A Study on Information Diffusion Mechanisms and Influence Enhancement Strategies for Textile Technology Achievements in Social Network Communication
Main Article Content
Abstract
Addressing issues such as slow diffusion speed, limited coverage, low user engagement, and poor conversion efficiency in the dissemination of textile technology achievements via social networks, this paper conducts an in-depth study on their information diffusion mechanisms and strategies for enhancing influence. The relevant achievements include not only conventional textile technologies but also functional textile innovations related to electromagnetic shielding materials, wearable antenna textiles, smart fabrics, and applied electromagnetic scenarios. First, drawing upon information diffusion theory and social network analysis methods, the structural characteristics of key disseminators, including core disseminators such as research institutions and enterprises, key intermediaries such as industry experts and opinion leaders, and ordinary users, are analyzed. This forms the basis for constructing a three-tier diffusion model of “source-intermediary-recipient”. Second, by scraping dissemination data from Weibo, WeChat, and Douyin platforms, the study explores the spatiotemporal evolution patterns of information diffusion and identifies key influencing factors across four dimensions: content characteristics, actor attributes, network structure, and platform environment. Third, a system dynamics model for information diffusion is established to simulate diffusion processes across different scenarios, revealing the underlying mechanisms of factor interactions. Finally, targeted influence enhancement strategies are proposed across four dimensions: content optimization, stakeholder collaboration, network restructuring, and platform adaptation.
Downloads
Article Details

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
B. Bhandari, J. Tanaka ZH, P. Mahapatra, et al., “Misinformation on cardiovascular disease spreads through social networks: a scoping review protocol,” BMJ Open, vol. 15, no. 7, p. e094167, 2025, doi: 10.1136/bmjopen-2024-094167.
A. Xia, Y. Y. Teoh, R. M. Nassar, et al., “Knowledge of information cascades through social networks facilitates strategic gossip,” Nature Human Behaviour, vol. 9, no. 10, pp. 1-14, 2025, doi: 10.1038/s41562-025-02241-2.
Y. Gao and L. Zhang, “Interactive recommendation of social network communication between cities based on GNN and user preferences,” Nonlinear Engineering, vol. 14, no. 1, pp. 20240068-20240068, 2025, doi: 10.1515/nleng-2024-0068.
K. R. J. Kamdjoug, “The influence of social network communication on the buying behavior of Cameroonian consumers on social e-commerce platforms,” Journal of Enterprise Information Management, vol. 36, no. 5, pp. 1319-1348, 2023, doi: 10.1108/jeim-09-2022-0329.
D. Linying and Y. Lixia, “COVID-19 Anxiety: The Impact of Older Adults’ Transmission of Negative Information and Online Social Networks,” Aging and health research, vol. 3, no. 1, pp. 100119-100119, 2023, doi: 10.1016/j.ahr.2023.100119.
B. N. M, C. E. M, and C. M. M, “PET/CT in nuclear endocrinology: Results of the survey diffused through different social networks of the SEM-NIM in Spain,” Revista espanola de medicina nuclear e imagen molecular, vol. 42, no. 2, pp. 77-82, 2022, doi: 10.1016/j.remnie.2022.11.003.
I. Radoslav and N. Pavel, “On disinformation as a hybrid threat spread through social networks,” Entrepreneurship and Sustainability Issues, vol. 10, no. 1, pp. 344-357, 2022, doi: 10.9770/jesi.2022.10.1(18).
N. Hamzelou, M. Ashtiani, and R. Sadeghi, “A propagation trust model in social networks based on the A* algorithm and multi-criteria decision making,” Computing, vol. 103, no. 5, pp. 1-41, 2021, doi: 10.1007/s00607-021-00918-w.
A. Swain, S. Satpathy, S. Dutta, et al., “A statistical analysis for COVID-19 as a contract tracing approach and social network communication management,” International Journal of Computer Applications in Technology, vol. 66, no. 3-4, pp. 279-285, 2021, doi: 10.1504/ijcat.2021.120457.
W. Lea, “Social networking: Crisis communication,” Nature, vol. 457, no. 7228, pp. 376-8, 2009, doi: 10.1038/457376a.
A, “A P, A,” L V, A. A C, et al. Methodology for disseminating information channels analysis in social networks (vol 14, pg 362, pp. ). VESTNIK SANKT-PETERBURGSKOGO UNIVERSITETA SERIYA 10 PRIKLADNAYA MATEMATIKA INFORMATIKA PROTSESSY UPRAVLENIYA. 2020; 16(2):214-215, 2018.
S. Andrey, K. Tatyana, C. Pavel, et al., “Organization changes of the university’s corporate culture under the influence of the social Internet communications,” SHS Web of Conferences, pp. 2801049-01049, 2016, doi: 10.1051/shsconf/20162801049.
