{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,15]],"date-time":"2025-06-15T14:28:08Z","timestamp":1749997688900,"version":"3.40.3"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031416811"},{"type":"electronic","value":"9783031416828"}],"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\/978-3-031-41682-8_15","type":"book-chapter","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T07:02:59Z","timestamp":1692342179000},"page":"231-248","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Multimodal Rumour Detection: Catching News that\u00a0Never Transpired!"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9488-3099","authenticated-orcid":false,"given":"Raghvendra","family":"Kumar","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7003-489X","authenticated-orcid":false,"given":"Ritika","family":"Sinha","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5494-9391","authenticated-orcid":false,"given":"Sriparna","family":"Saha","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7235-0665","authenticated-orcid":false,"given":"Adam","family":"Jatowt","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,19]]},"reference":[{"key":"15_CR1","doi-asserted-by":"publisher","unstructured":"Bai, N., Meng, F., Rui, X., Wang, Z.: Rumour detection based on graph convolutional neural net. IEEE Access 1 (2021). https:\/\/doi.org\/10.1109\/ACCESS.2021.3050563","DOI":"10.1109\/ACCESS.2021.3050563"},{"key":"15_CR2","doi-asserted-by":"publisher","unstructured":"Bai, Y., Yi, J., Tao, J., Tian, Z., Wen, Z., Zhang, S.: Fast end-to-end speech recognition via non-autoregressive models and cross-modal knowledge transferring from bert. IEEE\/ACM Trans. Audio, Speech and Lang. Proc. 29, 1897\u20131911 (2021). https:\/\/doi.org\/10.1109\/TASLP.2021.3082299","DOI":"10.1109\/TASLP.2021.3082299"},{"key":"15_CR3","doi-asserted-by":"publisher","unstructured":"Borth, D., Ji, R., Chen, T., Breuel, T., Chang, S.F.: Large-scale visual sentiment ontology and detectors using adjective noun pairs. In: Proceedings of the 21st ACM International Conference on Multimedia, MM 2013, pp. 223\u2013232. Association for Computing Machinery, New York, NY, USA (2013). https:\/\/doi.org\/10.1145\/2502081.2502282","DOI":"10.1145\/2502081.2502282"},{"key":"15_CR4","doi-asserted-by":"publisher","unstructured":"Chen, Y., Sui, J., Hu, L., Gong, W.: Attention-residual network with CNN for rumor detection. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, pp. 1121\u20131130. Association for Computing Machinery, New York, NY, USA (2019). https:\/\/doi.org\/10.1145\/3357384.3357950","DOI":"10.1145\/3357384.3357950"},{"key":"15_CR5","doi-asserted-by":"publisher","unstructured":"Cheung, T.H., Lam, K.M.: Transformer-graph neural network with global-local attention for multimodal rumour detection with knowledge distillation (2022). https:\/\/doi.org\/10.48550\/ARXIV.2206.04832, https:\/\/arxiv.org\/abs\/2206.04832","DOI":"10.48550\/ARXIV.2206.04832"},{"key":"15_CR6","doi-asserted-by":"publisher","unstructured":"Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1800\u20131807. IEEE Computer Society, Los Alamitos, CA, USA, July 2017. https:\/\/doi.org\/10.1109\/CVPR.2017.195, https:\/\/doi.org\/ieeecomputersociety.org\/10.1109\/CVPR.2017.195","DOI":"10.1109\/CVPR.2017.195"},{"key":"15_CR7","doi-asserted-by":"publisher","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255 (2009). https:\/\/doi.org\/10.1109\/CVPR.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"15_CR8","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota, June 2019. https:\/\/doi.org\/10.18653\/v1\/N19-1423, https:\/\/aclanthology.org\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"15_CR9","doi-asserted-by":"publisher","unstructured":"Ghani, N.A., Hamid, S., Targio Hashem, I.A., Ahmed, E.: Social media big data analytics: a survey. Computers in Human Behavior 101, 417\u2013428 (2019). https:\/\/doi.org\/10.1016\/j.chb.2018.08.039, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S074756321830414X","DOI":"10.1016\/j.chb.2018.08.039"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016","DOI":"10.1109\/CVPR.2016.90"},{"key":"15_CR11","doi-asserted-by":"publisher","unstructured":"Jin, Z., Cao, J., Guo, H., Zhang, Y., Luo, J.: Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In: Proceedings of the 25th ACM International Conference on Multimedia, MM 2017, pp. 795\u2013816. Association for Computing Machinery, New York, NY, USA (2017). https:\/\/doi.org\/10.1145\/3123266.3123454, https:\/\/doi.org\/10.1145\/3123266.3123454","DOI":"10.1145\/3123266.3123454"},{"key":"15_CR12","doi-asserted-by":"publisher","unstructured":"Kwon, S., Cha, M., Jung, K., Chen, W., Wang, Y.: Prominent features of rumor propagation in online social media. In: 2013 IEEE 13th International Conference on Data Mining, pp. 1103\u20131108 (2013). https:\/\/doi.org\/10.1109\/ICDM.2013.61","DOI":"10.1109\/ICDM.2013.61"},{"issue":"1","key":"15_CR13","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1109\/TCSVT.2021.3060162","volume":"32","author":"T Liu","year":"2022","unstructured":"Liu, T., Lam, K., Zhao, R., Qiu, G.: Deep cross-modal representation learning and distillation for illumination-invariant pedestrian detection. IEEE Trans. Circ. Syst. Video Technol. 