{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T15:46:04Z","timestamp":1742917564407,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031635427"},{"type":"electronic","value":"9783031635434"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-63543-4_15","type":"book-chapter","created":{"date-parts":[[2024,6,22]],"date-time":"2024-06-22T23:02:09Z","timestamp":1719097329000},"page":"227-236","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Comparative Analysis of Various Data Balancing Techniques for Propaganda Detection in Lithuanian News Articles"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3211-5126","authenticated-orcid":false,"given":"Ieva","family":"Rizgelien\u0117","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1931-6852","authenticated-orcid":false,"given":"Gra\u017eina","family":"Korvel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,6,23]]},"reference":[{"key":"15_CR1","unstructured":"Prier, J.: Commanding the trend: social media as information warfare. Strat. Stud. Q. 11(4), 50\u201385 (2017). http:\/\/www.jstor.org\/stable\/26271634"},{"key":"15_CR2","doi-asserted-by":"publisher","first-page":"107050","DOI":"10.1016\/j.asoc.2020.107050","volume":"101","author":"M Chora\u015b","year":"2021","unstructured":"Chora\u015b, M., et al.: Advanced Machine Learning techniques for fake news (online disinformation) detection: a systematic mapping study. Appl. Soft Comput. 101, 107050 (2021)","journal-title":"Appl. Soft Comput."},{"key":"15_CR3","first-page":"115","volume":"13","author":"AMUD Khanday","year":"2021","unstructured":"Khanday, A.M.U.D., Khan, Q.R., Rabani, S.T.: Identifying propaganda from online social networks during COVID-19 using machine learning techniques. Int. J. Inf. Technol. 13, 115\u2013122 (2021)","journal-title":"Int. J. Inf. Technol."},{"key":"15_CR4","doi-asserted-by":"crossref","unstructured":"Barrows, M., Haig, E., Conduit, D.: Sentiment and objectivity in Iranian state-sponsored propaganda on twitter. IEEE Trans. Comput. Soc. Syst. (2023)","DOI":"10.1109\/TCSS.2023.3273729"},{"key":"15_CR5","doi-asserted-by":"crossref","unstructured":"Killi, C.B.R., Balakrishnan, N., Rao, C.S.: Deep fake image classification using VGG-19 model. Ing\u00e9nierie des Syst\u00e8mes d'Information 28(2) (2023)","DOI":"10.18280\/isi.280228"},{"issue":"13","key":"15_CR6","doi-asserted-by":"publisher","first-page":"20101","DOI":"10.1007\/s11042-022-14307-8","volume":"82","author":"SK Panda","year":"2023","unstructured":"Panda, S.K., Diwan, T., Kakde, O.G., Tembhurne, J.V.: Improvised detection of deepfakes from visual inputs using light weight deep ensemble model. Multimedia Tools Appl. 82(13), 20101\u201320118 (2023)","journal-title":"Multimedia Tools Appl."},{"key":"15_CR7","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/978-3-030-99987-2_7","volume-title":"Information Wars in the Baltic States: Russia\u2019s Long Shadow","author":"A Zelenkauskaite","year":"2022","unstructured":"Zelenkauskaite, A.: Bots, trolls, elves, and the information war in Lithuania: theoretical considerations and practical problems. In: Information Wars in the Baltic States: Russia\u2019s Long Shadow, pp. 123\u2013140. Springer International Publishing, Cham (2022)"},{"key":"15_CR8","unstructured":"Kasperien\u0117, R., Krilavi\u010dius, T.: Content analysis methods for estimating the dynamics of Facebook groups. In: CEUR Workshop Proceedings [Electronic Resource]: IVUS 2019, International conference on information technologies, Kaunas, Lithuania, 25 Apr 2019. Aachen: CEUR-WS, 2019, vol. 2470. CEUR-WS, Aachen (2019)"},{"key":"15_CR9","doi-asserted-by":"crossref","unstructured":"Ruzait\u0117, J.: How Do Haters Hate? Verbal Aggression in Lithuanian Online Comments. Discourse and Conflict: Analysing Text and Talk of Conflict, Hate and Peace-Building, 115\u2013145 (2021)","DOI":"10.1007\/978-3-030-76485-2_5"},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Kankevi\u010di\u016bt\u0117, E., Songailait\u0117, M., Zhyhun, B., Mandravickait\u0117, J.: Lithuanian hate speech classification using deep learning methods. Autom. Technol. Bus. Process.\/Avtomatizaci\u00e2 Tehnologiceskih i Biznes-Processov 15(3) (2023)","DOI":"10.15673\/atbp.v15i3.2621"},{"key":"15_CR11","unstructured":"Kankevi\u010di\u016bt\u0117, E., Songailait\u0117, M., Mandravickait\u0117, J., Kalinauskait\u0117, D., Krilavi\u010dius, T.: A comparison of deep learning models for hate speech detection (2022)"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Petrauskait\u0117, R., Amilevi\u010dius, D., Dadurkevi\u010dius, V., Krilavi\u010dius, T., Ra\u0161kinis, G., Utka, A., Vai\u010denonien\u0117, J.: CLARIN-LT: Home for Lithuanian Language Resources. CLARIN. The Infrastructure for Language Resources. deGruyter, Berlin (2022)","DOI":"10.1515\/9783110767377-020"},{"key":"15_CR13","doi-asserted-by":"publisher","first-page":"117605","DOI":"10.1016\/j.eswa.2022.117605","volume":"205","author":"Z Feng","year":"2022","unstructured":"Feng, Z., Zhou, H., Zhu, Z., Mao, K.: Tailored text augmentation for sentiment analysis. Expert Syst. Appl. 205, 117605 (2022)","journal-title":"Expert Syst. Appl."