{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T21:54:49Z","timestamp":1743026089228,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031301070"},{"type":"electronic","value":"9783031301087"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-30108-7_21","type":"book-chapter","created":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T04:03:04Z","timestamp":1681272184000},"page":"243-254","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-modal Rumor Detection via\u00a0Knowledge-Aware Heterogeneous Graph Convolutional Networks"],"prefix":"10.1007","author":[{"given":"Boqun","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhong","family":"Qian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peifeng","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiaoming","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,4,13]]},"reference":[{"key":"21_CR1","doi-asserted-by":"crossref","unstructured":"Bian, T., et al.: Rumor detection on social media with bi-directional graph convolutional networks. In: AAAI, pp. 549\u2013556. AAAI Press (2020)","DOI":"10.1609\/aaai.v34i01.5393"},{"key":"21_CR2","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, C.K., Lau, C.T., Lee, B.: Unsupervised rumor detection based on users\u2019 behaviors using neural networks. Pattern Recogn. Lett. 105, 226\u2013233 (2018)","journal-title":"Pattern Recogn. Lett."},{"key":"21_CR3","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT (1), pp. 4171\u20134186. Association for Computational Linguistics (2019)"},{"key":"21_CR4","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 Multimedia, pp. 795\u2013816. ACM (2017)","DOI":"10.1145\/3123266.3123454"},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Khoo, L.M.S., Chieu, H.L., Qian, Z., Jiang, J.: Interpretable rumor detection in microblogs by attending to user interactions. In: AAAI, pp. 8783\u20138790. AAAI Press (2020)","DOI":"10.1609\/aaai.v34i05.6405"},{"key":"21_CR6","unstructured":"Kochkina, E., Liakata, M., Zubiaga, A.: All-in-one: multi-task learning for rumour verification. In: COLING, pp. 3402\u20133413. Association for Computational Linguistics (2018)"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Li, Q., Zhang, Q., Si, L.: Rumor detection by exploiting user credibility information, attention and multi-task learning. In: ACL (1), pp. 1173\u20131179. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/P19-1113"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Liu, X., Nourbakhsh, A., Li, Q., Fang, R., Shah, S.: Real-time rumor debunking on Twitter. In: CIKM, pp. 1867\u20131870. ACM (2015)","DOI":"10.1145\/2806416.2806651"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Lu, Y., Li, C.: GCAN: graph-aware co-attention networks for explainable fake news detection on social media. In: ACL, pp. 505\u2013514. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.acl-main.48"},{"key":"21_CR10","unstructured":"Ma, J., et al.: Detecting rumors from microblogs with recurrent neural networks. In: IJCAI, pp. 3818\u20133824. IJCAI\/AAAI Press (2016)"},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"Ma, J., Gao, W., Wong, K.: Detect rumor and stance jointly by neural multi-task learning. In: WWW (Companion Volume), pp. 585\u2013593. ACM (2018)","DOI":"10.1145\/3184558.3188729"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Ma, J., Gao, W., Wong, K.: Rumor detection on Twitter with tree-structured recursive neural networks. In: ACL (1), pp. 1980\u20131989. Association for Computational Linguistics (2018)","DOI":"10.18653\/v1\/P18-1184"},{"key":"21_CR13","unstructured":"Sujana, Y., Li, J., Kao, H.: Rumor detection on Twitter using multiloss hierarchical BiLSTM with an attenuation factor. In: AACL\/IJCNLP, pp. 18\u201326. Association for Computational Linguistics (2020)"},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Sun, M., Zhang, X., Ma, J., Liu, Y.: Inconsistency matters: a knowledge-guided dual-inconsistency network for multi-modal rumor detection. In: EMNLP (Findings), pp. 1412\u20131423. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.122"},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Wang, Y., et al.: EANN: event adversarial neural networks for multi-modal fake news detection. In: KDD, pp. 849\u2013857. ACM (2018)","DOI":"10.1145\/3219819.3219903"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Wei, L., Hu, D., Zhou, W., Yue, Z., Hu, S.: Towards propagation uncertainty: edge-enhanced Bayesian graph convolutional networks for rumor detection. In: ACL\/IJCNLP (1), pp. 3845\u20133854. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.acl-long.297"},{"key":"21_CR17","unstructured":"Wu, B., et al.: Visual transformers: token-based image representation and processing for computer vision. CoRR abs\/2006.03677 (2020)"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Wu, L., Rao, Y., Zhao, Y., Liang, H., Nazir, A.: DTCA: decision tree-based co-attention networks for explainable claim verification. In: ACL, pp. 1024\u20131035. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.acl-main.97"},{"key":"21_CR19","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 Multimedia, pp. 1942\u20131951. ACM (2019)","DOI":"10.1145\/3343031.3350850"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-30108-7_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T04:07:41Z","timestamp":1681272461000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-30108-7_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031301070","9783031301087"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-30108-7_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"13 April 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":"New Delhi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iconip2022.apnns.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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"810","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":"359","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":"44% - 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":"2.65","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":"3","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)"}},{"value":"ICONIP 2022 consists of a two-volume set, LNCS & CCIS, which includes 146 and 213 papers","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}