{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T07:19:53Z","timestamp":1765610393503,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031440663"},{"type":"electronic","value":"9783031440670"}],"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\/978-3-031-44067-0_21","type":"book-chapter","created":{"date-parts":[[2023,10,20]],"date-time":"2023-10-20T06:02:33Z","timestamp":1697781753000},"page":"405-419","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Propaganda Detection Robustness Through Adversarial Attacks Driven by\u00a0eXplainable AI"],"prefix":"10.1007","author":[{"given":"Danilo","family":"Cavaliere","sequence":"first","affiliation":[]},{"given":"Mariacristina","family":"Gallo","sequence":"additional","affiliation":[]},{"given":"Claudio","family":"Stanzione","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"21_CR1","doi-asserted-by":"crossref","unstructured":"Bangerter, M., et al.: Unisa at SemEval-2023 task 3: a shap-based method for propaganda detection. In: Proceedings of the 17th International Workshop on Semantic Evaluation, SemEval 2023, Toronto, Canada (2023)","DOI":"10.18653\/v1\/2023.semeval-1.122"},{"key":"21_CR2","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.ins.2019.09.013","volume":"509","author":"R Campos","year":"2020","unstructured":"Campos, R., Mangaravite, V., Pasquali, A., Jorge, A., Nunes, C., Jatowt, A.: YAKE! Keyword extraction from single documents using multiple local features. Inf. Sci. 509, 257\u2013289 (2020)","journal-title":"Inf. Sci."},{"key":"21_CR3","doi-asserted-by":"publisher","first-page":"93575","DOI":"10.1109\/ACCESS.2022.3204171","volume":"10","author":"N Capuano","year":"2022","unstructured":"Capuano, N., Fenza, G., Loia, V., Stanzione, C.: Explainable artificial intelligence in cybersecurity: a survey. IEEE Access 10, 93575\u201393600 (2022)","journal-title":"IEEE Access"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Chernyavskiy, A., Ilvovsky, D., Nakov, P.: Aschern at SemEval-2020 task 11: it takes three to tango: RoBERTa, CRF, and transfer learning. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 1462\u20131468 (2020)","DOI":"10.18653\/v1\/2020.semeval-1.191"},{"issue":"2","key":"21_CR5","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1109\/MIC.2021.3130380","volume":"26","author":"S Cresci","year":"2021","unstructured":"Cresci, S., Petrocchi, M., Spognardi, A., Tognazzi, S.: Adversarial machine learning for protecting against online manipulation. IEEE Internet Comput. 26(2), 47\u201352 (2021)","journal-title":"IEEE Internet Comput."},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Da San Martino, G., Barr\u00f3n-Cede\u00f1o, A., Wachsmuth, H., Petrov, R., Nakov, P.: SemEval-2020 task 11: detection of propaganda techniques in news articles. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 1377\u20131414 (2020)","DOI":"10.18653\/v1\/2020.semeval-1.186"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Dao, J., Wang, J., Zhang, X.: YNU-HPCC at SemEval-2020 task 11: LSTM network for detection of propaganda techniques in news articles. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 1509\u20131515 (2020)","DOI":"10.18653\/v1\/2020.semeval-1.197"},{"key":"21_CR8","unstructured":"Demetrio, L., Biggio, B., Giovanni, L., Roli, F., Alessandro, A., et al.: Explaining vulnerabilities of deep learning to adversarial malware binaries. In: CEUR Workshop Proceedings, vol. 2315 (2019)"},{"key":"21_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112876","volume":"140","author":"S Duari","year":"2020","unstructured":"Duari, S., Bhatnagar, V.: Complex network based supervised keyword extractor. Expert Syst. Appl. 140, 112876 (2020)","journal-title":"Expert Syst. Appl."},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Ermurachi, V., Gifu, D.: UAIC1860 at SemEval-2020 task 11: detection of propaganda techniques in news articles. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation, pp. 1835\u20131840 (2020)","DOI":"10.18653\/v1\/2020.semeval-1.241"},{"key":"21_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2017.07.030","volume":"418","author":"E Ferrara","year":"2017","unstructured":"Ferrara, E.: Contagion dynamics of extremist propaganda in social networks. Inf. Sci. 418, 1\u201312 (2017)","journal-title":"Inf. Sci."},{"key":"21_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/978-3-030-68796-0_18","volume-title":"Pattern Recognition. ICPR International Workshops and Challenges","author":"A Galli","year":"2021","unstructured":"Galli, A., Marrone, S., Moscato, V., Sansone, C.: Reliability of eXplainable artificial intelligence in adversarial perturbation scenarios. In: Del Bimbo, A., et al. (eds.) ICPR 2021. LNCS, vol. 12663, pp. 243\u2013256. