been cited, not including self-citations |
| ExpCites | 2.438 | Expected number of times an article has been cited, not including self-citations, matched on journal and year |
| RatioCites | 2.090 | Ratio of average number of citations (no self-citations) to expected number, matched on journal and year |
| ExpCitesPT | 3.850 | Expected number of citations (no self-citations), matched on journal, year, and publication type |
| RatioCitesPT | 1.323 | Ratio of average number of citations (no self-citations) to expected number, matched on journal, year, and publication type |
| HIndex | 23 | Hirsch-index (using total citations, including self-citations) |
| MIndex | 1.917 | Hirsch-index divided by the number of years since the first publication |
Multiple publishing categories can be attributed to the same article in Medine/PubMed. Articles are matched across all publishing types for computing “PT” values. For instance, an article with the kinds “Abstract; Multicenter Study; Clinical Trial” will only be compared to other articles with those three kinds. As a result, while evaluating the “PT” numbers, take in mind that there are frequently relatively few publications that match on the journal, year, AND all publishing kinds, which might skew the findings.
Search Strategy
⌅For this article, papers are selected from the PubMed database that included the keyword “Social Media” and checked research studies reported in the database between 2000, January and 2021, June.
Sampling
⌅Pubmed database provides coverage on Medline, dental journal, nursing journal. The PubMed database yielded a total of 713 documents while the term “Social Media” was searched. Even though this research has been conducted in a variety of disciplines, we plan to review the published literature in the Indian context. Only papers with the English language were considered.
Data Analysis
⌅713 articles were chosen using a systematic method for social media for health information in India and bibliographical methods, which included a co-citation analysis of reference and text analytics of the combination of bibliometric texts.
3. RESULTS AND DISCUSSION
⌅The categories assigned to an article in Medline/PubMed determine the type of publication. Because an article can have more than one publication type, a single publication may appear more than once in the table below. As a result, in Table II the NumPubs field may contain more than the total number of publications. Here it is clear that percentage of journal article publication is highest. But, the Ratio of average number of citations is highest for ‘Letter’ type of publication. The “Publication Type” expected values are not listed here since the data is already categorized by publication type.
Figure 1 indicates that researchers of social media health information in India have considered their article the following components or keywords (Table III). From Figure 2, it is clear that number of publication is highest on 2020. It might be due to COVID 19.
| PublicationType | NumPubs | %Pubs | FirstYear | LastYear | AvgCites | ExpCites | RatioCites |
|---|---|---|---|---|---|---|---|
| Journal Article | 686 | 96.893 | 2000 | 2021 | 5.114 | 3.924 | 1.303 |
| Research Support, Non-U.S. Gov’t | 98 | 13.842 | 2003 | 2021 | 15.520 | 15.003 | 1.035 |
| Review | 60 | 8.475 | 2004 | 2021 | 7.917 | 5.319 | 1.489 |
| Research Support, N.I.H., Extramural | 15 | 2.119 | 2005 | 2020 | 63.800 | 64.943 | 0.982 |
| Case Reports | 10 | 1.412 | 2007 | 2021 | 3.300 | 1.741 | 1.896 |
| Observational Study | 9 | 1.271 | 2014 | 2021 | 0.333 | 0.652 | 0.511 |
| Letter | 8 | 1.130 | 2015 | 2021 | 10.375 | 2.567 | 4.041 |
| Comparative Study | 8 | 1.130 | 2005 | 2021 | 6.500 | 4.425 | 1.469 |
| Video-Audio Media | 7 | 0.989 | 2015 | 2020 | 118.143 | 117.630 | 1.004 |