J. Joy, E. Chung, Z. Yuan, et al., “DiscoverFriends: secure social network communication in mobile ad hoc networks,” Wireless Communications and Mobile Computing, vol. 16, no. 11, pp. 1401-1413, 2016, doi: 10.1002/wcm.2708.
S. Molchanov, O. Almazova, A. Voiskounsky, et al., “Role of personality features of adolescents in processing information via social network communication,” National Psychological Journal, no. 4, pp. 3-15, 2018, doi: 10.11621/npj.2018.0401.
E. Susanne and E. Debora, “PP066 Disseminate Results Through Social Video And Social Networks,” International Journal of Technology Assessment in Health Care, vol. 33, no. S1, pp. 101-102, 2017, doi: 10.1017/s0266462317002422.
W. Yoo, D. Choi, and K. Park, “The effects of SNS communication: How expressing and receiving information predict MERSpreventive behavioral intentions in South Korea,” Computers in Human Behavior, pp. 6234-43, 2016, doi: 10.1016/j.chb.2016.03.058.
J. Sivasubramaniyam and C. Chandrasekar, “A Biometric-Secured Neighborhood Vector Relational Coefficient Framework for Social Network Communication,” International Journal of Cooperative Information Systems, vol. 27, no. 2, p. 15, 2018, doi: 10.1142/s0218843018500041.
J, “J A M, Armand J, A,” M H, et al. Parasite Transmission through Social Networks of Japanese Macaques: A Cost of Grooming? =Supplement, pp. 10-10, 2011, doi: 10.14907/primate.27.0.10.0.
D. Duvanova, A. Nikolaev, A. Nikolsko-Rzhevskyy, et al., “Violent conflict and online segregation: An analysis of social network communication across Ukraine’s regions,” Journal of Comparative Economics, vol. 44, no. 1, pp. 163-181, 2016, doi: 10.1016/j.jce.2015.10.003.
Y. Ophir, H. Rosenberg, S. C. Asterhan, et al., “In times of war, adolescents do not fall silent: Teacher–student social network communication in wartime,” Journal of Adolescence, pp. 4698-106, 2016, doi: 10.1016/j.adolescence.2015.11.005.
S. Lamrhari, H. Elghazi, S. Tigani, et al., “Enhancing Social Network Communication through Dynamic Clustering Balance,” 2018, doi: 10.1145/3177148.3180090.
D. Duvanova, A. Semenov, and A. Nikolaev, “Do social networks bridge political divides? The analysis of VKontakte social network communication in Ukraine,” Post-Soviet Affairs, vol. 31, no. 3, pp. 224-249, 2015, doi: 10.1080/1060586x.2014.918453.
D. Scanfeld, V. Scanfeld, and L. E. Larson, “Dissemination of health information through social networks: Twitter and antibiotics,” AJIC: American Journal of Infection Control, vol. 38, no. 3, pp. 182-188, 2009, doi: 10.1016/j.ajic.2009.11.004.
M. Thelwall, D. Wilkinson, and S. Uppal, “Data mining emotion in social network communication: Gender differences in MySpace,” Journal of the American Society for Information Science and Technology, vol. 61, no. 1, pp. 190-199, 2010, doi: 10.1002/asi.21180.
Y. G. Mo and B. Wellman, “Understanding Sequencing in Social Network Communications,” Bulletin de Méthodologie Sociologique, vol. 113, no. 1, pp. 76-87, 2012, doi: 10.1177/0759106311426997.
Y. Hu, S. Havlin, and A. H. Makse, “Conditions for Viral Influence Spreading through Multiplex Correlated Social Networks,” Physical Review X, vol. 4, no. 2, p. 021031, 2014, doi: 10.1103/physrevx.4.021031.
C. V. F. Ficarra, “Handbook of Research on Interactive Information Quality in Expanding Social Network Communications,” IGI Global, 2014, doi: 10.4018/978-1-4666-7377-9.
Förster and Rohn, “Is Small the New Big? Size Effects on TV Stations’ Social Network Communication,” Journal of Media Business Studies, vol. 10, no. 4, pp. 21-39, 2013, doi: 10.1080/16522354.2013.11073570.
O. Paul, “Social networks can spread the Olympic effect,” Nature, vol. 489, no. 7416, p. 337, 2012, doi: 10.1038/489337a.
A. Almaraz, Isidoro, B. Pazos, et al., “Social Network Communication: Perceptions and Uses for Spanish NGOs,” Cuadernos.info, vol. 32, no. 32, pp. 77-88, 2013, doi: 10.7764/cdi.32.497.