Open Access
Issue
SHS Web Conf.
Volume 153, 2023
The Fifth International Conference on Social Science, Public Health and Education (SSPHE2022)
Article Number 01009
Number of page(s) 11
DOI https://doi.org/10.1051/shsconf/202315301009
Published online 10 January 2023
  1. Jia, P., MirTabatabaei, A., Friedkin, N. E., & Bullo, F, Opinion dynamics and the evolution of social power in influence networks. SIAM review, 57(3), 367-397 (2015) [Google Scholar]
  2. Peng, S., Yang, A., Cao, L., Yu, S., & Xie, D, Social influence modeling using information theory in mobile social networks. Information Sciences, 379, 146-159 (2017) [CrossRef] [Google Scholar]
  3. Li, K., Zhang, L., & Huang, H. Social Influence Analysis: Models, Methods, and Evaluation. Engineering, 4(1), 40-46 (2018). doi:10.1016/j.eng.2018.02.004 [CrossRef] [Google Scholar]
  4. Peng, S., Wang, G., & Xie, D., Social Influence Analysis in Social Networking Big Data: Opportunities and Challenges. Ieee Network, 31(1), 11-17 (2017). doi:10.1109/mnet.2016.1500104nm [CrossRef] [Google Scholar]
  5. Loyola-Gonzalez, O., Lopez-Cuevas, A., Medina-Perez, M. A., Camina, B., RamirezMarquez, J. E., & Monroy, R, Fusing pattern discovery and visual analytics approaches in tweet propagation. Information Fusion, 46, 91-101 (2019) [CrossRef] [Google Scholar]
  6. Cialdini, R. B., & Goldstein, N. J., Social influence: Compliance and conformity. Annu. Rev. Psychol., 55, 591-621 (2004) [Google Scholar]
  7. Peng, S., Zhou, Y., Cao, L., Yu, S., Niu, J., & Jia, W., Influence analysis in social networks: A survey. Journal of Network and Computer Applications, 106, 17-32 (2017). doi:10.1016/j.jnca.2018.01.005 [Google Scholar]
  8. Kempe, D., Kleinberg, J., & Tardos, E., Maximizing the spread of influence through a social network. Paper presented at the Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining. (2003) [Google Scholar]
  9. Huberman, B. A., Romero, D. M., & Wu, F. J. A. P. A., Social networks that matter: Twitter under the microscope. (2008) [Google Scholar]
  10. Fung, H. H., Carstensen, L. L., Lutz, A. M. J. P., & aging., Influence of time on social preferences: Implications for life-span development. 14(4), 595 (1999) [Google Scholar]
  11. Bakshy, E., Eckles, D., Yan, R., & Rosenn, I., Social influence in social advertising: evidence from field experiments. Paper presented at the Proceedings of the 13 th ACM conference on electronic commerce. (2012) [Google Scholar]
  12. Bae, Y., & Lee, H., Sentiment analysis of twitter audiences: Measuring the positive or negative influence of popular twitterers. Journal of the American Society for Information Science and Technology, 63(12), 2521-2535 (2012). doi: 10.1002/asi.22768 [Google Scholar]
  13. Newman, M. E. J., & Girvan, M., Finding and evaluating community structure in networks. Physical Review E, 69(2) (2004). doi:10.1103/PhysRevE.69.026113 [Google Scholar]
  14. Wu, F., & Huberman, B. A., Finding communities in linear time: a physics approach. European Physical Journal B, 38(2), 331-338 (2004). doi:10.1140/epjb/e2004-00125-x [CrossRef] [Google Scholar]
  15. Rogers, E. M., & Cartano, D. G. J. P. O. Q., Methods of measuring opinion leadership. 435-441 (1962) [Google Scholar]
  16. Watts, D. J., & Strogatz, S. H. J. N., Collective dynamics of ‘small-world'networks. 393(6684), 440 (1998) [Google Scholar]
  17. Newman, M. E. J. S. R., The structure and function of complex networks. 45(2), 167256 (2003) [Google Scholar]
  18. Rusinowska, A., Berghammer, R., De Swart, H., & Grabisch, M., Social networks: prestige, centrality, and influence. Paper presented at the International Conference on Relational and Algebraic Methods in Computer Science. (2011) [Google Scholar]
  19. Wu, J. & Yu W., Research on the influence of government Weibo based on social network analysis. Information Science, 39(02), 78-85 (2021). doi:10.13833/j.issn.10077634.2021.02.010 [Google Scholar]
  20. Wasserman, S., & Faust, K., Social network analysis: Methods and applications (Vol. 8): Cambridge university press. (1994) [Google Scholar]
