{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:27:55Z","timestamp":1772252875114,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T00:00:00Z","timestamp":1609718400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["No. 2018YFC0831703"],"award-info":[{"award-number":["No. 2018YFC0831703"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Identifying fake news on media has been an important issue. This is especially true considering the wide spread of rumors on popular social networks such as Twitter. Various kinds of techniques have been proposed for automatic rumor detection. In this work, we study the application of graph neural networks for rumor classification at a lower level, instead of applying existing neural network architectures to detect rumors. The responses to true rumors and false rumors display distinct characteristics. This suggests that it is essential to capture such interactions in an effective manner for a deep learning network to achieve better rumor detection performance. To this end we present a simplified aggregation graph neural network architecture. Experiments on publicly available Twitter datasets demonstrate that the proposed network has performance on a par with or even better than that of state-of-the-art graph convolutional networks, while significantly reducing the computational complexity.<\/jats:p>","DOI":"10.3390\/make3010005","type":"journal-article","created":{"date-parts":[[2021,1,4]],"date-time":"2021-01-04T08:35:19Z","timestamp":1609749319000},"page":"84-94","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks"],"prefix":"10.3390","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9091-3262","authenticated-orcid":false,"given":"Liang","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingqun","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen LiCi Electronic Company, Shenzhen 518000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Jia","sequence":"additional","affiliation":[{"name":"College of Computer, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/S0271-5309(97)00024-4","article-title":"Rumor research revisited and expanded","volume":"18","author":"Pendleton","year":"1998","journal-title":"Lang. Commun."},{"key":"ref_2","unstructured":"Castillo, C., Mendoza, M., and Poblete, B. (April, January 28). Information credibility on twitter. Proceedings of the 20th International Conference on World Wide Web, Hyderabad, India."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ma, J., Gao, W., Wei, Z., Lu, Y., and Wong, K.F. (2015, January 19\u201323). Detect rumors using time series of social context information on microblogging websites. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, Melbourne Australia.","DOI":"10.1145\/2806416.2806607"},{"key":"ref_4","unstructured":"Ma, J., Gao, W., and Wong, K.F. (August, January 30). Detect rumors in microblog posts using propagation structure via kernel learning. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, Vancouver, BC, Canada."},{"key":"ref_5","unstructured":"Ma, J., Gao, W., Mitra, P., Kwon, S., Jansen, B.J., Wong, K.F., and Cha, M. (2016, January 9\u201315). Detecting rumors from microblogs with recurrent neural networks. Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Qi, H., Chuan, Z., Wu, J., Wang, M., and Wang, B. (2019, January 14\u201319). Deep Structure Learning for Rumor Detection on Twitter. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.","DOI":"10.1109\/IJCNN.2019.8852468"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bian, T., Xiao, X., Xu, T., Zhao, P., Huang, W., Rong, Y., and Huang, J. (2020, January 7\u201312). Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks. 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Detect rumors on twitter by promoting information campaigns with generative adversarial learning. Proceedings of the World Wide Web Conference, San Francisco, CA, USA.","DOI":"10.1145\/3308558.3313741"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yu, F., Liu, Q., Wu, S., Wang, L., and Tan, T. (2017, January 19\u201325). A convolutional approach for misinformation identification. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/545"},{"key":"ref_12","unstructured":"Ruchansky, N., Seo, S., and Liu, Y. (2017, January 6\u201310). Csi: A hybrid deep model for fake news detection. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, Singapore."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, Y., and Wu, Y.F.B. (2018, January 2\u20137). 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