{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:11:09Z","timestamp":1743048669616,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031746260"},{"type":"electronic","value":"9783031746277"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-74627-7_44","type":"book-chapter","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T14:00:53Z","timestamp":1735653653000},"page":"525-530","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News Without Modifying it"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1339-6983","authenticated-orcid":false,"given":"Federico","family":"Siciliano","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7969-7821","authenticated-orcid":false,"given":"Luca","family":"Maiano","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9393-5248","authenticated-orcid":false,"given":"Lorenzo","family":"Papa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6107-9611","authenticated-orcid":false,"given":"Federica","family":"Baccini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6461-1391","authenticated-orcid":false,"given":"Irene","family":"Amerini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7669-9055","authenticated-orcid":false,"given":"Fabrizio","family":"Silvestri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,1]]},"reference":[{"key":"44_CR1","doi-asserted-by":"crossref","unstructured":"Barnab\u00f2, G., et al.: FbMultiLingMisinfo: challenging large-scale multilingual benchmark for misinformation detection. In: International Joint Conference on Neural Networks (IJCNN). IEEE (2022, to appear)","DOI":"10.1109\/IJCNN55064.2022.9892739"},{"key":"44_CR2","unstructured":"Campanile, L., et al.: Vulnerabilities assessment of deep learning-based fake news checker under poisoning attacks. In: Computational Data and Social Networks, p. 385 (2021)"},{"key":"44_CR3","doi-asserted-by":"publisher","unstructured":"De, A., et al.: A transformer-based approach to multilingual fake news detection in low-resource languages. ACM Trans. Asian and Low-Resour. Lang. Inf. Process. 21(1) (2021). ISSN: 2375-4699. https:\/\/doi.org\/10.1145\/3472619","DOI":"10.1145\/3472619"},{"key":"44_CR4","doi-asserted-by":"publisher","unstructured":"Fan, J., et al.: A survey on data poisoning attacks and defenses. In: 2022 7th IEEE International Conference on Data Science in Cyberspace (DSC), pp. 48\u201355 (2022). https:\/\/doi.org\/10.1109\/DSC55868.2022.00014","DOI":"10.1109\/DSC55868.2022.00014"},{"key":"44_CR5","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.neucom.2021.04.112","volume":"459","author":"SCH Hoi","year":"2021","unstructured":"Hoi, S.C.H., et al.: Online learning: a comprehensive survey. Neurocomputing 459, 249\u2013289 (2021)","journal-title":"Neurocomputing"},{"key":"44_CR6","doi-asserted-by":"publisher","unstructured":"Horne, B.D., N\u00f8rregaard, J., Adali, S.: Robust fake news detection over time and attack. ACM Trans. Intell. Syst. Technol. 11(1) (2019). ISSN: 2157-6904. https:\/\/doi.org\/10.1145\/3363818","DOI":"10.1145\/3363818"},{"key":"44_CR7","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification (2014). arXiv: 1408.5882 [cs.CL]","DOI":"10.3115\/v1\/D14-1181"},{"key":"44_CR8","doi-asserted-by":"crossref","unstructured":"Liu, Y., Wu, Y.-F.B.: Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: 32nd AAAI Conference on Artificial Intelligence (2018)","DOI":"10.1609\/aaai.v32i1.11268"},{"key":"44_CR9","doi-asserted-by":"crossref","unstructured":"Nan, Q., et al.: MDFEND: multi-domain fake news detection. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 3343\u20133347 (2021)","DOI":"10.1145\/3459637.3482139"},{"key":"44_CR10","doi-asserted-by":"crossref","unstructured":"Nguyen, V.-H., et al.: FANG: leveraging social context for fake news detection using graph representation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management (2020)","DOI":"10.1145\/3340531.3412046"},{"key":"44_CR11","doi-asserted-by":"publisher","unstructured":"Price, K.R., Priisalu, J., Nomm, S.: Analysis of the impact of poisoned data within Twitter classification models. IFACPapersOnLine 52(19) (2019). 14th IFAC Symposium on Analysis, Design, and Evaluation of Human Machine Systems HMS 2019, pp. 175\u2013180. ISSN: 2405-8963. https:\/\/doi.org\/10.1016\/j.ifacol.2019.12.170","DOI":"10.1016\/j.ifacol.2019.12.170"},{"key":"44_CR12","unstructured":"Shu, K., et al.: FakeNewsNet: a data repository with news content, social context and dynamic information for studying fake news on social media. arXiv preprint arXiv:1809.01286 (2018)"},{"key":"44_CR13","doi-asserted-by":"publisher","unstructured":"Zhou, X., Zafarani, R.: A survey of fake news: fundamental theories, detection methods, and opportunities. ACM Comput. Surv. 53(5) (2020). ISSN: 0360-0300. https:\/\/doi.org\/10.1145\/3395046","DOI":"10.1145\/3395046"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-74627-7_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T14:16:31Z","timestamp":1735654591000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-74627-7_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031746260","9783031746277"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-74627-7_44","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"1 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}