| Multicenter Study | 7 | 0.989 | 2007 | 2021 | 4.286 | 2.693 | 1.591 |
| Research Support, U.S.Gov’t, Non-P.H.S. | 6 | 0.847 | 2007 | 2020 | 9.333 | 9.465 | 0.986 |
| Systematic Review | 6 | 0.847 | 2015 | 2021 | 4.333 | 4.138 | 1.047 |
| Editorial | 6 | 0.847 | 2014 | 2020 | 2.000 | 1.717 | 1.165 |
| Randomized Controlled Trial | 5 | 0.706 | 2014 | 2019 | 6.600 | 4.583 | 1.440 |
| Evaluation Study | 4 | 0.565 | 2011 | 2019 | 8.250 | 5.389 | 1.531 |
| Historical Article | 4 | 0.565 | 2000 | 2012 | 2.500 | 2.625 | 0.952 |
| Meta-Analysis | 3 | 0.424 | 2017 | 2018 | 21.667 | 28.817 | 0.752 |
| News | 3 | 0.424 | 2021 | 2021 | 0.333 | 0.167 | 2.000 |
| Research Support, U.S. Gov’t, P.H.S. | 2 | 0.282 | 2003 | 2005 | 12.000 | 8.542 | 1.405 |
| Comment | 2 | 0.282 | 2014 | 2020 | 7.000 | 2.333 | 3.000 |
| Clinical Trial Protocol | 2 | 0.282 | 2018 | 2018 | 1.500 | 1.604 | 0.935 |
| Clinical Trial | 1 | 0.141 | 2004 | 2004 | 8.000 | 5.000 | 1.600 |
| Consensus Development Conference | 1 | 0.141 | 2008 | 2008 | 7.000 | 7.000 | 1.000 |
| Validation Study | 1 | 0.141 | 2016 | 2016 | 5.000 | 4.500 | 1.111 |
| Congress | 1 | 0.141 | 2014 | 2014 | 0.000 | 0.000 | 1.000 |
| Introductory Journal Article | 1 | 0.141 | 2014 | 2014 | 0.000 | 0.000 | 1.000 |
Keyword analyses revealed four subjects in their studies. The following are applicable: Topic/ disease, gender and age group, Systems and software, places.
Classification
⌅-
Topic/ Disease: According to authors (Young & Rice, 2011Young, S. D., & Rice, E. (2011). Online social networking technologies, HIV knowledge, and sexual risk and testing behaviors among homeless youth. AIDS and Behavior, 15(2), 253-260. DOI: https://doi.org/10.1007/s10461-010-9810-0 ; Lariscy et al. , 2010Lariscy, R. W., Reber, B. H., & Paek, H. J. (2010). Examination of media channels and types as health information sources for adolescents: comparisons for black/white, male/female, urban/rural. Journal of Broadcasting & Electronic Media, 54(1), 102-120. DOI: https://doi.org/10.1080/08838150903550444 ; Corley et al. , 2010Corley, C. D., Cook, D. J., Mikler, A. R., & Singh, K. P. (2010). Text and structural data mining of influenza mentions in web and social media. International Journal of Environmental Research and Public Health, 7(2), 596-615. DOI: https://doi.org/10.3390/ijerph7020596 ; Takahashi et al. ,2009Takahashi, Y., Uchida, C., Miyaki, K., Sakai, M., Shimbo, T., & Nakayama, T. (2009). Potential benefits and harms of a peer support social network service on the internet for people with depressive tendencies: qualitative content analysis and social network analysis. Journal of Medical Internet Research, 11(3), e29. DOI: https://doi.org/10.2196/jmir.1142 ; Mukherjee et al. ,2019Mukherjee, S. K., Kumar, J., Jha, A. K., & Rani, J. R. (2019). Role of social media promotion of prescription drugs on patient belief-system and behaviour. International Journal of e-Collaboration (IJeC), 15(2), 23-43.; Selkie et al. ,2011Selkie, E. M., Benson, M., & Moreno, M. (2011). Adolescents’ views regarding uses of social networking websites and text messaging for adolescent sexual health education. American Journal of Health Education, 42(4), 205-212. DOI: https://doi.org/10.1080/19325037.2011.10599189 ; Liang & Mackey, 2011Liang, B. A., & Mackey, T. (2011). Direct-to-consumer advertising with interactive internet media: global regulation and public health issues. Jama, 305(8), 824-825. DOI: https://doi.org/10.1001/jama.2011.203 ), in their findings, researchers identified the usage of social media in the population for different diseases or health conditions. However, standardization of the devices and regulatory approvals remain inadequate and would need an adequate process to improve their function.
