iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://api.crossref.org/works/10.1007/S10489-024-05312-5
{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T00:45:33Z","timestamp":1710377133546},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72004021"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s10489-024-05312-5","type":"journal-article","created":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T07:02:26Z","timestamp":1708153346000},"page":"2851-2866","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Joint learning of structural and textual information on propagation network by graph attention networks for rumor detection"],"prefix":"10.1007","volume":"54","author":[{"given":"Qihang","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Yuzhe","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0001-9975-9807","authenticated-orcid":false,"given":"Xiaodong","family":"Feng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,2,17]]},"reference":[{"key":"5312_CR1","unstructured":"Baek J, Lee DB, Hwang SJ (2020) Learning to extrapolate knowledge: transductive few-shot out-of-graph link prediction. In Advances in neural information processing systems 33: annual conference on neural information processing systems 2020, NeurIPS 2020, December 6-12, 2020, virtual"},{"key":"5312_CR2","doi-asserted-by":"crossref","unstructured":"Bian T, Xiao X, Xu T, Zhao P, Huang W, Rong Y, Huang J (2020) Rumor detection on social media with bi-directional graph convolutional networks. In The Thirty-fourth AAAI conference on artificial intelligence, AAAI 2020, New York, USA, February 7-12, 2020, pp 549\u2013556. AAAI Press","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"5312_CR3","unstructured":"Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3(Jan):993\u20131022"},{"key":"5312_CR4","doi-asserted-by":"crossref","unstructured":"Castillo C, Mendoza M, Poblete B (2011) Information credibility on twitter. In Proceedings of the 20th international conference on world wide web, pp 675\u2013684","DOI":"10.1145\/1963405.1963500"},{"key":"5312_CR5","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.patrec.2017.10.014","volume":"105","author":"W Chen","year":"2018","unstructured":"Chen W, Zhang Y, Yeo CK, Lau CT, Lee B-S (2018) Unsupervised rumor detection based on users\u2019 behaviors using neural networks. Pattern Recogn Lett 105:226\u2013233","journal-title":"Pattern Recogn Lett"},{"key":"5312_CR6","doi-asserted-by":"crossref","unstructured":"Chen X, Zhou F, Trajcevski G, Bonsangue M (2022) Multi-view learning with distinguishable feature fusion for rumor detection. Knowl-Based Syst 240:108085","DOI":"10.1016\/j.knosys.2021.108085"},{"issue":"5","key":"5312_CR7","doi-asserted-by":"publisher","first-page":"102678","DOI":"10.1016\/j.ipm.2021.102678","volume":"58","author":"X Chen","year":"2021","unstructured":"Chen X, Zhou F, Zhang F, Bonsangue M (2021) Catch me if you can: a participant-level rumor detection framework via fine-grained user representation learning. Inf Process Manag 58(5):102678","journal-title":"Inf Process Manag"},{"key":"5312_CR8","doi-asserted-by":"crossref","unstructured":"Chen Y, Hu L, Sui J (2019) Text-based fusion neural network for rumor detection. In: Knowledge science, engineering and management: 12th international conference, KSEM 2019, Athens, Greece, August 28\u201330, 2019, Proceedings, Part II 12, pp 105\u2013109. Springer","DOI":"10.1007\/978-3-030-29563-9_11"},{"key":"5312_CR9","doi-asserted-by":"crossref","unstructured":"Chen Y, Sui J, Hu L, Gong W (2019) Attention-residual network with cnn for rumor detection. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 1121\u20131130","DOI":"10.1145\/3357384.3357950"},{"key":"5312_CR10","doi-asserted-by":"crossref","unstructured":"Dong M, Zheng B, Hung NQV, Su H, Li G (2019) Multiple rumor source detection with graph convolutional networks. In: Proceedings of the 28th ACM international conference on information and knowledge management, pp 569\u2013578","DOI":"10.1145\/3357384.3357994"},{"key":"5312_CR11","doi-asserted-by":"crossref","unstructured":"Grover A, Leskovec J (2016) Node2vec: scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, CA, USA, August 13\u201317, 2016, pp 855\u2013864. ACM","DOI":"10.1145\/2939672.2939754"},{"key":"5312_CR12","doi-asserted-by":"crossref","unstructured":"He Z, Li C, Zhou F, Yang Y (2021) Rumor detection on social media with event augmentations. