[1] YANG J, PAN Y. COVID-19 rumor detection on social networks based on content information and user response[J]. Frontiers in Physics, 2021: 570.
[2] 刘政, 卫志华, 张韧弦. 基于卷积神经网络的谣言检测[J]. 计算机应用, 2017, 37(11): 3053-3056.
LIU Z, WEI Z H, ZHANG R X. Rumor detection based on convolutional neural network[J]. Journal of Computer Applications, 2017, 37(11): 3053-3056.
[3] 周丽娜, 谭励, 曹娟, 等. 基于卷积神经网络的食品安全领域谣言检测方法[J]. 计算机应用与软件, 2022, 39(3): 45-50.
ZHOU L N, TAN L, CAO J, et al. Rumor detection method in food safety field based on convolutional neural network[J]. Computer Applications and Software, 2022, 39(3): 45-50.
[4] GHANEM B, PONZETTO S P, ROSSO P, et al. Fakeflow: fake news detection by modeling the flow of affective information[C]//Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics, 2021: 679-689.
[5] ALKHODAIR S A, DING S H H, FUNG B C M, et al. Detecting breaking news rumors of emerging topics in social media[J]. Information Processing & Management, 2020, 57(2): 102018.
[6] MONTI F, FRASCA F, EYNARD D, et al. Fake news detection on social media using geometric deep learning[J]. arXiv:1902.06673, 2019.
[7] 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: 549-556.
[8] CUI W, SHANG M. KAGN: knowledge-powered attention and graph convolutional networks for social media rumor detection[J] Journal of Big Data, 2023: 45.
[9] WANG S, KONG Q, WANG Y, et al. Enhancing rumor detection in social media using dynamic propagation structures[C]//Proceedings of the 2019 IEEE International Conference on Intelligence and Security Informatics (ISI), 2019: 41-46.
[10] HUANG Q , ZHOU C, WU J, et al. Deep spatial-temporal structure learning for rumor detection on twitter[J]. Neural Computing and Applications, 2023, 35(18): 12995-13005.
[11] MA J, GAO W, WONG K F. Detect rumors in microblog posts using propagation structure via kernel learning[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, 2017: 708-717.
[12] KWON S, CHA M, JUNG K, et al. Prominent features of rumor propagation in online social media[C]//Proceedings of 2013 IEEE 13th International Conference on Data Mining, 2013: 1103-1108.
[13] BHATTACHARJEE U, SRIJITH P K, DESARKAR M S. Term specific tf-idf boosting for detection of rumours in social networks[C]//Proceedings of the 2019 11th International Conference on Communication Systems & Networks (COMSNETS), 2019: 726-731.
[14] MA J, GAO W, MITRA P, et al. Detecting rumors from microblogs with recurrent neural networks[C]//Proceedings of the International Joint Conference on Artificial Intelligence, 2016.
[15] WU L, LIU H. Tracing fake-news footprints: characterizing social media messages by how they propagate[C]//Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 2018: 637-645.
[16] CHEN T, LI X, YIN H, et al. Call attention to rumors: deep attention based recurrent neural networks for early rumor detection[C]//Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2018 Workshops, BDASC, BDM, ML4Cyber, PAISI, DaMEMO, Melbourne, VIC, Australia, June 3, 2018. [S.l.]: Springer International Publishing, 2018: 40-52.
[17] WU K, YANG S, ZHU K Q. False rumors detection on sina weibo by propagation structures[C]//Proceedings of 2015 IEEE 31st International Conference on Data Engineering, 2015: 651-662.
[18] WANG Z, ZHANG W, TAN C W. On inferring rumor source for SIS model under multiple observations[C]//Proceedings of the 2015 IEEE International Conference on Digital Signal Processing (DSP), 2015: 755-759.
[19] ZHOU Y, WU C, ZHU Q, et al. Rumor source detection in networks based on the SEIR model[J]. IEEE Access, 2019, 7: 45240-45258.
[20] 薛海涛, 王莉, 杨延杰, 等. 基于用户传播网络与消息内容融合的谣言检测模型[J]. 计算机应用, 2021, 41(12): 3540-3545.
XUE H T, WANG L, YANG Y J, et al. Rumor detection model based on user propagation network and message content[J]. Journal of Computer Applications, 2021, 41(12): 3540-3545.
[21] MA J, GAO W, WONG K F. Rumor detection on twitter with tree-structured recursive neural networks[C]//Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2018.
[22] SONG C, SHU K, WU B. Temporally evolving graph neural network for fake news detection[J]. Information Processing and Management, 2021, 58(6): 102712.
[23] WEI S, WU B, XIANG A, et al. DGTR: dynamic graph transformer for rumor detection[J]. Frontiers in Research Metrics and Analytics, 2023: 1055348.
[24] CHOI J, KO T, CHOI Y, et al. Dynamic graph convolutional networks with attention mechanism for rumor detection on social media[J]. PloS One, 2021, 16(8): e0256039.
[25] WOO S, PARK J, LEE J Y, et al. Cbam: convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018: 3-19.
[26] ZUBIAGA A, LIAKATA M, PROCTER R. Exploiting context for rumour detection in social media[C]//Proceedings of 9th International Conference on Social Informatics: (SocInfo 2017), Oxford, UK, September 13-15, 2017. [S.l.]: Springer International Publishing, 2017: 109-123.
[27] FAGBOHUNGBE O, QIAN L. Impact of learning rate on noise resistant property of deep learning models[J]. arXiv:2205.07856, 2022.
[28] BROCK A, DONAHUE J, SIMONYAN K. Large scale gan training for high fidelity natural image synthesis[C//Proceedings of the International Conference on Learning Representations, 2018.
[29] CAI G, WANG Y, HE L. Learning smooth representation for unsupervised domain adaptation[J]. arXiv:1905.10748, 2019.
[30] ZHU Y, LYU F, HU C, et al. Encoder-decoder architecture for supervised dynamic graph learning: a survey[J]. arXiv:2203.10480, 2022.
[31] LI M, LU S, ZHANG L, et al. A community detection method for social network based on community embedding[J]. IEEE Transactions on Computational Social Systems, 2021, 8(2): 308-318.
[32] WANG X, CUI P, WANG J, et al. Community preserving network embedding[C]//Proceedings of the AAAI Conference on Artificial Intelligence, 2022. |