-
Classification according to gender and age: In India, authors have studied everyone from children to the elderly in the field of social media (Mukherjee et al. , 2019Mukherjee, S. K., Kumar, J., Jha, A. K., & Rani, J. R. (2019). Role of social media promotion of prescription drugs on patient belief-system and behaviour. International Journal of e-Collaboration (IJeC), 15(2), 23-43.; Van de Belt et al. , 2013Van de Belt, T. H., Engelen, L. J., Berben, S. A., Teerenstra, S., Samsom, M., & Schoonhoven, L. (2013). Internet and social media for health-related information and communication in health care: preferences of the Dutch general population. Journal of Medical Internet Research, 15(10), e220. DOI: https://doi.org/10.2196/jmir.2607 ; Chou et al. , 2011Chou, W. Y. S., Hunt, Y., Folkers, A., & Augustson, E. (2011). Cancer survivorship in the age of YouTube and social media: a narrative analysis. Journal of Medical Internet Research, 13(1), e7. DOI: https://doi.org/10.2196/jmir.1569 ; Mukherjee et al. , 2021Mukherjee, S. K., Pandey, V., Kumar, J., & Jha, A. (2021). A Study of user Profile and Their Attitudes about Social Media Promotion of Prescription Drugs in Eastern India. Linguistica Antverpiensia, 219-245. DOI: https://doi.org/10.4018/IJeC.2019040102 ). This ensures that social media applications and technologies are available to people of all ages.
-
Systems and software: The study found that health information is being searched in social media (Volpp & Mohta, 2018Volpp, K. G., & Mohta, N. S. (2018). Patient engagement survey: Social networks to improve patient health. NEJM Catalyst, 4(1).; Zhao et al. , 2020Zhao, Y., Cheng, S., Yu, X., & Xu, H. (2020). Chinese public’s attention to the COVID-19 epidemic on social media: observational descriptive study. Journal of Medical Internet Research, 22(5), e18825. DOI: https://doi.org/10.2196/18825 ; Aparicio-Martinez et al. , 2019Aparicio-Martinez, P., Perea-Moreno, A. J., Martinez-Jimenez, M. P., Redel-Macías, M. D., Vaquero-Abellan, M., & Pagliari, C. (2019). A bibliometric analysis of the health field regarding social networks and young people. International Journal of Environmental Research and Public Health, 16(20), 4024. DOI: https://doi.org/10.3390/ijerph16204024 ). Facebook, Twitter, Youtube, Blog, etc. are known as the social network for health Communication. It was determined that these social network apps can be found in smartphone features and software, as well as play store apps.
-
Places: The importance of social media for sharing or seeking health information in India cannot be ignored. The marginalized group can use it for their benefit (Jha & Pandey, 2017Jha, A., & Pandey, J. R. (2017). An empirical note on health information digital divide: a study of Indian patients. International Journal of Asian Business and Information Management (IJABIM), 8(2), 15-34. DOI: https://doi.org/10.4018/IJABIM.2017040102 ). People living in developing places have started using social media more (Barrios et al. , 2019Barrios, C., Flores, E., Martínez, M. Á., & Ruiz-Martínez, M. (2019). Is there convergence in international research collaboration? An exploration at the country level in the basic and applied science fields. Scientometrics, 120(2), 631-659. DOI: https://doi.org/10.1007/s11192-019-03133-9 ; Pai & Alathur, 2019Pai, R. R., & Alathur, S. (2019). Predicting Mobile Health Technology Acceptance by the Indian Rural Community: A Qualitative Study. International Journal of Electronic Government Research (IJEGR), 15(4), 37-62. DOI: https://doi.org/10.4018/IJEGR.2019100103 ; Mukherjee et al. , 2021Mukherjee, S. K., Pandey, V., Kumar, J., & Jha, A. (2021). A Study of user Profile and Their Attitudes about Social Media Promotion of Prescription Drugs in Eastern India. Linguistica Antverpiensia, 219-245. DOI: https://doi.org/10.4018/IJeC.2019040102 ).