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval, pp 2020\u20132024","DOI":"10.1145\/3404835.3463001"},{"key":"5312_CR13","doi-asserted-by":"crossref","unstructured":"Huang Q, Yu J, Wu J, Wang B (2020) Heterogeneous graph attention networks for early detection of rumors on twitter. In: 2020 international joint conference on neural networks (IJCNN), pp 1\u20138. IEEE","DOI":"10.1109\/IJCNN48605.2020.9207582"},{"key":"5312_CR14","doi-asserted-by":"crossref","unstructured":"Huang Q, Zhou C, Wu J, Liu L, Wang B (2020) Deep spatial\u2013temporal structure learning for rumor detection on twitter. Neural Comput & Applic 1\u201311","DOI":"10.1109\/IJCNN.2019.8852468"},{"key":"5312_CR15","doi-asserted-by":"crossref","unstructured":"Huang Q, Zhou C, Wu J, Wang M, Wang B (2019) Deep structure learning for rumor detection on twitter. In: 2019 International joint conference on neural networks (IJCNN), pp 1\u20138","DOI":"10.1109\/IJCNN.2019.8852468"},{"key":"5312_CR16","unstructured":"Ivanov S, Burnaev E (2018) Anonymous walk embeddings. In: Proceedings of the 35th international conference on machine learning, ICML 2018, Stockholmsm\u00e4ssan, Stockholm, Sweden, July 10-15, 2018, vol 80 of proceedings of machine learning research, pp 2191\u20132200. PMLR"},{"key":"5312_CR17","doi-asserted-by":"crossref","unstructured":"Jin Z, Cao J, Guo H, Zhang Y, Luo J (2017) Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In: Proceedings of the 25th ACM international conference on multimedia, pp 795\u2013816","DOI":"10.1145\/3123266.3123454"},{"key":"5312_CR18","doi-asserted-by":"crossref","unstructured":"Jogalekar NS, Attar V, Palshikar GK (2020) Rumor detection on social networks: a sociological approach. In: IEEE International conference on big data, big data 2020, Atlanta, GA, USA, December 10-13, 2020, pp 3877\u20133884. IEEE","DOI":"10.1109\/BigData50022.2020.9378149"},{"key":"5312_CR19","doi-asserted-by":"crossref","unstructured":"Ke L, Chen X, Lu Z, Su H, Wang H (2020) A novel approach for cantonese rumor detection based on deep neural network. In 2020 IEEE International conference on systems, man, and cybernetics (SMC), pp 1610\u20131615. IEEE","DOI":"10.1109\/SMC42975.2020.9283056"},{"key":"5312_CR20","doi-asserted-by":"crossref","unstructured":"Khoo LMS, Chieu HL, Qian Z, Jiang J (2020) Interpretable rumor detection in microblogs by attending to user interactions. In: The Thirty-fourth AAAI conference on artificial intelligence, AAAI 2020, New York, USA, February 7-12, 2020, pp 8783\u20138790. AAAI Press","DOI":"10.1609\/aaai.v34i05.6405"},{"key":"5312_CR21","doi-asserted-by":"crossref","unstructured":"Kwon S, Cha M, Jung K, Chen W, Wang Y (2013) Prominent features of rumor propagation in online social media. In: 2013 IEEE 13th International conference on data mining, pp 1103\u20131108","DOI":"10.1109\/ICDM.2013.61"},{"key":"5312_CR22","doi-asserted-by":"crossref","unstructured":"Li J, Rong Y, Cheng H, Meng H, Huang W-b, Huang J (2019) Semi-supervised graph classification: a hierarchical graph perspective. In: The World wide web conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019, pp 972\u2013982. ACM","DOI":"10.1145\/3308558.3313461"},{"key":"5312_CR23","doi-asserted-by":"crossref","unstructured":"Li Q, Zhang Q, Si L (2019) Rumor detection by exploiting user credibility information, attention and multi-task learning. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 1173\u20131179","DOI":"10.18653\/v1\/P19-1113"},{"key":"5312_CR24","doi-asserted-by":"crossref","unstructured":"Li X, Shang Y, Cao Y, Li Y, Tan J, Liu Y (2020) Type-aware anchor link prediction across heterogeneous networks based on graph attention network. In: The Thirty-fourth AAAI conference on artificial intelligence, AAAI 2020, New York, USA, February 7-12, 2020, pp 147\u2013155","DOI":"10.1609\/aaai.v34i01.5345"},{"key":"5312_CR25","unstructured":"Liu Y, Wu Y-fB (2018) Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the thirty-second AAAI conference on artificial intelligence, (AAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, pp 354\u2013361. AAAI Press"},{"key":"5312_CR26","doi-asserted-by":"crossref","unstructured":"Long Q, Jin Y, Song G, Li Y, Lin W (2020) Graph structural-topic neural network. