{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T10:02:14Z","timestamp":1768557734532,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819980758","type":"print"},{"value":"9789819980765","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T00:00:00Z","timestamp":1699920000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,14]],"date-time":"2023-11-14T00:00:00Z","timestamp":1699920000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8076-5_20","type":"book-chapter","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T14:02:10Z","timestamp":1699884130000},"page":"280-291","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Inter-modal Fusion Network with\u00a0Graph Structure Preserving for\u00a0Fake News Detection"],"prefix":"10.1007","author":[{"given":"Jing","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Jin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoke","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao-Yuan","family":"Jing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,14]]},"reference":[{"key":"20_CR1","doi-asserted-by":"crossref","unstructured":"Bian, T., et al.: Rumor detection on social media with bi-directional graph convolutional networks. In: AAAI Conference on Artificial Intelligence, pp. 549\u2013556 (2020)","DOI":"10.1609\/aaai.v34i01.5393"},{"issue":"1","key":"20_CR2","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1007\/s13735-017-0143-x","volume":"7","author":"C Boididou","year":"2018","unstructured":"Boididou, C., Papadopoulos, S., Zampoglou, M., Apostolidis, L., Papadopoulou, O., Kompatsiaris, Y.: Detection and visualization of misleading content on Twitter. Int. J. Multimed. Inf. Retr. 7(1), 71\u201386 (2018)","journal-title":"Int. J. Multimed. Inf. Retr."},{"key":"20_CR3","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607 (2020)"},{"key":"20_CR4","doi-asserted-by":"crossref","unstructured":"Chen, T., Li, X., Yin, H., Zhang, J.: Call attention to rumors: deep attention based recurrent neural networks for early rumor detection. In: Trends and Applications in Knowledge Discovery and Data Mining, pp. 40\u201352 (2018)","DOI":"10.1007\/978-3-030-04503-6_4"},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: Cross-modal ambiguity learning for multimodal fake news detection. In: The World Wide Web Conference, pp. 2897\u20132905 (2022)","DOI":"10.1145\/3485447.3511968"},{"key":"20_CR6","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, pp. 1024\u20131034 (2017)"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Jin, Z., Cao, J., Guo, H., Zhang, Y., Luo, J.: Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In: ACM International Conference on Multimedia, pp. 795\u2013816 (2017)","DOI":"10.1145\/3123266.3123454"},{"issue":"3","key":"20_CR9","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1109\/TMM.2016.2617078","volume":"19","author":"Z Jin","year":"2016","unstructured":"Jin, Z., Cao, J., Zhang, Y., Zhou, J., Tian, Q.: Novel visual and statistical image features for microblogs news verification. IEEE Trans. Multimed. 19(3), 598\u2013608 (2016)","journal-title":"IEEE Trans. Multimed."},{"key":"20_CR10","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4171\u20134186 (2019)"},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"Khattar, D., Goud, J.S., Gupta, M., Varma, V.: MVAE: multimodal variational autoencoder for fake news detection. In: The World Wide Web Conference, pp. 2915\u20132921 (2019)","DOI":"10.1145\/3308558.3313552"},{"key":"20_CR12","unstructured":"Khosla, P., et al.: Supervised contrastive learning. In: Advances in Neural Information Processing Systems, pp. 18661\u201318673 (2020)"},{"key":"20_CR13","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"20_CR14","unstructured":"Ma, J., et al.: Detecting rumors from microblogs with recurrent neural networks. In: International Joint Conference on Artificial Intelligence, pp. 3818\u20133824 (2016)"},{"key":"20_CR15","doi-asserted-by":"crossref","unstructured":"Qi, P., Cao, J., Yang, T., Guo, J., Li, J.: Exploiting multi-domain visual information for fake news detection. In: IEEE International Conference on Data Mining, pp. 518\u2013527 (2019)","DOI":"10.1109\/ICDM.2019.00062"},{"key":"20_CR16","doi-asserted-by":"crossref","unstructured":"Qian, F., Gong, C., Sharma, K., Liu, Y.: Neural user response generator: fake news detection with collective user intelligence. In: International Joint Conference on Artificial Intelligence, pp. 3834\u20133840 (2018)","DOI":"10.24963\/ijcai.2018\/533"},{"issue":"1","key":"20_CR17","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","volume":"20","author":"F Scarselli","year":"2008","unstructured":"Scarselli, F., Gori, M., Tsoi, A.C., Hagenbuchner, M., Monfardini, G.: The graph neural network model. IEEE Trans. Neural Netw. 20(1), 61\u201380 (2008)","journal-title":"IEEE Trans. Neural Netw."},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Vaibhav, V., Mandyam, R., Hovy, E.: Do sentence interactions matter? Leveraging sentence level representations for fake news classification. In: Graph-Based Methods for Natural Language Processing, pp. 134\u2013139 (2019)","DOI":"10.18653\/v1\/D19-5316"},{"key":"20_CR19","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"},{"key":"20_CR20","doi-asserted-by":"crossref","unstructured":"Wang, L., Zhang, C., Xu, H., Zhang, S., Xu, X., Wang, S.: Cross-modal contrastive learning for multimodal fake news detection. arXiv preprint arXiv:2302.14057 (2023)","DOI":"10.1145\/3581783.3613850"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: EANN: event adversarial neural networks for multi-modal fake news detection. In: ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 849\u2013857 (2018)","DOI":"10.1145\/3219819.3219903"},{"key":"20_CR22","doi-asserted-by":"crossref","unstructured":"Wu, Y., Zhan, P., Zhang, Y., Wang, L., Xu, Z.: Multimodal fusion with co-attention networks for fake news detection. In: Findings of the Association for Computational Linguistics, ACL-IJCNLP, pp. 2560\u20132569 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.226"},{"issue":"5","key":"20_CR23","doi-asserted-by":"publisher","first-page":"102610","DOI":"10.1016\/j.ipm.2021.102610","volume":"58","author":"J Xue","year":"2021","unstructured":"Xue, J., Wang, Y., Tian, Y., Li, Y., Shi, L., Wei, L.: Detecting fake news by exploring the consistency of multimodal data. Inf. Process. Manage. 58(5), 102610 (2021)","journal-title":"Inf. Process. Manage."},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Yu, F., Liu, Q., Wu, S., Wang, L., Tan, T., et al.: A convolutional approach for misinformation identification. In: International Joint Conference on Artificial Intelligence, pp. 3901\u20133907 (2017)","DOI":"10.24963\/ijcai.2017\/545"},{"key":"20_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, H., Fang, Q., Qian, S., Xu, C.: Multi-modal knowledge-aware event memory network for social media rumor detection. In: ACM International Conference on Multimedia, pp. 1942\u20131951 (2019)","DOI":"10.1145\/3343031.3350850"},{"key":"20_CR26","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1007\/978-3-030-47436-2_27","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"X Zhou","year":"2020","unstructured":"Zhou, X., Wu, J., Zafarani, R.: $$\\sf SAFE$$: similarity-aware multi-modal fake news detection. In: Lauw, H.W., Wong, R.C.-W., Ntoulas, A., Lim, E.-P., Ng, S.-K., Pan, S.J. (eds.) PAKDD 2020. LNCS (LNAI), vol. 12085, pp. 354\u2013367. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-47436-2_27"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8076-5_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T15:17:06Z","timestamp":1710256626000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8076-5_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,14]]},"ISBN":["9789819980758","9789819980765"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8076-5_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,14]]},"assertion":[{"value":"14 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1274","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"650","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"51% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4.14","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.46","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}