Research Articles
Vol. 3 No. 2 (2021)
The Network Life of Non-biomedical Knowledge: Mapping Vietnamese Traditional Medicine Discourses on Facebook
The University of Melbourne
Abstract
Traditional medicine is hugely popular throughout Southeast Asia and other parts of the world. The development of the internet and online social networks in these contexts has enabled a significant proliferation of non-biomedical knowledge and practices via platforms such as Facebook. People use Facebook to advocate for non- biomedical alternatives to unaffordable biomedicine, share family medical recipes, discuss medicinal properties of indigenous plants, buy and sell these plants, and even crowdsource disease diagnoses. This paper examines the network characteristics of, and discourses present within, three popular Vietnamese non-biomedical knowledge Facebook sites over a period of five years. These large-scale datasets are studied using social network analysis and generative statistical models for topic analysis (Latent Dirichlet allocation). Forty-nine unique topics were quantitatively identified and qualitatively interpreted. Among these topics, themes of religion and philanthropy, critical discussions of traditional medicine, and negotiations involving overseas Vietnamese were particularly notable. Although non-biomedical networks on Facebook are growing both in terms of scale and popularity, sub-network comment activities within these networks exhibit ‘small world’ characteristics. This suggests that social media seem to be replicating existing social dynamics that historically enable the maintenance of traditional forms of medical knowledge, rather than transforming them here.
References
- Albright, J. (2018). The Graph API: Key Points in the Facebook and Cambridge Analytica Debacle. Medium. Available at https://medium.com/tow-center/the-graph-api-key-points-in-the-facebook-and-cambridge-analytica-debacle-b69fe692d747
- Arun, R., Suresh, V., Madhavan, C. V., & Murthy, M. N. (2010). On finding the natural number of topics with latent dirichlet allocation: Some observations. In Pacific-Asia conference on knowledge discovery and data mining (pp. 391-402). Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-13657-3_43
- Blei, D. M. (2012). Surveying a suite of algorithms that offer a solution to managing large document archives. Communication of the ACM, 55(4), 77-84. DOI: 10.1145/2133806.2133826
- Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of machine Learning research, 3, 993-1022.
- Blei, D.M., Mcauliffe, J. (2007). Supervised topic models. Neural Information Processing Systems Proceedings. 121-128. DOI: 10.5555/2981562.2981578
- Caci, B., Cardaci, M., & Tabacchi, M. E. (2012). Facebook as a small world: a topological hypothesis. Social Network Analysis and Mining, 2(2), 163-167. DOI: 10.1007/s13278-011-0042-8
- Cao, J., Xia, T., Li, J., Zhang, Y., & Tang, S. (2009). A density-based method for adaptive LDA model selection. Neurocomputing, 72(7-9), 1775-1781. DOI: 10.1016/j.neucom.2008.06.011
- Castells, M. (1996). The rise of the network society. Malden, Mass, Blackwell Publishers. DOI: 10.1002/9781444319514
- Castells, M. (1997). Power of identity: The information age: Economy, society, and culture. Blackwell Publishers. DOI: 1002/9781444318234
- Castells, M. (1998). End of Millennium: The Information Age: Economy, Society and Culture. Oxford and Maiden: Blackwell Publishers.
- Catanese, S. A., De Meo, P., Ferrara, E., Fiumara, G., & Provetti, A. (2011, May). Crawling facebook for social network analysis purposes. In Proceedings of the international conference on web intelligence, mining and semantics. pp. 1-8. DOI: 10.1145/1988688.1988749
- Craig, D. (2002). Familiar medicine: Everyday health knowledge and practice in today's Vietnam. University of Hawaii Press. USA. DOI: 10.1017/S0021911804001500
- Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695(5), 1-9.
- DiMaggio, P. (2015). Adapting computational text analysis to social science (and vice versa). Big Data & Society, 2(2), 1-5. DOI: 10.1177/2053951715602908
- Epskamp, S., Costantini, G., Haslbeck, J., Cramer, A. O., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2019). Package ‘qgraph’. Available at https://cran.r-project.org/web/packages/qgraph/qgraph.pdf.