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 1065\u20131073","DOI":"10.1145\/3394486.3403150"},{"key":"5312_CR27","doi-asserted-by":"crossref","unstructured":"Ma J, Liu Y, Han M, Hu C, Ju Z (2023) Propagation structure fusion for rumor detection based on node-level contrastive learning. IEEE Transactions on Neural Networks and Learning Systems, Early Access","DOI":"10.1109\/TNNLS.2023.3319661"},{"key":"5312_CR28","unstructured":"Ma J, Gao W, Mitra P, Kwon S, Jansen BJ, Wong K-F, Cha M (2016) Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of the twenty-fifth international joint conference on artificial intelligence, pp 3818\u20133824"},{"key":"5312_CR29","doi-asserted-by":"crossref","unstructured":"Ma J, Gao W, Wei Z, Lu Y, Wong K-F (2015) Detect rumors using time series of social context information on microblogging websites. In: Proceedings of the 24th ACM international conference on information and knowledge management, CIKM 2015, Melbourne, VIC, Australia, October 19 - 23, 2015, pp 1751\u20131754. ACM","DOI":"10.1145\/2806416.2806607"},{"key":"5312_CR30","doi-asserted-by":"crossref","unstructured":"Ma J, Gao W, Wong K-F (2017) Detect rumors in microblog posts using propagation structure via kernel learning. In: Proceedings of the 55th annual meeting of the association for computational linguistics (vol 1, Long Papers), pp 708\u2013717, Vancouver, Canada. Association for Computational Linguistics","DOI":"10.18653\/v1\/P17-1066"},{"key":"5312_CR31","doi-asserted-by":"crossref","unstructured":"Ma J, Gao W, Wong K-F (2018) Rumor detection on twitter with tree-structured recursive neural networks. In: Proceedings of the 56th annual meeting of the association for computational linguistics (vol 1, Long Papers), pp 1980\u20131989","DOI":"10.18653\/v1\/P18-1184"},{"key":"5312_CR32","doi-asserted-by":"crossref","unstructured":"Micali S, Allen-Zhu Z (2016) Reconstructing markov processes from independent and anonymous experiments. Discret Appl Math 108\u2013122","DOI":"10.1016\/j.dam.2015.06.035"},{"key":"5312_CR33","unstructured":"Oono K, Suzuki T (2020) Graph neural networks exponentially lose expressive power for node classification. In: 8th International conference on learning representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020"},{"key":"5312_CR34","doi-asserted-by":"crossref","unstructured":"Park H, Neville J (2019) Exploiting interaction links for node classification with deep graph neural networks. In: Proceedings of the twenty-eighth international joint conference on artificial intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pp 3223\u20133230","DOI":"10.24963\/ijcai.2019\/447"},{"key":"5312_CR35","doi-asserted-by":"crossref","unstructured":"Peng H, Li J, Gong Q, Ning Y, Wang S, He L (2020) Motif-matching based subgraph-level attentional convolutional network for graph classification. In: The Thirty-fourth AAAI conference on artificial intelligence, AAAI 2020, New York, NY, USA, February 7-12, 2020, pp 5387\u20135394. AAAI Press","DOI":"10.1609\/aaai.v34i04.5987"},{"issue":"1","key":"5312_CR36","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1109\/TCSS.2019.2946181","volume":"7","author":"J Qiu","year":"2019","unstructured":"Qiu J, Chai Y, Tian Z, Du X, Guizani M (2019) Automatic concept extraction based on semantic graphs from big data in smart city. IEEE Trans Comput Soc Syst 7(1):225\u2013233","journal-title":"IEEE Trans Comput Soc Syst"},{"key":"5312_CR37","doi-asserted-by":"publisher","first-page":"1519","DOI":"10.1007\/s13042-017-0661-0","volume":"9","author":"J Qiu","year":"2018","unstructured":"Qiu J, Qi L, Wang J, Zhang G (2018) A hybrid-based method for chinese domain lightweight ontology construction. Int J Mach Learn Cybernet 9:1519\u20131531","journal-title":"Int J Mach Learn Cybernet"},{"issue":"5","key":"5312_CR38","doi-asserted-by":"publisher","first-page":"102618","DOI":"10.1016\/j.ipm.2021.102618","volume":"58","author":"A Silva","year":"2021","unstructured":"Silva A, Han Y, Luo L, Karunasekera S, Leckie C (2021) Propagation2vec: embedding partial propagation networks for explainable fake news early detection. Inf Process Manag 58(5):102618","journal-title":"Inf Process Manag"},{"key":"5312_CR39","doi-asserted-by":"crossref","unstructured":"Sivasangari V, Mohan AK, Suthendran K, Sethumadhavan M (2018) Isolating rumors using sentiment analysis. J Cyber Secur Mobil 7(1): 181\u2013200","DOI":"10.13052\/2245-1439.7113"},{"key":"5312_CR40","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.ins.2020.12.080","volume":"560","author":"K