[4] [4] Bian T, Xiao X, Xu T, et al. Rumor detection on social media with bi-directional graph convolutional networks[C]//Proceedings of the AAAI conference on artificial intelligence. 2020, 34(1): 549-556.

[5] [5] Shelke S, Attar V. Rumor detection in social network based on user, content and lexical features[J]. Multimedia Tools and Applications, 2022, 81(12): 17347-17368.

[6] [6] Hamidian S, Diab M T. Rumor detection and classification for twitter data[J]. arXiv preprint arXiv: 1912.08926, 2019.

[7] [7] Ma J, Gao W, Wei Z, et al. Detect rumors using time series of social context information on microblogging websites[C]//Proceedings of the 24th ACM international on conference on information and knowledge management. 2015: 1751-1754.

[8] [8] Friggeri A, Adamic L, Eckles D, et al. Rumor cascades[C]//proceedings of the international AAAI conference on web and social media. 2014, 8(1): 101-110.

[9] [9] Liu Y, Wu Y F. Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks[C]//Proceedings of the AAAI conference on artificial intelligence. 2018, 32(1).

[10] [10] Xu S, Liu X, Ma K, et al. Rumor detection on social media using hierarchically aggregated feature via graph neural networks[J]. Applied Intelligence, 2023, 53(3): 3136-3149.

[11] [11] Ma J, Gao W, Wong K F. Detect rumors in microblog posts using propagation structure via kernel learning[C]. Association for Computational Linguistics, 2017.

[12] [12] Vosoughi S, Roy D, Aral S. The spread of true and false news online[J]. science, 2018, 359(6380): 1146-1151.

[13] [13] Bai L, Han X, Jia C. A Rumor Detection Model Incorporating Propagation Path Contextual Semantics and User Information[J]. Neural Processing Letters, 2023: 1-20.

[14] [14] Chen Z, Zhuang F, Liao L, et al. A syntactic multi-level interaction network for rumor detection[J]. Neural Computing and Applications, 2023: 1-14.

[15] [15] Bian T, Xiao X, Xu T, et al. Rumor detection on social media with bi-directional graph convolutional networks[C]//Proceedings of the AAAI conference on artificial intelligence. 2020, 34(1): 549-556.

[16] [16] Jain D K, Kumar A, Shrivastava A. CanarDeep: a hybrid deep neural model with mixed fusion for rumour detection in social data streams[J]. Neural Computing and Applications, 2022: 1-12.

[17] [17] Chen T, Li X, Yin H, et al. Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection[C]. Springer International Publishing, 2018: 40-52.

[18] [18] He sicheng, Wang Yuxin, Sun Qingxuan. M3E: Moka Massive Mixed Embedding Model. 2023.

[19] [19] Ma J, Gao W, Mitra P, et al. Detecting rumors from microblogs with recurrent neural networks[J]. 2016.

[20] [20] Liu Z, Zhang L, Tu C, et al. Statistical and semantic analysis of rumors in chinese social media[J]. Scientia Sinica Informationis, 2015, 45(12): 1536.

[21] [21] Castillo C, Mendoza M, Poblete B. Information credibility on twitter[C]//Proceedings of the 20th international conference on World wide web. 2011: 675-684.

[22] [22] Cho K, Van Merrinboer B, Gulcehre C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[J]. arXiv preprint arXiv: 1406.1078, 2014.

[23] [23] Hamilton W, Ying Z, Leskovec J. Inductive representation learning on large graphs[J]. Advances in neural information processing systems, 2017, 30.

[24] [24] Velikovi P, Cucurull G, Casanova A, et al. Graph attention networks[J]. arXiv preprint arXiv: 1710.10903, 2017.

[25] [25] MA J, GAO W, WONG K, et al. Rumor detection on twitter with tree-structured recursive neural networks[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Melbourne, Australia, 2018: 1980-1989.

[26] [26] Brody S, Alon U, Yahav E. How attentive are graph attention networks[J]. arXiv preprint arXiv: 2105.14491, 2021.

[27] [27] Rossi E, Charpentier B, Di Giovanni F, et al. Edge directionality improves learning on heterophilic graphs[J]. arXiv preprint arXiv: 2305.10498, 2023.