- Feather, J., 2013. The information society: A study of continuity and change. 6th edition. Facet publishing. London, UK. DOI: 10.1108/00220410510632121
- Gerlach, M., Peixoto, T. P., & Altmann, E. G. (2018). A network approach to topic models. Science advances, 4(7), eaaq1360. DOI: 10.1126/sciadv.aaq1360
- Graham, T. & Ackland, R. (2016). SocialMediaLab: Tools for collecting social media data and generating networks for analysis. CRAN (The Comprehensive R Archive Network). Retrieved from https://cran.rproject.org/web/packages/SocialMediaLab/SocialMediaLab.pdf
- Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National academy of Sciences, 101(suppl 1), 5228-5235. DOI: 10.1073/pnas.0307752101
- Grün, B. & Hornik, K. (2011). topicmodels: An R package for fitting topic models. Journal of Statistical Software, 40(13), 1-30. DOI: 10.18637/jss.v040.i13
- Hether, H. J., Murphy, S. T., & Valente, T. W. (2016). A social network analysis of supportive interactions on prenatal sites. Digital health, 2, pp. 1-12. DOI: 10.1177/2055207616628700
- Hoang, T. A. (2015). Modeling user interest and community interest in microbloggings: An integrated approach. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 708-721). Springer, Cham. DOI: 10.1007/978-3-319-18038-0_55
- Hou-Liu, J. (2018). Benchmarking and Improving Recovery of Number of Topics in Latent Dirichlet Allocation Models. Available at https://pdfs.semanticscholar.org/2175/aa77463e23da96281cc2fb5125e0b9de3bbd.pdf
- Humphries, M. D., & Gurney, K. (2008). Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence. PLoS One, 3(4), e0002051. DOI: 10.1371/journal.pone.0002051
- Kemp, S., 2019a, Digital 2019: Global Digital Overview, DataReportal, viewed 12 December 019 < https://datareportal.com/reports/digital-2019-global-digital-overview>
- Kemp, S., 2019b, Digital 2019: Vietnam, DataReportal, viewed 12 December 2019 < <https://datareportal.com/reports/digital-2019-vietnam>
- Latour, B. (2005). Reassembling the social: An introduction to actor-network-theory: Oxford University Press. UK.
- Law, J., & Mol, A. (1995). Notes on materiality and sociality. The sociological review, 43(2), 274-294. DOI: 10.1111/j.1467-954X.1995.tb00604.x
- Liebeskind, C., & Liebeskind, S. (2018). Identifying Abusive Comments in Hebrew Facebook. In 2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE) (pp. 1-5). IEEE. DOI: 10.1109/ICSEE.2018.8646190
- Lu, X., Yu, Z., Guo, B., & Zhou, X. (2014). Predicting the content dissemination trends by repost behavior modeling in mobile social networks. Journal of Network and Computer Applications, 42, 197-207. DOI: 10.1016/j.jnca.2014.01.015
- Ma, S., Zhang, C., & He, D. (2016). Document representation methods for clustering bilingual documents. In Proceedings of the 79th ASIS&T Annual Meeting: Creating Knowledge, Enhancing Lives through Information & Technology (p. 65). American Society for Information Science. DOI: 10.1002/pra2.2016.14505301065
- Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., Pfetscha, B., Heyerc, G., Reberd, U., Ha?usslerd, T., Schmid-Petrie, H. & Adam, S. (2018). Applying LDA topic modeling in communication research: Toward a valid and reliable methodology. Communication Methods and Measures, 12(2-3), 93-118. DOI: 10.1080/19312458.2018.1430754
- Minh Huy (2018). ‘Overseas remittances to Vietnam continue increasing’. Sai Gon Giai Phong News Online. Available at https://m.sggpnews.org.vn/business/overseas-remittances-to-vietnam-continue-increasing-79438.html.
- Mol, A. (2008). The logic of care: Health and the problem of patient choice. Routledge. UK. DOI: 10.1111/j.1467-9566.2009.1168_2.x
- Mol, A.P., 2009. Environmental governance through information: China and Vietnam. Singapore Journal of Tropical Geography, 30(1), pp.114-129. DOI: 10.1111/j.1467-9493.2008.00358.x
- Monnais, L., Thompson, C. M., & Wahlberg, A. (Eds.). (2011). Southern medicine for southern people: Vietnamese medicine in the making. Cambridge Scholars Publishing. UK.
