{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T19:47:35Z","timestamp":1767901655572,"version":"3.49.0"},"reference-count":22,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T00:00:00Z","timestamp":1660608000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"China Government under NSFC-Xinjiang Joint Fund","award":["U1703261"],"award-info":[{"award-number":["U1703261"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Information"],"abstract":"<jats:p>Many rumors spread quickly and widely on social media, affecting social stability. The rumors of most current detection methods only use textual information or introduce external auxiliary information (such as user information and propagation information) to enhance the detection effect, and the inherent statistical features of the corpus have not been fully used and compared with the external auxiliary features; in addition, statistical features are more certain and can only be obtained from textual information. Therefore, we adopted a method based on the adaptive fusion of word frequency distribution features and textual features to detect rumors. Statistical features were extracted by encoding statistical information through a variational autoencoder. We extracted semantic features and sequence features as textual features through a parallel network comprising a convolutional neural network and a bidirectional long-term memory network. In addition, we also designed an adaptive valve to only fuse useful statistical features with textual features according to the credibility of textual features, which can solve the over-fitting problem caused by the excessive use of statistical features. The accuracy of the model in three public datasets (Twitter15, Twitter16, and Weibo) reached 87.5%, 88.6%, and 95.8%, respectively, and the F1 value reached 87.4%, 88.5%, and 95.8%, respectively, showing that the model can effectively improve the performance of rumor detection.<\/jats:p>","DOI":"10.3390\/info13080388","type":"journal-article","created":{"date-parts":[[2022,8,16]],"date-time":"2022-08-16T03:40:32Z","timestamp":1660621232000},"page":"388","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A Rumor Detection Method Based on Adaptive Fusion of Statistical Features and Textual Features"],"prefix":"10.3390","volume":"13","author":[{"given":"Ziyan","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China"},{"name":"Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiping","family":"Dan","sequence":"additional","affiliation":[{"name":"College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China"},{"name":"Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fangmin","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China"},{"name":"Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhun","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China"},{"name":"Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanke","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer and Information Technology, China Three Gorges University, Yichang 443002, China"},{"name":"Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering, China Three Gorges University, Yichang 443002, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,16]]},"reference":[{"key":"ref_1","first-page":"1421","article-title":"Social Network Rumor Detection: A Survey","volume":"48","author":"Gao","year":"2020","journal-title":"Acta Electonica Sin."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ma, J., Gao, W., and Wong, K.F. (2019). Detect rumors on twitter by promoting information campaigns with generative adversarial learning. WWW \u203219: The World Wide Web Conference, ACM.","DOI":"10.1145\/3308558.3313741"},{"key":"ref_3","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_4","doi-asserted-by":"crossref","unstructured":"Yang, F., Liu, Y., Yu, X., and Yang, M. (2012, January 12\u201316). Automatic detection of rumor on sina weibo. Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, Beijing, China.","DOI":"10.1145\/2350190.2350203"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kwon, S., Cha, M., Jung, K., Chen, W., and Wang, Y. (2013, January 7\u201310). Prominent features of rumor propagation in online social media. Proceedings of the 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA.","DOI":"10.1109\/ICDM.2013.61"},{"key":"ref_6","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 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), New York, NY, USA."},{"key":"ref_7","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 (IJCAI-17), Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/545"},{"key":"ref_8","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_9","unstructured":"Li, X., Li, Z., Xie, H., and Li, Q. (2021, January 2\u20139). Merging statistical feature via adaptive gate for improved text classification. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual Event. Available online: https:\/\/aaai.org\/Conferences\/AAAI-21\/."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chen, T., Li, X., Yin, H., and Zhang, J. (2018). Call attention to rumors: Deep attention based recurrent neural networks for early rumor detection. Trends and Applications in Knowledge Discovery and Data Mining\u2014PAKDD 2018, Springer.","DOI":"10.1007\/978-3-030-04503-6_4"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ajao, O., Bhowmik, D., and Zargari, S. (2018, January 18\u201320). Fake news identification on twitter with hybrid cnn and rnn models. Proceedings of the 9th International Conference on Social Media and Society, Copenhagen, Denmark.","DOI":"10.1145\/3217804.3217917"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nguyen, T.N., Li, C., and Niederee, C. (2017). On early-stage debunking Rumors On Twitter: Leveraging the wisdom of weak learners. Social Informatics\u2014SocInfo 2017, Springer.","DOI":"10.1007\/978-3-319-67256-4_13"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ma, J., Gao, W., and Wong, K.F. (2018, January 15\u201320). Rumor Detection on Twitter with Tree-Structured Recursive Neural Networks. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (ACL 2018), Melbourne, Australia.","DOI":"10.18653\/v1\/P18-1184"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Jin, Z., Cao, J., Guo, H., Zhang, Y., and Luo, J. (2017, January 23\u201327). Multimodal fusion with recurrent neural networks for rumor detection on microblogs. Proceedings of the 25th ACM International Conference on Multimedia, Mountain View, CA, USA.","DOI":"10.1145\/3123266.3123454"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Khattar, D., Goud, J.S., Gupta, M., and Varma, V. (2019). Mvae: Multimodal variational autoencoder for fake news detection. WWW \u203219: The World Wide Web Conference, ACM.","DOI":"10.1145\/3308558.3313552"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"116517","DOI":"10.1016\/j.eswa.2022.116517","article-title":"MDMN: Multi-task and Domain Adaptation based Multi-modal Network for early rumor detection","volume":"195","author":"Zhou","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_17","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_18","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Resnick, P., and Mei, Q. (2015, January 18\u201322). Enquiring minds: Early detection of rumors in social media from enquiry posts. Proceedings of the 24th International Conference on World Wide Web, Florence, Italy.","DOI":"10.1145\/2736277.2741637"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, Y., and Wu, Y.F. (2018, January 2\u20137). Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. Proceedings of the AAAI Conference on Artificial Intelligence, New Orleans, LA, USA.","DOI":"10.1609\/aaai.v32i1.11268"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ma, J., and Gao, W. (2020, January 8\u201313). Debunking Rumors on Twitter with Tree Transformer. Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain.","DOI":"10.18653\/v1\/2020.coling-main.476"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bing, C., Wu, Y., Dong, F., Xu, S., Liu, X., and Sun, S. (2022). Dual Co-Attention-Based Multi-Feature Fusion Method for Rumor Detection. 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