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-68796-0_18"},{"issue":"2","key":"21_CR13","first-page":"44","volume":"40","author":"D Gunning","year":"2019","unstructured":"Gunning, D., Aha, D.: DARPA\u2019s explainable artificial intelligence (XAI) program. AI Mag. 40(2), 44\u201358 (2019)","journal-title":"AI Mag."},{"key":"21_CR14","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1016\/j.procs.2020.11.022","volume":"178","author":"Y Kirill","year":"2020","unstructured":"Kirill, Y., Mihail, I.G., Sanzhar, M., Rustam, M., Olga, F., Ravil, M.: Propaganda identification using topic modelling. Procedia Comput. Sci. 178, 205\u2013212 (2020)","journal-title":"Procedia Comput. Sci."},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Li, J., Ji, S., Du, T., Li, B., Wang, T.: TextBugger: generating adversarial text against real-world applications. In: 26th Annual Network and Distributed System Security Symposium (2019)","DOI":"10.14722\/ndss.2019.23138"},{"key":"21_CR16","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"21_CR17","unstructured":"Mishra, S., Dutta, S., Long, J., Magazzeni, D.: A survey on the robustness of feature importance and counterfactual explanations. arXiv preprint arXiv:2111.00358 (2021)"},{"key":"21_CR18","unstructured":"Morrish, L.: How QAnon content endures on social media through visuals and code words (2020). http:\/\/firstdraftnews.org\/articles\/how-qanon-content-endures-on-social-media-through-visuals-and-code-words\/. Accessed 20 Apr 2023"},{"key":"21_CR19","unstructured":"Oliinyk, V.A., Vysotska, V., Burov, Y., Mykich, K., Fernandes, V.B.: Propaganda detection in text data based on NLP and machine learning. In: MoMLeT+ DS, pp. 132\u2013144 (2020)"},{"key":"21_CR20","unstructured":"Pawelczyk, M., Agarwal, C., Joshi, S., Upadhyay, S., Lakkaraju, H.: Exploring counterfactual explanations through the lens of adversarial examples: a theoretical and empirical analysis. In: International Conference on Artificial Intelligence and Statistics, pp. 4574\u20134594. PMLR (2022)"},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Polonijo, B., \u0160uman, S., \u0160imac, I.: Propaganda detection using sentiment aware ensemble deep learning. In: 2021 44th International Convention on Information, Communication and Electronic Technology (MIPRO), pp. 199\u2013204. IEEE (2021)","DOI":"10.23919\/MIPRO52101.2021.9596654"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: \u201cWhy should i trust you?\u201d Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"issue":"1","key":"21_CR23","doi-asserted-by":"publisher","first-page":"23705","DOI":"10.1038\/s41598-021-03100-6","volume":"11","author":"M Szczepa\u0144ski","year":"2021","unstructured":"Szczepa\u0144ski, M., Pawlicki, M., Kozik, R., Chora\u015b, M.: New explainability method for BERT-based model in fake news detection. Sci. Rep. 11(1), 23705 (2021)","journal-title":"Sci. Rep."},{"key":"21_CR24","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.neucom.2023.02.013","volume":"531","author":"J Wei","year":"2023","unstructured":"Wei, J., Yao, L., Meng, Q.: Self-adaptive logit balancing for deep neural network robustness: defence and detection of adversarial attacks. Neurocomputing 531, 180\u2013194 (2023)","journal-title":"Neurocomputing"},{"key":"21_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2023.103647","volume":"229","author":"W Xiang","year":"2023","unstructured":"Xiang, W., Su, H., Liu, C., Guo, Y., Zheng, S.: Improving the robustness of adversarial attacks using an affine-invariant gradient estimator. Comput. Vis. Image Underst. 229, 103647 (2023)","journal-title":"Comput. Vis. Image Underst."}],"container-title":["Communications in Computer and Information Science","Explainable Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44067-0_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,13]],"date-time":"2024-02-13T06:06:41Z","timestamp":1707804401000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44067-0_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031440663","9783031440670"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44067-0_21","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"21 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"xAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"World Conference on Explainable Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","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":"26 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"xai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/xaiworldconference.com\/","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":"220","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":"94","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":"3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}