- Nguyen, D. (2019). Mapping knowledge domains of non-biomedical modalities: A large-scale co-word analysis of literature 1987–2017. Social Science & Medicine, 233, 1-12. DOI: 10.1016/j.socscimed.2019.05.044
- Nikita, M. (2016). Package ‘ldatuning’: Tuning of the Latent Dirichlet Allocation Models Parameters’. r package version 1.0.0. Available at https://CRAN.R project.org/package=ldatuning
- Ortmann S. (2017) The Vietnamese Government and Institutional Reforms. In: Environmental Governance in Vietnam. Palgrave Macmillan, Cham. DOI: 10.1007/978-3-319-49760-0_3
- Paul, M. J., & Dredze, M. (2012). A model for mining public health topics from Twitter. Health, 11(16-16), 1.
- Ríssola, E. A., Bahrainian, S. A., & Crestani, F. (2019). Anticipating Depression Based on Online Social Media Behaviour. In International Conference on Flexible Query Answering Systems (pp. 278-290). Springer, Cham. DOI: 10.1007/978-3-030-27629-4_26
- Singer, M., Baer, H., Long, D., & Pavlotski, A. (2019). Introducing medical anthropology: a discipline in action. Rowman & Littlefield. USA.
- Smith, N., & Graham, T. (2019). Mapping the anti-vaccination movement on Facebook. Information, Communication & Society, 22(9), 1310-1327. DOI: 10.1080/1369118X.2017.1418406
- Statt, N. 2019. ‘Facebook is redesigning its core app around the two parts people actually like to use’. The Verge. Available at https://www.theverge.com/2019/4/30/18523265/facebook-events-groups-redesign-news-feed-features-f8-2019.
- Thompson, C. M. (2017). The implications of gia truy?n: Family transmission texts, medical authors, and social class within the healing community in Vietnam. South East Asia Research, 25(1), 34-46. DOI: 10.1177/0967828X17690045
- Uzzi, B., & Spiro, J. (2005). Collaboration and creativity: The small world problem. American journal of sociology, 111(2), 447-504. DOI: 10.1086/432782
- Van Dijck, J., & Poell, T. (2013). Understanding social media logic. Media and communication, 1(1), 2-14. DOI: 10.17645/mac.v1i1.70
- Vu, T., Nguyen, D. Q., Nguyen, D. Q., Dras, M., & Johnson, M. (2018). VnCoreNLP: A Vietnamese Natural Language Processing Toolkit. arXiv preprint arXiv:1801.01331. DOI: 10.18653/v1/N18-5012
- Wang, W., Chen, R. R., Ou, C. X., & Ren, S. J. (2019). Media or message, which is the king in social commerce?: An empirical study of participants' intention to repost marketing messages on social media. Computers in Human Behavior, 93, 176-191. DOI: 10.1016/j.chb.2018.12.007
- Watts, D. J. (1999). Networks, dynamics, and the small-world phenomenon. American Journal of Sociology, 105(2), 493-527. DOI: 10.1086/210318
- Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small world’ networks. Nature, 393(6684), 440-442. DOI: 10.1038/30918
- Wittel, A. (2001). Toward a network sociality. Theory, culture & society, 18(6), 51-76. DOI: 10.1177/026327601018006003
- Wohlgemuth, J., & Matache, M. T. (2012). Small world properties of Facebook group networks. University of Nebraska at Omaha. ProQuest Dissertations Publishing. DOI: 10.25088/ComplexSystems.23.3.197
- Zhao, Y. (2018). An Investigation of Autism Support Groups on Facebook. Doctoral disseration. The University of Wisconsin-Milwaukee. Available at https://dc.uwm.edu/cgi/viewcontent.cgi?article=2968&context=etd.
- Zuboff, S. (2019). The age of surveillance capitalism: The fight for a human future at the new frontier of power. PublicAffairs Hachette Book Group. NY, US.