[24] D. Krackhardt and L. W. Porter. The snowball effect: Turnover embedded in communication networks. Journal of applied psychology, 71(1):50, 1986.
[25] E. G. Larsen and Z. Fazekas. Transforming stability into change: How the media select and report opinion polls. The International Journal of Press/Politics, 25(1):115-134, 2020.
[26] J. G. Lee, S. Moon, and K. Salamatian. Modeling and predicting the popularity of online contents with cox proportional hazard regression model. Neurocomputing, 76(1):134-145, 2012.
[27] D. Leng, J. Guo, L. Pan, J. Li, and X. Wang. Enhance information propagation for graph neural network by heterogeneous aggregations. arXiv preprint arXiv:2102.04064, 2021.
[28] Z. Li, H. Wang, P. Zhang, P. Hui, J. Huang, J. Liao, J. Zhang, and J. Bu. Live-streaming fraud detection: a heterogeneous graph neural network approach. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pages 3670-3678, 2021.
[29] P. Lorenz-Spreen, B. M. Monsted, P. Hövel, and S. Lehmann. Accelerating dynamics of collective attention. Nature communications, 10(1):1759, 2019.
[30] W. Luo, H. Zhang, X. Yang, L. Bo, X. Yang, Z. Li, X. Qie, and J. Ye. Dynamic heterogeneous graph neural network for real-time event prediction. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pages 3213-3223, 2020.
[31] J. Ma, W. Gao, P. Mitra, S. Kwon, B. J. Jansen, K.-F. Wong, and M. Cha. Detecting rumors from microblogs with recurrent neural networks. 2016.
[32] M. Mahdikhani. Predicting the popularity of tweets by analyzing public opinion and emotions in different stages of COVID-19 pandemic. International Journal of Information Management Data Insights, 2(1):100053, 2022.
[33] C. Mao and W. Xiao. A comprehensive algorithm for evaluating node influences in social networks based on preference analysis and random walk. Complexity, 2018, 2018.
[34] Y. Miao et al. Practical significance of the working mechanism of public opinion guidance in universities. The Frontiers of Society, Science and Technology, 3(3), 2021.
[35] V.-H. Nguyen, K. Sugiyama, P. Nakov, and M.-Y. Kan. Fang: Leveraging social context for fake news detection using graph representation. In Proceedings of the 29th ACM international conference on information & knowledge management, pages 1165-1174, 2020.
[36] J. Pennington, R. Socher, and C. D. Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532-1543, 2014.
[37] B. Perozzi, R. Al-Rfou, and S. Skiena. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 701-710, 2014.
[38] K. Popat, S. Mukherjee, J. Strötgen, and G. Weikum. Credibility assessment of textual claims on the web. In Proceedings of the 25th ACM international on conference on information and knowledge management, pages 2173-2178, 2016.
[39] K. Popat, S. Mukherjee, J. Strötgen, and G. Weikum. Where the truth lies: Explaining the credibility of emerging claims on the web and social media. In Proceedings of the 26th International Conference on World Wide Web Companion, pages 1003–1012, 2017.
[40] Y. Qian, Y. Zhang, Y. Ye, C. Zhang, et al. Distilling meta knowledge on heterogeneous graph for illicit drug trafficker detection on social media. Advances in Neural Information Processing Systems, 34:26911-26923, 2021.
[41] J. Qiu, J. Tang, H. Ma, Y. Dong, K. Wang, and J. Tang. Deepinf: Social influence prediction with deep learning. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, pages 2110-2119, 2018.
[42] K. Rapoza. Can 'fake news' impact the stock market?, 2017.
[43] G. Salton. Introduction to modern information retrieval. McGraw-Hill, 1983.
[44] M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling. Modeling relational data with graph convolutional networks. In The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings 15, pages 593–607. Springer, 2018.
[45] A. Schulz and P. Roessler. The spiral of silence and the internet: Selection of online content and the perception of the public opinion climate in computer-mediated communication environments. International journal of public opinion research, 24(3):346-367, 2012.
[46] J. Shang, S. Huang, D. Zhang, Z. Peng, D. Liu, Y. Li, and L. Xu. Rne2vec: information diffusion popularity prediction based on repost network embedding. Computing, 103:271-289, 2021.