| Classification | Components/ Keywords |
|---|---|
| Topic | Covid 19, sars, sexual, nutrition, cancer, hiv, tuberculosis, zika, obesity, smoking, metal, stress, suicide, vaccine, tobacco, anxiety, depression, presbyopia, diabetes, ophthalmic, pregnancy, rheumatology, cardiovascular, coronaphobia, Rabies, Dengue, Chikungunia, cardiac, Overweight, Biopsy, Cervical, Chronic, Cyberchondria, Pediatrics, Optometry, COPD, Dyssomnia, Risk, Virus, Polio, Alcohol, breastfeeding, breeding, leptospirosis, cardiopulmonary, liver, laparoscopic, Measles-Rubella, pancreatic |
| Media | Facebook, tweet, digital, YouTube, WhatsApp, smartphone, telemedicine, Google, online, website, blog, internet |
| Places | West Bengal, Uttar Pradesh, Kerala, Amritsar, Jharkhand, Madhya Pradesh, Chennai, Mumbai, Chhattisgarh, urban, rural, Delhi, Andhra Pradesh, Gujarat, Bihar, Karnataka, village, India, Pondicherry |
| Gender & age group | Children, adult, young, aged, students, parents, adolescents, males, women, youth, grandmother, infant, boys, men, young, minor |
To sum up, the keywords reflect that the social media researches on basic health conditions and health communication in India showed the need and relevance for developing countries and rural areas.
Top Journals
⌅Table V shows the number of journals (NumPubs) and the share of the total published publications (percentage of pubs) appear for each journal. Here it is clear that ‘Journal of Family Medicine and Primary Care’ have more publication; but, ratio of average number of citations is highest in ‘Asian Journal of Psychiatry’. The quotes have the same significance as the summary table in general.
| Journal | NumPubs | %Pubs | FirstYear | LastYear | AvgCites | ExpCites | RatioCites | ExpCitesPT | RatioCitesPT |
|---|---|---|---|---|---|---|---|---|---|
| J Family Med Prim Care | 24 | 3.390 | 2016 | 2021 | 1.000 | 0.650 | 1.539 | 0.941 | 1.063 |
| PLoS One | 15 | 2.119 | 2012 | 2021 | 2.733 | 3.690 | 0.741 | 3.440 | 0.795 |
| Indian J Med Ethics | 13 | 1.836 | 2012 | 2021 | 0.692 | 0.659 | 1.051 | 0.539 | 1.284 |
| BMC Public Health | 10 | 1.412 | 2011 | 2021 | 6.200 | 4.354 | 1.424 | 3.940 | 1.574 |
| J Educ Health Promot | 10 | 1.412 | 2012 | 2021 | 1.200 | 0.857 | 1.400 | 0.809 | 1.483 |
| BMJ Open | 10 | 1.412 | 2013 | 2021 | 0.400 | 1.804 | 0.222 | 1.744 | 0.229 |
| Asian J Psychiatr | 9 | 1.271 | 2014 | 2020 | 30.889 | 2.843 | 10.863 | 2.889 | 10.694 |
| J Med Internet Res | 9 | 1.271 | 2014 | 2021 | 10.889 | 8.319 | 1.309 | 9.519 | 1.144 |
| Indian J Public Health | 8 | 1.130 | 2007 | 2020 | 4.750 | 1.865 | 2.546 | 1.827 | 2.600 |
| Indian Pediatr | 7 | 0.989 | 2008 | 2020 | 5.571 | 1.896 | 2.938 | 3.820 | 1.458 |
Table VI shows year wise number of articles (NumPubs), number of times any article in that year (NumCitesAll) has been cited, inclusive of self-quotation (CumPubs), the number of times no articles have been mentioned in the same year (NumCites); the number of cumulative articles mentioned, including self-quotations (CumCitesAll); the cumulative number of articles cited in that year (CumCitesAL); (CumCites).