},{"key":"15_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-021-00492-0","volume":"8","author":"C Shorten","year":"2021","unstructured":"Shorten, C., Khoshgoftaar, T.M., Furht, B.: Text data augmentation for deep learning. J. Big Data 8, 1\u201334 (2021)","journal-title":"J. Big Data"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Liu, R., Xu, G., Jia, C., Ma, W., Wang, L., Vosoughi, S.: Data boost: text data augmentation through reinforcement learning guided conditional generation. arXiv preprint arXiv:2012.02952 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.726"},{"issue":"5","key":"15_CR16","doi-asserted-by":"publisher","first-page":"5291","DOI":"10.1007\/s11227-022-04851-3","volume":"79","author":"M Madani","year":"2023","unstructured":"Madani, M., Motameni, H., Mohamadi, H.: KNNGAN: an oversampling technique for textual imbalanced datasets. J. Supercomput. 79(5), 5291\u20135326 (2023)","journal-title":"J. Supercomput."},{"key":"15_CR17","doi-asserted-by":"crossref","unstructured":"Prusa, J., Khoshgoftaar, T.M., Dittman, D.J., Napolitano, A.: Using random undersampling to alleviate class imbalance on tweet sentiment data. In: 2015 IEEE International Conference on Information Reuse and Integration, pp. 197\u2013202. IEEE (2015)","DOI":"10.1109\/IRI.2015.39"},{"key":"15_CR18","doi-asserted-by":"publisher","unstructured":"He, H., Bai, Y., Garcia, E.A., Li, S.: ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), Hong Kong, 2008, pp. 1322\u20131328. https:\/\/doi.org\/10.1109\/IJCNN.2008.4633969","DOI":"10.1109\/IJCNN.2008.4633969"},{"key":"15_CR19","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"15_CR20","doi-asserted-by":"crossref","unstructured":"Linzen, T.: How can we accelerate progress towards human-like linguistic generalization? arXiv preprint arXiv:2005.00955 (2020)","DOI":"10.18653\/v1\/2020.acl-main.465"},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Qiao, F., Peng, X.: Uncertainty-guided model generalization to unseen domains. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6790\u20136800 (2021)","DOI":"10.1109\/CVPR46437.2021.00672"},{"key":"15_CR22","doi-asserted-by":"publisher","unstructured":"Batista, G.E.A.P.A., Prati, R.C., Monard, M.C.: A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explor. Newsl. 6(1), 20\u201329 (2004). https:\/\/doi.org\/10.1145\/1007730.1007735","DOI":"10.1145\/1007730.1007735"},{"issue":"04","key":"15_CR23","first-page":"104","volume":"7","author":"M Beckmann","year":"2015","unstructured":"Beckmann, M., Ebecken, N.F., Pires de Lima, B.S.: A KNN undersampling approach for data balancing. J. Intell. Learn. Syst. Appl. 7(04), 104\u2013116 (2015)","journal-title":"J. Intell. Learn. Syst. Appl."},{"key":"15_CR24","first-page":"769","volume":"6","author":"I Tomek","year":"1976","unstructured":"Tomek, I.: Two modifications of CNN. IEEE Trans. Syst. Man Cybern. 6, 769\u2013772 (1976)","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"15_CR25","doi-asserted-by":"crossref","unstructured":"LaValley, M.P.: Logistic regression. Circulation 117(18), 2395\u20132399 (2008)","DOI":"10.1161\/CIRCULATIONAHA.106.682658"},{"key":"15_CR26","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: Xgboost: a scalable tree boosting system. In: Proceedings of the 22nd International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794 (2016)","DOI":"10.1145\/2939672.2939785"},{"issue":"827","key":"15_CR27","first-page":"188","volume":"117","author":"MO Stitson","year":"1996","unstructured":"Stitson, M.O., Weston, J.A.E., Gammerman, A., Vovk, V., Vapnik, V.: Theory of support vector machines. Univ. London 117(827), 188\u2013191 (1996)","journal-title":"Univ. London"},{"key":"15_CR28","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45, 5\u201332 (2001). https:\/\/doi.org\/10.1023\/A:1010933404324","journal-title":"Mach. Learn."},{"key":"15_CR29","unstructured":"LRT tyrimas. Lietuvos \u201epenktoji kolona\u201c: Rusijos propagand\u0105 platina \u0161eimos gyn\u0117jai, sektos ir knyg\u0173 apie Stalin\u0105 leid\u0117jai - LRT"},{"key":"15_CR30","unstructured":"Stollenwerk, F., et al.: Text Annotation Handbook: A Practical Guide for Machine Learning Projects (2023). arXiv:2310.11780"},{"key":"15_CR31","unstructured":"https:\/\/propaganda.qcri.org\/annotations\/definitions.html"}],"container-title":["Communications in Computer and Information Science","Digital Business and Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-63543-4_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,22]],"date-time":"2024-06-22T23:04:08Z","timestamp":1719097448000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-63543-4_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031635427","9783031635434"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-63543-4_15","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"23 June 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DB&IS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Baltic Conference on Digital Business and Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dbis2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dbis2024.vu.lt","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}