  21. Quercia, D., Capra, L., & Crowcroft, J., The social world of twitter: Topics, geography, and emotions. Paper presented at the Sixth International AAAI Conference on Weblogs and Social Media. (2012) [Google Scholar]
  22. Carrington, P. J., Scott, J., & Wasserman, S., Models and methods in social network analysis (Vol. 28): Cambridge university press. (2005) [Google Scholar]
  23. Page, L., Brin, S., Motwani, R., & Winograd, T., The PageRank citation ranking: Bringing order to the web. Retrieved from (1999) [Google Scholar]
  24. Riquelme, F., & Gonzalez-Cantergiani, P., Measuring user influence on Twitter: A survey. Information Processing & Management, 52(5), 949-975 (2016) [CrossRef] [Google Scholar]
  25. Tunkelang, D. J. T. N. C., A twitter analog to pagerank. 44 (2009) [Google Scholar]
  26. Borgs, C., Brautbar, M., Chayes, J., & Lucier, B., Maximizing social influence in nearly optimal time. Paper presented at the Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete algorithms. (2014) [Google Scholar]
  27. Lu, L., Zhang, Y.-C., Yeung, C. H., & Zhou, T., Leaders in social networks, the delicious case. 6(6), e21202 (2011) [Google Scholar]
  28. Luo, F., Xu, Y., Pu, Q. & Qiu, Q., Multidimensional Weibo user influence measurement based on PageRank. Computer Application Research, 37(05), 13541358+1367. (2020). doi: 10.19734/j.issn.1001-3695.2018.10.0798 [Google Scholar]
  29. Kleinberg, J. M. J. J. O. T. A., Authoritative sources in a hyperlinked environment. 46(5), 604632 (1999) [Google Scholar]
  30. Li, Q., Zhou, T., Lu, L., Chen, D. J. P. A. S. M., & Applications, i., Identifying influential spreaders by weightedLeaderRank. 404, 47-55 (2014) [Google Scholar]
  31. Cheng, A., Evans, M., & Singh, H. J. R. O. S., June, Toronto, Canada. Inside Twitter: An indepth look inside the Twitter world. (2009) [Google Scholar]
  32. Weng, J., Lim, E.-P., Jiang, J., & He, Q., Twitterrank: finding topic-sensitive influential twitterers. Paper presented at the Proceedings of the third ACM international conference on Web search and data mining. (2010) [Google Scholar]
  33. Ren, X., & Linyuan, L. J. C. S. B., Review of ranking nodes in complex networks. 59(13), 1175-1197 (2014) [Google Scholar]
  34. Barbieri, N., Bonchi, F., Manco, G. J. K., & systems, i., Topic-aware social influence propagation models. 37(3), 555-584 (2013) [Google Scholar]
  35. Bonchi, F., Castillo, C., & Ienco, D. J. J. O. I. I. S., Meme ranking to maximize posts virality in Weibogingplatforms. 40(2), 211-239 (2013) [Google Scholar]
  36. Guo, W., Wu, S., Wang, L., & Tan, T., Social-relational topic model for social networks. Paper presented at the Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. (2015) [Google Scholar]
  37. Zhang, L., & Chen, L., Research on the interactive communication model of Weibo public opinion topics with multi subject intervention. Information Science. (2022) [Google Scholar]
  38. Jun-jun, C. J. B. J. U., Research on information dissemination and topics growth trends prediction in social networks. (2013) [Google Scholar]
  39. Fowler, J. H., & Christakis, N. A. J. B., Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study. 337, a2338 (2008) [Google Scholar]
  40. Zhang, C., Zhang, C. & Le, P., Research on the diffusion characteristics of popular Weibo topics. Surveying and Mapping Geographic Information, 43(02), 115-118 (2018). doi: 10.14188/j.2095-6045.2016192. [Google Scholar]
  41. Chen, C. C., Chen, Y. T., Sun, Y. L., & Chen, M. C., Life cycle modeling of news events using aging theory. In N. Lavrac, D. Gamberger, H. Blockeel, & L. Todorovski (Eds.), Machine Learning: Ecml 2003, 2837, 47-59 (2003) [Google Scholar]
  42. Wang, C., Zhang, M., Ru, L., & Ma, S., Automatic online news topic ranking using media focus and user attention based on aging theory. Paper presented at the Proceedings of the 17th ACM conference on Information and knowledge management. (2008) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.