| PubYear | NumPubs | NumCitesAll | NumCites | CumPubs | CumCitesAll | CumCites |
|---|---|---|---|---|---|---|
| 2021 | 115 | 965 | 865 | 708 | 4066 | 3607 |
| 2020 | 220 | 1221 | 1087 | 593 | 3101 | 2742 |
| 2019 | 71 | 324 | 283 | 373 | 1880 | 1655 |
| 2018 | 57 | 431 | 383 | 302 | 1556 | 1372 |
| 2017 | 55 | 350 | 315 | 245 | 1125 | 989 |
| 2016 | 34 | 253 | 224 | 190 | 775 | 674 |
| 2015 | 29 | 137 | 115 | 156 | 522 | 450 |
| 2014 | 30 | 110 | 96 | 127 | 385 | 335 |
| 2013 | 11 | 96 | 82 | 97 | 275 | 239 |
| 2012 | 24 | 58 | 53 | 86 | 179 | 157 |
| 2011 | 11 | 44 | 39 | 62 | 121 | 104 |
| 2010 | 5 | 22 | 20 | 51 | 77 | 65 |
| 2009 | 7 | 19 | 18 | 46 | 55 | 45 |
| 2008 | 4 | 14 | 11 | 39 | 36 | 27 |
| 2007 | 9 | 7 | 6 | 35 | 22 | 16 |
| 2006 | 4 | 5 | 5 | 26 | 15 | 10 |
| 2005 | 5 | 6 | 2 | 22 | 10 | 5 |
| 2004 | 7 | 1 | 0 | 17 | 4 | 3 |
| 2003 | 4 | 1 | 1 | 10 | 3 | 3 |
| 2002 | 4 | 2 | 2 | 6 | 2 | 2 |
| 2000 | 2 | 0 | 0 | 2 | 0 | 0 |
The bibliographic analysis of the articles will help to identify research gaps and potential prospects (Xu et al. , 2020Xu, S., Zhang, X., Feng, L., & Yang, W. (2020). Disruption risks in supply chain management: a literature review based on bibliometric analysis. International Journal of Production Research, 58(11), 3508-3526. DOI: https://doi.org/10.1080/00207543.2020.1717011 ). The study of co-quotation analytics and the bibliographic mix of chosen papers are discussed in the following pages.
Study Characteristics
⌅Identification of co-occurrences, co-authorships based on author keywords are among the research features of the selected 713 articles. This report demonstrates how social media is used in India for health information. It also highlights the most active, most studied fields for potential advancements in the field assessment. The results of the experiments are discussed in the following pages.
Commonly associated author keywords in the research articles
⌅The network visualization analysis for the authors’ keywords is seen in Figure 3. To map the results, VOSviewer was used by Van Eck and Waltman (2009, 2013Van Eck, N. J., & Waltman, L. (2013). VOSviewer manual. Leiden: Univeristeit Leiden, 1(1), 1-53.). This software is not only useful for generating, analyzing, and exploring network data maps, but it is also useful for bibliometric data analysis (Van Eck & Waltman, 2013Van Eck, N. J., & Waltman, L. (2013). VOSviewer manual. Leiden: Univeristeit Leiden, 1(1), 1-53.). Perianes-Rodriguez et al. (2016)Perianes-Rodriguez, A., Waltman, L., & Van Eck, N. J. (2016). Constructing bibliometric networks: A comparison between full and fractional counting. Journal of Informetrics, 10(4), 1178-1195. DOI: https://doi.org/10.1016/j.joi.2016.10.006 suggested the fractional counting process and the overall sensitivity of the relation to normalizing the effects. For co-occurrence data, the Van Eck and Waltman data index (Pai & Alathur, 2019Pai, R. R., & Alathur, S. (2019). Predicting Mobile Health Technology Acceptance by the Indian Rural Community: A Qualitative Study. International Journal of Electronic Government Research (IJEGR), 15(4), 37-62. DOI: https://doi.org/10.4018/IJEGR.2019100103 ) is used.