Tu","year":"2021","unstructured":"Tu K, Chen C, Hou C, Yuan J, Li J, Yuan X (2021) Rumor2vec: a rumor detection framework with joint text and propagation structure representation learning. Inf Sci 560:137\u2013151","journal-title":"Inf Sci"},{"key":"5312_CR41","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2018) Graph attention networks. In 6th International conference on learning representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018"},{"key":"5312_CR42","doi-asserted-by":"publisher","first-page":"224","DOI":"10.1016\/j.neucom.2020.01.095","volume":"397","author":"Z Wang","year":"2020","unstructured":"Wang Z, Guo Y (2020) Rumor events detection enhanced by encoding sentimental information into time series division and word representations. Neurocomputing 397:224\u2013243","journal-title":"Neurocomputing"},{"key":"5312_CR43","doi-asserted-by":"crossref","unstructured":"Wu Z, Pi D, Chen J, Xie M, Cao J (2020) Rumor detection based on propagation graph neural network with attention mechanism. Expert Syst Appl 158:113595","DOI":"10.1016\/j.eswa.2020.113595"},{"key":"5312_CR44","doi-asserted-by":"crossref","unstructured":"Yang F, Liu Y, Yu X, Yang M (2012) Automatic detection of rumor on sina weibo. In Proceedings of the ACM SIGKDD workshop on mining data semantics, MDS \u201912, pp 1\u20137, New York, USA. Association for Computing Machinery","DOI":"10.1145\/2350190.2350203"},{"key":"5312_CR45","doi-asserted-by":"crossref","unstructured":"Yang X, Lyu Y, Tian T, Liu Y, Liu Y, Zhang X (2021) Rumor detection on social media with graph structured adversarial learning. In: Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence, pp 1417\u20131423","DOI":"10.24963\/ijcai.2020\/197"},{"key":"5312_CR46","unstructured":"Yang Y, Zheng L, Zhang J, Cui Q, Li Z, Yu PS (2018) TI-CNN: convolutional neural networks for fake news detection. arXiv:1806.00749"},{"key":"5312_CR47","doi-asserted-by":"crossref","unstructured":"Yu F, Liu Q, Wu S, Wang L, Tan T (2017) A convolutional approach for misinformation identification. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence, IJCAI-17, pp 3901\u20133907","DOI":"10.24963\/ijcai.2017\/545"},{"key":"5312_CR48","doi-asserted-by":"crossref","unstructured":"Yu Z, Lu S, Wang D, Li Z (2021) Modeling and analysis of rumor propagation in social networks. Inf Sci 580:857\u2013873","DOI":"10.1016\/j.ins.2021.09.012"},{"issue":"2","key":"5312_CR49","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/s00365-006-0663-2","volume":"26","author":"Y Yuan","year":"2007","unstructured":"Yuan Y, Rosasco L, Caponnetto A (2007) On early stopping in gradient descent learning. Constr Approx 26(2):289\u2013315","journal-title":"Constr Approx"},{"key":"5312_CR50","doi-asserted-by":"crossref","unstructured":"Zhao Z, Resnick P, Mei Q (2015) Enquiring minds: early detection of rumors in social media from enquiry posts. In: Proceedings of the 24th International Conference on World Wide Web, WWW \u201915, page 1395\u20131405, Republic and Canton of Geneva, CHE. International World Wide Web Conferences Steering Committee","DOI":"10.1145\/2736277.2741637"},{"key":"5312_CR51","unstructured":"Zhou L, Li J, Gu Z, Qiu J, Gupta BB, Tian Z (2022) Panner: pos-aware nested named entity recognition through heterogeneous graph neural network. IEEE Trans Comput Soc Syst, Early Access"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05312-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-05312-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-05312-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T20:46:19Z","timestamp":1710362779000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-05312-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2]]},"references-count":51,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["5312"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-05312-5","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2]]},"assertion":[{"value":"1 February 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Ethical approval was not needed for this research.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and Informed Consent for Data Used"}},{"value":"The authors declare that they have no known competing interests or personal relationships that could have appeared to influence the work reported in this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}