Lan truyền thông tin trong khủng hoảng thực phẩm: Phân tích nội dung về thịt heo dịch bệnh trên nền tảng TikTok và YouTube tại Việt Nam
DOI:
https://doi.org/10.24311/jabes/2026.37.1.4Keywords:
Crisis communication, Social network analytics, Information diffusion, TikTok, YouTube, Food safetyAbstract
The development of social media has fundamentally changed the way crisis information is generated, disseminated, and received. This study analyzes the roles of TikTok and YouTube in disseminating information about Group X's "sick pork" crisis in Vietnam using network analysis. Data were collected from May 30 to June 15, 2025, including user–hashtag networks on TikTok (584 nodes, 726 edges) and video-level data on YouTube (60 videos). The study uses graph theory metrics such as centrality, betweenness, closeness, and PageRank, community detection using the Louvain algorithm, and force-directed layout visualization. For YouTube, the study adds temporal analysis, keyword extraction, and comment sentiment analysis. The results show that TikTok spreads information quickly due to its recommendation and hashtag mechanisms, while YouTube promotes in-depth discussions and clearly reflects community sentiment. Both platforms complement each other in shaping and amplifying crisis messages. The study recommends that businesses and regulators monitor multiple platforms, tailor responses to channel characteristics, and apply network analytics to identify key actors, mitigate negative impacts, and support crisis communications.
References
Alexander, D. E. (2014). Social media in disaster risk reduction and crisis management. Science and Engineering Ethics, 20(3), 717-733. https://doi.org/10.1007/s11948-013-9502-z
Ausat, A. M. A. (2023). The role of social media in shaping public opinion and its influence on economic decisions. Technology and Society Perspectives (TACIT), 1(1), 35-44. https://doi.org/10.61100/tacit.v1i1.37
Austin, L., Fisher Liu, B., & Jin, Y. (2012). How audiences seek out crisis information: Exploring the social-mediated crisis communication model. Journal of Applied Communication Research, 40(2), 188-207. https://doi.org/10.1080/00909882.2012.654498
Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A theory of fads, fashion, custom, and cultural change as informational cascades. Journal of Political Economy, 100(5), 992-1026. https://doi.org/10.1086/261849
Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. https://doi.org/10.1088/1742-5468/2008/10/P10008
Boin, A. (2009). The new world of crises and crisis management: Implications for policymaking and research. Review of Policy Research, 26(4), 367-377. https://doi.org/10.1111/j.1541-1338.2009.00389.x
Boin, A., Rhinard, M., & Ekengren, M. (2014). Managing transboundary crises: The emergence of European Union capacity. Journal of Contingencies and Crisis Management, 22(3), 131-142. https://doi.org/10.1111/1468-5973.12052
Civelek, M. E., Çemberci, M., & Eralp, N. E. (2016). The role of social media in crisis communication and crisis management. International Journal of Research in Business & Social Science, 5(3), 111-120. https://doi.org/10.20525/ijrbs.v5i3.279
Coombs, W. T. (2007). Ongoing Crisis Communication: Planning, Managing, and Responding (2nd ed.). Thousand Oaks, CA: Sage.
Coombs, W. T., & Holladay, S. J. (2012). The paracrisis: The challenges created by publicly managing crisis prevention. Public Relations Review, 38(3), 408-415. https://doi.org/10.1016/j.pubrev.2012.04.004
Daley, D. J., & Kendall, D. G. (1965). Stochastic rumours. IMA Journal of Applied Mathematics, 1(1), 42-55. https://doi.org/10.1093/imamat/1.1.42
Eriksson, M. (2018). Lessons for crisis communication on social media: A systematic review of what research tells the practice. International Journal of Strategic Communication, 12(5), 526-551. https://doi.org/10.1080/1553118X.2018.1510405
Falkheimer, J., & Heide, M. (2015). Trust and brand recovery campaigns in crisis: Findus Nordic and the horsemeat scandal. International Journal of Strategic Communication, 9(2), 134-147. https://doi.org/10.1080/1553118X.2015.1008636
Frandsen, F., & Johansen, W. (2010). Apologizing in a globalizing world: Crisis communication and apologetic ethics. Corporate Communication: An International Journal, 15(4), 350-364. https://doi.org/10.1108/13563281011085475
Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215-239. https://doi.org/10.1016/0378-8733(78)90021-7