A total of 727 keywords out of 3105 were obtained for this research study with 713 papers by setting a metric for counting, form, analytical unit, and threshold value as fraction metric, coexistence analysis, and minimum occurrence of keywords as 2.
The size of the circle in Figure 3 reflects the frequency of the keyword. The larger the diameter, the more often the keyword appears in social media for health information journals. The distance between them shows the topic’s relative intensity and similarity (Guo, et al. , 2019Guo, Y. M., Huang, Z. L., Guo, J., Li, H., Guo, X. R., & Nkeli, M. J. (2019). Bibliometric analysis on smart cities research. Sustainability, 11(13), 3606. DOI: https://doi.org/10.3390/su11133606 ). In this report, keywords like humans, social media, India, Covid 19 have a higher weight. The use of the same color in different publications indicated a related subject.
The network visualization map displayed in Figure 3 has 8 clusters that describe the subfields of social media. The Yellow Cluster comprises varieties of family problems and their information such as family planning services, survey, health, postnatal care, risk factors, breast cancer awareness, geography, fertility, health education, family characteristics, etc. The red cluster included social media, online learning, pharmacovigilance, machine learning, natural language processing, algorithms, neural networks, etc. The keywords in the green cluster are human, measles, management, cluster analysis, infant nutrition, qualitative research, adolescence, diet, etc. Next, the keywords in the blue color cluster are correlated with India, social phobia, students, universities, etc.
The purple cluster contains pathology, what’s app, Facebook, etc. keywords which are there. A cluster of other blue colors are combined of Covid 19, content analysis, gender, fake news, sentiment analysis, deep learning, anxiety disorder, information, periodical as a topic, Twitter, etc. The orange cluster, includes key terms basic reproduction number, culture media, circadian rhythm, parents, social media use, adolescents, behavior, students, etc.
Co-citation analysis
⌅By reviewing quotations, this paper examines “the frequency with which two documents are cited together by other documents”(Small, 1973Small, H. (1973). Co‐citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265-269. DOI: https://doi.org/10.1002/asi.4630240406 ). It is also very important (Figure 4). A total of 91 papers out of 4488 were obtained for this research with 713 papers and the minimum number of papers per author is 2.
This diagram illustrates the interactions between the primary authors and the remaining researchers in the field of social media connected to health information in India. The first cluster, led by Dash, Chinmaya; Gupta, Ravi and Raheja, Amol have the most members. There are a total of 8 writers. The next cluster (green) has 7 writers, the most prominent of one is Kar, Sujita Kumar. Both yellow and blue cluster has the same number of writers; that is 6. For the yellow cluster Banerjee, Debanjan is the most prominent one and in the blue cluster Garg, Kanwaljeet, and Chairasia, Bipin are the most prominent ones. The purple cluster contains 5 writers, where each author has the same importance. In the other blue cluster 4 writers are there and among them Grover, Sandeep is the most prominent one.
