{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T15:22:49Z","timestamp":1774797769697,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,11,15]],"date-time":"2019-11-15T00:00:00Z","timestamp":1573776000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1703261"],"award-info":[{"award-number":["U1703261"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Social media makes it easy for individuals to publish and consume news, but it also facilitates the spread of rumors. This paper proposes a novel deep recurrent neural model with a symmetrical network architecture for automatic rumor detection in social media such as Sina Weibo, which shows better performance than the existing methods. In the data preparing phase, we filter the posts according to the followers of the user. We then use sequential encoding for the posts and multiple embedding layers to get better feature representation, and multiple recurrent neural network layers to capture the dynamic temporal signals characteristic. The experimental results on the Sina Weibo dataset show that: 1. the sequential encoding performs better than the term frequency-inverse document frequency (TF-IDF) or the doc2vec encoding scheme; 2. the model is more accurate when trained on the posts from the users with more followers; and 3. the model achieves superior improvements over the existing works on the accuracy of detection, including the early detection.<\/jats:p>","DOI":"10.3390\/sym11111408","type":"journal-article","created":{"date-parts":[[2019,11,15]],"date-time":"2019-11-15T11:25:56Z","timestamp":1573817156000},"page":"1408","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Deep Recurrent Neural Network and Data Filtering for Rumor Detection on Sina Weibo"],"prefix":"10.3390","volume":"11","author":[{"given":"Yichun","family":"Xu","sequence":"first","affiliation":[{"name":"College of Computer and Information, China Three Gorges University, Yichang 443002, China"}]},{"given":"Chen","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer and Information, China Three Gorges University, Yichang 443002, China"}]},{"given":"Zhiping","family":"Dan","sequence":"additional","affiliation":[{"name":"College of Computer and Information, China Three Gorges University, Yichang 443002, China"}]},{"given":"Shuifa","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Computer and Information, China Three Gorges University, Yichang 443002, China"},{"name":"Department of Electrical Engineering and Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI 53201, USA"}]},{"given":"Fangmin","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Computer and Information, China Three Gorges University, Yichang 443002, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,15]]},"reference":[{"key":"ref_1","unstructured":"(2019, October 31). Pew Research Center. Available online: http:\/\/www.journalism.org\/2017\/09\/07\/news-use-across-social -media-platforms-2017\/."},{"key":"ref_2","unstructured":"(2019, October 31). Sohu-INC. Available online: http:\/\/www.sohu.com\/a\/246000912_393779."},{"key":"ref_3","unstructured":"(2019, October 31). Sina Weibo. Available online: https:\/\/weibo.com\/1866405545\/FFwn4EnNV?type=comment#_ rnd1543636471604."},{"key":"ref_4","unstructured":"(2019, October 31). CNBC. Available online: https:\/\/www.cnbc.com\/id\/100646197."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., and Procter, R. (2018). Detection and Resolution of Rumours in Social Media: A Survey. ACM Comput. Surv., 51.","DOI":"10.1145\/3161603"},{"key":"ref_6","unstructured":"Cao, J., Guo, J., Li, X., Jin, Z., Guo, H., and Li, J. (2018). Automatic Rumor Detection on Microblogs: A Survey. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1109\/TMM.2017.2757769","article-title":"Predicting microblog sentiments via weakly super-vised multi-modal deep learning","volume":"20","author":"Chen","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1109\/TMM.2018.2867718","article-title":"Cross-Modality Microblog Sentiment Prediction via Bi-Layer Multimodal Hypergraph Learning","volume":"21","author":"Ji","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_9","unstructured":"Qazvinian, V., Rosengren, E., Radev, D.R., and Mei, Q. (2011). Rumor has it: Identifying Misinformation in Microblogs, EMNLP 2011."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Castillo, C., Mendoza, M., and Poblete, B. (April, January 28). Information Credibility on Twitter. Proceedings of the International Conference on World Wide Web 2011, Hyderabad, India.","DOI":"10.1145\/1963405.1963500"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Yang, F., Yu, X., Liu, Y., and Yang, M. (2012, January 12\u201316). Automatic Detection of Rumor on Sina Weibo. Proceedings of the SIGKDD Workshop on Mining Data Semantics, Beijing, China.","DOI":"10.1145\/2350190.2350203"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kwon, S., Cha, M., and Jung, K. (2017). Rumor detection over varying time windows. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0168344"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1109\/TCSS.2016.2517458","article-title":"Rumor Identification in Microblogging Systems Based on Users\u2019 Behavior","volume":"2","author":"Liang","year":"2015","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Wu, K., Yang, S., and Zhu, K. (2015, January 13\u201317). False Rumors Detection on Sina Weibo by Propagation Structures. Proceedings of the ICDE2015, Seoul, Korea.","DOI":"10.1109\/ICDE.2015.7113322"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ma, J., Gao, W., Wei, Z., and Lu, Y. (2015, January 19\u201323). Detect Rumors Using Time Series of Social Context In-formation on Microblogging Websites. Proceedings of the CIKM2015, Melbourne, Australia.","DOI":"10.1145\/2806416.2806607"},{"key":"ref_16","unstructured":"Vosoughi, S. (2015). Automatic Detection and Verification of Rumors on Twitter. [Ph.D. Dissertation, MIT]."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Mousavi Kahaki, S.M., Nordin, M.J., Ahmad, N.S., Arzoky, M., and Ismail, W. (2019). Deep convolutional neural network designed for age assessment based on orthopantomography data. Neural Comput. Applic.","DOI":"10.1007\/s00521-019-04449-6"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kim, Y. (2014, January 25\u201329). Convolutional Neural Networks for Sentence Classification. Proceedings of the EMNLP 2014, Doha, Qatar.","DOI":"10.3115\/v1\/D14-1181"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.neucom.2015.12.091","article-title":"An Empirical Convolutional Neural Network approach for Semantic Relation Classification","volume":"190","author":"Qin","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ma, J., Cao, W., and Wong, K. (2018, January 15\u201320). Rumor Detection on Twitter with Tree-structured Recursive Neural Networks. Proceedings of the ACL 2018, Melbourne, Australia.","DOI":"10.18653\/v1\/P18-1184"},{"key":"ref_21","first-page":"141","article-title":"On Early-Stage Debunking Rumors on Twitter: Leveraging the Wisdom of Weak Learners","volume":"10540","author":"Nguyen","year":"2017","journal-title":"LNCS"},{"key":"ref_22","unstructured":"Ma, J., Cao, 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 IJCAI2016, New York, NY, USA."},{"key":"ref_23","unstructured":"Natali, R., Seo, S., and Liu, Y. (2017, January 6\u201310). CSI: A Hybrid Deep Model for Fake News Detection. Proceedings of the CIKM2017, Singapore."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., Bahdanau, D., and Bengio, Y. (2014, January 25\u201329). On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. Proceedings of the Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation (SSST-8), Doha, Qatar.","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A.R., and Hinton, G. (2013, January 26\u201330). Speech Recognition with Deep Recurrent Neu-ral Networks. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_27","unstructured":"Pascanu, R., G\u00fcl\u00e7ehre, C., Cho, K., and Bengio, Y. (2014, January 14\u201316). How to Construct Deep Recurrent Neural Networks. Proceedings of the ICLR 2014, Banff, AB, Canada."},{"key":"ref_28","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the ICML2015, Lille, France."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mousavi Kahaki, S.M., Nordin, M.J., Ashtari, A.H., and Zahra, J.S. (2016). Invariant Feature Matching for Image Registration Application Based on New Dissimilarity of Spatial Features. 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