Fruchterman, T. M. J., & Reingold, E. M. (1991). Graph drawing by force‐directed placement. Journal of Software: Practice and Experience, 21(11), 1129-1164. https://doi.org/10.1002/spe.4380211102
Gaisbauer, F., Pournaki, A., Banisch, S., & Olbrich, E. (2023). Grounding force-directed network layouts with latent space models. Journal of Computational Social Science, 6(2), 707-739. https://doi.org/10.1007/s42001-023-00207-w
Held, P., Krause, B., & Kruse, R. (2016). Dynamic clustering in social networks using Louvain and infomap method. In 2016 Third European Network Intelligence Conference (ENIC), Wroclaw, Poland, 2016 (pp. 61-68). doi: 10.1109/ENIC.2016.017
Jalili, M., & Perc, M. (2017). Information cascades in complex networks. Journal of Complex Networks, 5(5), 665-693. https://doi.org/10.1093/comnet/cnx019
Koli, K. (2024). The role of social media in crisis communication. ISAR Journal of Arts, Humanities and Social Sciences, 2(12), 33-40. https://isarpublisher.com/journal/isarjahss
Muhammad, R. F., & Kasahara, S. (2024). Agent-based simulation of fake news dissemination: the role of trust assessment and big five personality traits on news spreading. Social Network Analysis and Mining, 14(75). https://doi.org/10.1007/s13278-024-01235-8
Munusamy, S., Syasyila, K., Shaari, A. A. H., Pitchan, M. A., Kamaluddin, M. R., & Jatnika, R. (2024). Psychological factors contributing to the creation and dissemination of fake news among social media users: a systematic review. BMC Psychology, 12(673). https://doi.org/10.1186/s40359-024-02129-2
Nguyễn Xuân Hồng. (2025). Góp phần minh định cách diễn đạt “Khủng hoảng truyền thông” tại Việt Nam qua khảo sát một số tài liệu về truyền thông. Tạp chí Khoa học và Công nghệ Việt Nam, 67(4). DOI: 10.31276/VJST.67(4).61-68
Noelle-Neumann, E. (1974). The spiral of silence: A theory of public opinion. Journal of Communication, 24(2), 43-51. https://doi.org/10.1111/j.1460-2466.1974.tb00367.x
Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: Bringing order to the web. Stanford Digital Library Technologies Project.
Reuter, C., Hughes, A. L., & Kaufhold, M.-A. (2018). Social media in crisis management: An evaluation and analysis of crisis informatics research. International Journal of Human-Computer Interaction, 34(4), 280-294. https://doi.org/10.1080/10447318.2018.1427832
Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). New York: Free Press.
Swastiningsih, S., Aziz, A., & Dharta, Y. (2024). The role of social media in shaping public opinion: A comparative analysis of traditional vs. digital media platforms. The Journal of Academic Science, 1(6), 620-626. https://doi.org/10.59613/fm1dpm66
Tokita, C. K., Guess, A. M., & Tarnita, C. E. (2021). Polarized information ecosystems can reorganize social networks via information cascades. Proceedings of the National Academy of Sciences, 118(50), e2102147118. https://doi.org/10.1073/pnas.2102147118
Veil, S. R., Buehner, T., & Palenchar, M. J. (2011). A work‐in‐process literature review: Incorporating social media in risk and crisis communication. Journal of Contingencies and Crisis Management, 19(2), 110-122. https://doi.org/10.1111/j.1468-5973.2011.00639.x
Venturini, T., Jacomy, M., & Jensen, P. (2021). What do we see when we look at networks: Visual network analysis, relational ambiguity, and force-directed layouts. Big Data & Society, 8(1). https://doi.org/10.1177/20539517211018488
Wang, L., Wu, H., Wang, W., & Chen, K.-C. (2015). Socially enabled wireless networks: Resource allocation via bipartite graph matching. IEEE Communications Magazine, 53(10), 128-135. https://doi.org/10.1109/MCOM.2015.7295474
Weick, K. E. (1995). Sensemaking in Organizations (Vol. 3, No. 10.1002). Thousand Oaks, CA: Sage Publications.
Zafarani, R., Abbasi, M. A., & Liu, H. (2014). Social Media Mining: An Introduction. Cambridge University Press. https://doi.org/10.1017/CBO9781139088510
Zaharna, R. S. (2007). The soft power differential: Network communication and mass communication in public diplomacy. The Hague Journal of Diplomacy, 2(3), 213-228. https://doi.org/10.1163/187119007X240505
Zhu, Z., Su, J., & Kong, L. (2015). Measuring influence in online social network based on the user-content bipartite graph. Computers in Human Behavior, 52, 184-189. https://doi.org/10.1016/j.chb.2015.04.072
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