Co-occurrence Map using Text
⌅A total of 3675 terms out of 20321 were obtained for this study with 713 papers and minimum occurrence of terms as 2. 60% of most relevant terms are selected out of 3675 which is 2205
The size of the circle in Figure 5, reflects the frequency of the keyword. The larger the diameter, the more often the keyword appears in social media for health information journals. The distance between them shows the topic’s relative intensity and similarity (Guo et al. , 2019Guo, Y. M., Huang, Z. L., Guo, J., Li, H., Guo, X. R., & Nkeli, M. J. (2019). Bibliometric analysis on smart cities research. Sustainability, 11(13), 3606. DOI: https://doi.org/10.3390/su11133606 ). In this report, keywords like workers, group discussion, detection, and surgeon have a higher weight. The use of the same color in different publications indicated a related subject.
The network visualization map displayed in Figure 5 has 9 clusters that describe the subfields of social media. The Yellow Cluster comprises college, self-breast examination, medical science, socioeconomic stratum, etc. The red cluster included detection, fake news, reconstruction, processing, computing, modeling, hybrid approach, architecture, cell, gene, generation, etc. The keywords in the green cluster are biopsy, pathologist, mask, specialty, abnormality, coordination, comprehensive strategy, daily activity, etc. Next, the keywords in the blue color cluster are correlated with ultrasonography, internalization, artery intima, cardiovascular disease, origin, glucose, BMI, depressive symptom, etc.
The purple cluster contains young child, infant, IPC, differential impact, program activity, etc. keywords that are there. A cluster of other blue colors are combined of the social media channel, meta-analysis, peer review, citation, altmetric score, e-survey, cloud, bridge, artificial intelligence, etc. The orange cluster includes key terms caste, residence, national health survey, wealth index. Young mother, recent birth, effective implementation, social group, socio-economic group, interval, marital status, logistic regression model, etc. The pink cluster includes keywords like group discussion, media campaign, content analysis, suicide, broader health analysis, important challenge, Chennai, college suicide, media professional, qualitative interview, media guideline, etc. Other green cluster contains Uttar Pradesh, northern India, pediatrics, informal learning, businessman, mobilizer, Hindi, current situation, force, sale, coding, neglect, process, interrupt transmission, etc. keywords. The grey cluster includes ethical aspects, sex selection, couple, live birth, baby boy, release, colony, activity pattern, circadian clock, etc. as the keyword. Other pink colors have surgeon, clinical practice, mainstream, social media channel, variance, main source, the social media network, privilege, agreement, demographical data, chat group, Linkedin, post-graduate training, common degenerative condition, invasive spinal surgery, etc. are there as keywords.
Martinez et al. (2019)Aparicio-Martinez, P., Perea-Moreno, A. J., Martinez-Jimenez, M. P., Redel-Macías, M. D., Vaquero-Abellan, M., & Pagliari, C. (2019). A bibliometric analysis of the health field regarding social networks and young people. International Journal of Environmental Research and Public Health, 16(20), 4024. DOI: https://doi.org/10.3390/ijerph16204024 in their study have shown that Journals are having largest number of publication a finding which is similar to our study. Like this study it also has ‘humans’ as keyword highest number of times and it is clear in Co-occurrence study. It, Pai & Alathur (2021)Pai, R. R., & Alathur, S. (2021). Bibliometric analysis and methodological review of mobile health services and applications in India. International Journal of Medical Informatics, 145, 104330., Zyoud et al. (2018)Zyoud, S. E. H., Sweileh, W. M., Awang, R., & Al-Jabi, S. W. (2018). Global trends in research related to social media in psychology: mapping and bibliometric analysis. International Journal of Mental Health Systems, 12(1), 1-8. and Madjido et al. (2019)Madjido, M., Espressivo, A., Maula, A. W., Fuad, A., & Hasanbasri, M. (2019). Health information system research situation in Indonesia: A bibliometric analysis. Procedia Computer Science, 161, 781-787. DOI: https://doi.org/10.1016/j.procs.2019.11.183 also has similar publication trends.
