{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T14:22:43Z","timestamp":1780755763529,"version":"3.54.1"},"publisher-location":"Cham","reference-count":28,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031466762","type":"print"},{"value":"9783031466779","type":"electronic"}],"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\/978-3-031-46677-9_29","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:02:29Z","timestamp":1699102949000},"page":"421-436","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["FSKD: Detecting Fake News with\u00a0Few-Shot Knowledge Distillation"],"prefix":"10.1007","author":[{"given":"Jing","family":"Yuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunyan","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojie","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"29_CR1","unstructured":"Ba, J., Caruana, R.: Do deep nets really need to be deep? In: NeurIPS, pp. 2654\u20132662 (2014)"},{"key":"29_CR2","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT, pp. 4171\u20134186 (2019)"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Douze, M., Szlam, A., Hariharan, B., J\u00e9gou, H.: Low-shot learning with large-scale diffusion. In: CVPR, pp. 3349\u20133358 (2018)","DOI":"10.1109\/CVPR.2018.00353"},{"key":"29_CR4","doi-asserted-by":"crossref","unstructured":"Geng, R., Li, B., Li, Y., Zhu, X., Jian, P., Sun, J.: Induction networks for few-shot text classification. In: EMNLP-IJCNLP, pp. 3902\u20133911 (2019)","DOI":"10.18653\/v1\/D19-1403"},{"issue":"6","key":"29_CR5","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","volume":"129","author":"J Gou","year":"2021","unstructured":"Gou, J., Yu, B., Maybank, S.J., Tao, D.: Knowledge distillation: a survey. Int. J. Comput. Vis. 129(6), 1789\u20131819 (2021)","journal-title":"Int. J. Comput. Vis."},{"key":"29_CR6","doi-asserted-by":"crossref","unstructured":"Gu, J., Wang, Y., Chen, Y., Li, V.O.K., Cho, K.: Meta-learning for low-resource neural machine translation. In: EMNLP, pp. 3622\u20133631 (2018)","DOI":"10.18653\/v1\/D18-1398"},{"key":"29_CR7","unstructured":"Hinton, G.E., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. CoRR abs\/1503.02531 (2015)"},{"key":"29_CR8","doi-asserted-by":"crossref","unstructured":"Jiao, X., et al.: Tinybert: Distilling BERT for natural language understanding. In: EMNLP, pp. 4163\u20134174 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.372"},{"key":"29_CR9","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: ICLR (2015)"},{"key":"29_CR10","doi-asserted-by":"crossref","unstructured":"Kwon, S., Cha, M., Jung, K., Chen, W., Wang, Y.: Prominent features of rumor propagation in online social media. In: ICDM, pp. 1103\u20131108 (2013)","DOI":"10.1109\/ICDM.2013.61"},{"key":"29_CR11","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wu, Y.B.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: AAAI pp. 354\u2013361 (2018)","DOI":"10.1609\/aaai.v32i1.11268"},{"key":"29_CR12","unstructured":"Ma, J., et al.: Detecting rumors from microblogs with recurrent neural networks. In: IJCAI, pp. 3818\u20133824 (2016)"},{"key":"29_CR13","doi-asserted-by":"crossref","unstructured":"Ma, J., Gao, W., Wong, K.: Rumor detection on twitter with tree-structured recursive neural networks. In: ACL, pp. 1980\u20131989 (2018)","DOI":"10.18653\/v1\/P18-1184"},{"issue":"8","key":"29_CR14","doi-asserted-by":"publisher","first-page":"1979","DOI":"10.1109\/TPAMI.2018.2858821","volume":"41","author":"T Miyato","year":"2019","unstructured":"Miyato, T., Maeda, S., Koyama, M., Ishii, S.: Virtual adversarial training: a regularization method for supervised and semi-supervised learning. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1979\u20131993 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"29_CR15","doi-asserted-by":"crossref","unstructured":"Ren, X., Shi, R., Li, F.: Distill BERT to traditional models in Chinese machine reading comprehension (student abstract). In: AAAI, pp. 13901\u201313902 (2020)","DOI":"10.1609\/aaai.v34i10.7223"},{"key":"29_CR16","doi-asserted-by":"crossref","unstructured":"Rios, A., Kavuluru, R.: Few-shot and zero-shot multi-label learning for structured label spaces. In: EMNLP, pp. 3132\u20133142 (2018)","DOI":"10.18653\/v1\/D18-1352"},{"key":"29_CR17","doi-asserted-by":"crossref","unstructured":"Ruchansky, N., Seo, S., Liu, Y.: CSI: A hybrid deep model for fake news detection. In: CIKM, pp. 797\u2013806 (2017)","DOI":"10.1145\/3132847.3132877"},{"key":"29_CR18","unstructured":"Sanh, V., Debut, L., Chaumond, J., Wolf, T.: Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. CoRR abs\/1910.01108 (2019)"},{"key":"29_CR19","unstructured":"Shu, K., Mahudeswaran, D., Wang, S., Lee, D., Liu, H.: Fakenewsnet: a data repository with news content, social context and dynamic information for studying fake news on social media. CoRR abs\/1809.01286 (2018)"},{"issue":"1","key":"29_CR20","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1145\/3137597.3137600","volume":"19","author":"K Shu","year":"2017","unstructured":"Shu, K., Sliva, A., Wang, S., Tang, J., Liu, H.: Fake news detection on social media: a data mining perspective. SIGKDD Explor. 19(1), 22\u201336 (2017)","journal-title":"SIGKDD Explor."},{"key":"29_CR21","doi-asserted-by":"crossref","unstructured":"Sun, S., Cheng, Y., Gan, Z., Liu, J.: Patient knowledge distillation for BERT model compression. In: EMNLP-IJCNLP, pp. 4322\u20134331 (2019)","DOI":"10.18653\/v1\/D19-1441"},{"key":"29_CR22","doi-asserted-by":"crossref","unstructured":"Sung, F., Yang, Y., Zhang, L., Xiang, T., Torr, P.H.S., Hospedales, T.M.: Learning to compare: relation network for few-shot learning. In: CVPR, pp. 1199\u20131208 (2018)","DOI":"10.1109\/CVPR.2018.00131"},{"key":"29_CR23","doi-asserted-by":"crossref","unstructured":"Wang, W.Y.: \"liar, liar pants on fire\": a new benchmark dataset for fake news detection. In: ACL, pp. 422\u2013426 (2017)","DOI":"10.18653\/v1\/P17-2067"},{"key":"29_CR24","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yao, Q., Kwok, J.T., Ni, L.M.: Generalizing from a few examples: a survey on few-shot learning. ACM Comput. Surv. 53(3), 63:1\u201363:34 (2020)","DOI":"10.1145\/3386252"},{"key":"29_CR25","doi-asserted-by":"crossref","unstructured":"Wu, Y., Lin, Y., Dong, X., Yan, Y., Ouyang, W., Yang, Y.: Exploit the unknown gradually: one-shot video-based person re-identification by stepwise learning. In: CVPR, pp. 5177\u20135186 (2018)","DOI":"10.1109\/CVPR.2018.00543"},{"key":"29_CR26","doi-asserted-by":"crossref","unstructured":"Yang, F., Liu, Y., Yu, X., Yang, M.: Automatic detection of rumor on sina weibo. In: SIGKDD Workshop (2012)","DOI":"10.1145\/2350190.2350203"},{"key":"29_CR27","doi-asserted-by":"crossref","unstructured":"Yang, S., Shu, K., Wang, S., Gu, R., Wu, F., Liu, H.: Unsupervised fake news detection on social media: A generative approach. In: AAAI, pp. 5644\u20135651 (2019)","DOI":"10.1609\/aaai.v33i01.33015644"},{"key":"29_CR28","doi-asserted-by":"crossref","unstructured":"Yu, M., et al.: Diverse few-shot text classification with multiple metrics. In: NAACL-HLT, pp. 1206\u20131215 (2018)","DOI":"10.18653\/v1\/N18-1109"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46677-9_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T11:11:44Z","timestamp":1730459504000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46677-9_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031466762","9783031466779"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46677-9_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","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":"27 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2023.uqcloud.net\/","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":"Yes. Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"503","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":"216","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":"43% - 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":"2.97","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":"3.77","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)"}}]}}