32(1), 315\u2013329 (2022). https:\/\/doi.org\/10.1109\/TCSVT.2021.3060162","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"key":"15_CR14","unstructured":"Liu, Y., et al.: Roberta: a robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 (2019)"},{"key":"15_CR15","unstructured":"Ma, J., et al.: Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, pp. 3818\u20133824. AAAI Press (2016)"},{"key":"15_CR16","doi-asserted-by":"publisher","unstructured":"Ma, M., Ren, J., Zhao, L., Tulyakov, S., Wu, C., Peng, X.: Smil: multimodal learning with severely missing modality (2021). https:\/\/doi.org\/10.48550\/ARXIV.2103.05677, https:\/\/arxiv.org\/abs\/2103.05677","DOI":"10.48550\/ARXIV.2103.05677"},{"key":"15_CR17","doi-asserted-by":"publisher","unstructured":"Mukherjee, R., et al.: MTLTS: a multi-task framework to obtain trustworthy summaries from crisis-related microblogs. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, ACM, February 2022. https:\/\/doi.org\/10.1145\/3488560.3498536","DOI":"10.1145\/3488560.3498536"},{"key":"15_CR18","doi-asserted-by":"crossref","unstructured":"Nguyen, D.Q., Vu, T., Nguyen, A.T.: BERTweet: a pre-trained language model for English Tweets. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pp. 9\u201314 (2020)","DOI":"10.18653\/v1\/2020.emnlp-demos.2"},{"key":"15_CR19","doi-asserted-by":"publisher","unstructured":"Pathak, A.R., Mahajan, A., Singh, K., Patil, A., Nair, A.: Analysis of techniques for rumor detection in social media. Procedia Comput. Sci. 167, 2286\u20132296 (2020). https:\/\/doi.org\/10.1016\/j.procs.2020.03.281, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S187705092030747X, international Conference on Computational Intelligence and Data Science","DOI":"10.1016\/j.procs.2020.03.281"},{"key":"15_CR20","doi-asserted-by":"publisher","unstructured":"Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543. Association for Computational Linguistics, Doha, Qatar, October 2014. https:\/\/doi.org\/10.3115\/v1\/D14-1162, https:\/\/aclanthology.org\/D14-1162","DOI":"10.3115\/v1\/D14-1162"},{"key":"15_CR21","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)"},{"issue":"8","key":"15_CR22","doi-asserted-by":"publisher","first-page":"3035","DOI":"10.1109\/TKDE.2019.2961675","volume":"33","author":"C Song","year":"2021","unstructured":"Song, C., Yang, C., Chen, H., Tu, C., Liu, Z., Sun, M.: Ced: credible early detection of social media rumors. IEEE Trans. Knowl. Data Eng. 33(8), 3035\u20133047 (2021). https:\/\/doi.org\/10.1109\/TKDE.2019.2961675","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"15_CR23","doi-asserted-by":"publisher","unstructured":"Sun, S., Cheng, Y., Gan, Z., Liu, J.: Patient knowledge distillation for BERT model compression. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 4323\u20134332. Association for Computational Linguistics, Hong Kong, China, November 2019. https:\/\/doi.org\/10.18653\/v1\/D19-1441, https:\/\/aclanthology.org\/D19-1441","DOI":"10.18653\/v1\/D19-1441"},{"key":"15_CR24","doi-asserted-by":"publisher","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2818\u20132826 (2016). https:\/\/doi.org\/10.1109\/CVPR.2016.308","DOI":"10.1109\/CVPR.2016.308"},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"Takahashi, T., Igata, N.: Rumor detection on twitter. In: The 6th International Conference on Soft Computing and Intelligent Systems, and The 13th International Symposium on Advanced Intelligence Systems, pp. 452\u2013457 (2012)","DOI":"10.1109\/SCIS-ISIS.2012.6505254"},{"key":"15_CR26","doi-asserted-by":"publisher","unstructured":"Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1480\u20131489. Association for Computational Linguistics, San Diego, California, June 2016. https:\/\/doi.org\/10.18653\/v1\/N16-1174, https:\/\/aclanthology.org\/N16-1174","DOI":"10.18653\/v1\/N16-1174"},{"key":"15_CR27","doi-asserted-by":"publisher","unstructured":"Zubiaga, A., Aker, A., Bontcheva, K., Liakata, M., Procter, R.: Detection and resolution of rumours in social media. ACM Comput. Surv. 51(2), 1\u201336 (2018). https:\/\/doi.org\/10.1145\/3161603","DOI":"10.1145\/3161603"},{"key":"15_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1007\/978-3-319-67217-5_8","volume-title":"Social Informatics","author":"A Zubiaga","year":"2017","unstructured":"Zubiaga, A., Liakata, M., Procter, R.: Exploiting context for rumour detection in\u00a0social media. In: Ciampaglia, G.L., Mashhadi, A., Yasseri, T. (eds.) SocInfo 2017. LNCS, vol. 10539, pp. 109\u2013123. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-67217-5_8"}],"container-title":["Lecture Notes in Computer Science","Document Analysis and Recognition - ICDAR 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-41682-8_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T07:19:02Z","timestamp":1692343142000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-41682-8_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031416811","9783031416828"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-41682-8_15","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":"19 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDAR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Document Analysis and Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"San Jos\u00e9, CA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","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":"21 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdar2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icdar2023.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"316","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":"154","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":"49% - 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":"2.89","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":"1.50","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Number and type of other papers accepted : IJDAR track 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)"}}]}}