In Co-occurance analysis ‘Telemedicine’ is an important keyword (Pai & Alathur, (2021)Pai, R. R., & Alathur, S. (2021). Bibliometric analysis and methodological review of mobile health services and applications in India. International Journal of Medical Informatics, 145, 104330.; Madjido et al. (2019)Madjido, M., Espressivo, A., Maula, A. W., Fuad, A., & Hasanbasri, M. (2019). Health information system research situation in Indonesia: A bibliometric analysis. Procedia Computer Science, 161, 781-787. DOI: https://doi.org/10.1016/j.procs.2019.11.183 ); as is echoed in our article. However Co-citation analysis in our study is marketly different; perhaps they have worked on M-health.
Zyoud et al. (2018)Zyoud, S. E. H., Sweileh, W. M., Awang, R., & Al-Jabi, S. W. (2018). Global trends in research related to social media in psychology: mapping and bibliometric analysis. International Journal of Mental Health Systems, 12(1), 1-8. mentions 10 most active journals which are not a part of our findings. This could be because we have focused on social media where as he has focused on Internet.
The usefulness of mHealth is the other important finding in the clustered papers. MHealth has been used to combine health awareness with phone (Pai & Alathur, 2020Pai, R. R., & Alathur, S. (2020). Bibliometric Analysis and Methodological Review of Mobile Health Services and Applications in India. International Journal of Medical Informatics, 104330. DOI: https://doi.org/10.1016/j.ijmedinf.2020.104330 ).
4. CONCLUSION
⌅The objective of this paper has been to develop better insight of literature on social media based health information. From the study we find that ‘Public Health’ has been on top fields that has been studied (Table I). Social media therefore appears to be important in the context of public health. Hence it may be assumed that social media could play an important role in prevention of disease and spreading awareness. We also find that the journals are the most common publications espousing social media and health information. This could be because the journals major reason are the major publication medium. But it could also be because social media health communication is being considered by serious researchers (Table II). Average citation is highest for ‘Video-Audio Media’ (Table II); this could be because Video-audio media appears more credible or perhaps because it invites greater attention. Journal of Family Medicine Primary Care has published highest number of paper (Table III). In 2020 number of publication on this topic was highest (Table IV); that is why citation is also highest in this year, might be due to pandemic. From Figure 4, it is clear that very few author have studied in this area. This reflect that it is an emerging issue for researchers but it is expected that more number of researches in India and others developing countries will eventually explore this area. ‘Worker’ is most frequent subfield appearing in literature. This may be explained on the basis of lower income group finding social media a cheaper and better option for health information.
Implications
⌅Social media health information has arrived and will be more prevalent in future as reach of social media extends to developing and under developing countries. The regulators therefore has the challenges to regulate it in such a way that its positive influence remains higher than its negative connotations. Health care Regulators should frame policies so that public health may benefit immensely form the use of social media base health information. Prevention of disease is important and social media can play an important role. As this is an emerging issue academicians and researchers are required to pay greater attention and develop new theories and models for better societal health outcomes. As it appears social media is a greater importance for marginalized people; health care providers and regulators must take precautions to avoid possible negative outcomes.
Research Gaps and Future Scope
⌅There are some flaws in the study. First, there were only papers from publications of the same ‘PubMed’ index, with no listing of articles from other sources. Second, the science Only English literature is permitted; no other languages are possible. The total consolidation of the study findings could be harmed (Müller et al. , 2018Müller, A. M., Maher, C. A., Vandelanotte, C., Hingle, M., Middelweerd, A., Lopez, M. L., & Wark, P. A. (2018). Physical activity, sedentary behavior, and diet-related eHealth and mHealth research: bibliometric analysis. Journal of Medical Internet Research, 20(4), e8954. DOI: https://doi.org/10.2196/jmir.8954 ). Third, “social media” is the keyword for this review. The future researchers in this respect may include databases other than PubMed. Also newer keywords such as social network, social networking sites may be used for deeper analysis. Finally comparisons of two or distinct regions may provide information on differences across geography.