{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,19]],"date-time":"2025-04-19T04:07:32Z","timestamp":1745035652142,"version":"3.40.4"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031785535"},{"type":"electronic","value":"9783031785542"}],"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\/978-3-031-78554-2_11","type":"book-chapter","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T14:08:37Z","timestamp":1737727717000},"page":"171-186","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["HyperSMOTE-MC: Enhancing Multiclass Bot Detection on\u00a0X Through Hypergraph-Based Resampling"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9827-0882","authenticated-orcid":false,"given":"Lulwah","family":"AlKulaib","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3675-0199","authenticated-orcid":false,"given":"Chang-Tien","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,25]]},"reference":[{"key":"11_CR1","doi-asserted-by":"crossref","unstructured":"Alkulaib, L., Lu, C.T.: Balancing the scales: hypersmote for enhanced hypergraph classification. In: Proceedings of the IEEE International Conference on Big Data (2023)","DOI":"10.1109\/BigData59044.2023.10386107"},{"key":"11_CR2","unstructured":"AlKulaib, L.A.: Twitter Bots Multiclass Classification Using Bot-Like Behavior Features. Ph.D. thesis, The George Washington University (2018)"},{"issue":"8","key":"11_CR3","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.2105\/AJPH.2018.304512","volume":"108","author":"JP Allem","year":"2018","unstructured":"Allem, J.P., Ferrara, E.: Could social bots pose a threat to public health? Am. J. Public Health 108(8), 1005 (2018)","journal-title":"Am. J. Public Health"},{"issue":"10","key":"11_CR4","doi-asserted-by":"publisher","first-page":"1378","DOI":"10.2105\/AJPH.2018.304567","volume":"108","author":"DA Broniatowski","year":"2018","unstructured":"Broniatowski, D.A., et al.: Weaponized health communication: twitter bots and Russian trolls amplify the vaccine debate. Am. J. Public Health 108(10), 1378\u20131384 (2018)","journal-title":"Am. J. Public Health"},{"key":"11_CR5","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":"11_CR6","doi-asserted-by":"crossref","unstructured":"Chu, Z., Gianvecchio, S., Wang, H., Jajodia, S.: Who is tweeting on twitter: human, bot, or cyborg? In: Proceedings of the 26th Annual Computer Security Applications Conference, pp. 21\u201330 (2010)","DOI":"10.1145\/1920261.1920265"},{"key":"11_CR7","doi-asserted-by":"crossref","unstructured":"Davis, C.A., Varol, O., Ferrara, E., Flammini, A., Menczer, F.: Botornot: a system to evaluate social bots. In: Proceedings of the 25th International Conference Companion on World Wide Web, pp. 273\u2013274 (2016)","DOI":"10.1145\/2872518.2889302"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Feng, S., Wan, H., Wang, N., Li, J., Luo, M.: Twibot-20: a comprehensive twitter bot detection benchmark. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management (2021)","DOI":"10.1145\/3459637.3482019"},{"key":"11_CR9","doi-asserted-by":"crossref","unstructured":"Feng, S., Wan, H., Wang, N., Luo, M.: Botrgcn: twitter bot detection with relational graph convolutional networks. In: Proceedings of the 2021 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 236\u2013239 (2021)","DOI":"10.1145\/3487351.3488336"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 3558\u20133565 (2019)","DOI":"10.1609\/aaai.v33i01.33013558"},{"issue":"7","key":"11_CR11","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1145\/2818717","volume":"59","author":"E Ferrara","year":"2016","unstructured":"Ferrara, E., Varol, O., Davis, C., Menczer, F., Flammini, A.: The rise of social bots. Commun. ACM 59(7), 96\u2013104 (2016)","journal-title":"Commun. ACM"},{"key":"11_CR12","doi-asserted-by":"crossref","unstructured":"He, H., Bai, Y., Garcia, E.A., Li, S.: Adasyn: adaptive synthetic sampling approach for imbalanced learning. In: IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), pp. 1322\u20131328. IEEE (2008)","DOI":"10.1109\/IJCNN.2008.4633969"},{"key":"11_CR13","doi-asserted-by":"publisher","unstructured":"Heidari, M., Jones, J.H.J., Uzuner, O.: An empirical study of machine learning algorithms for social media bot detection. In: 2021 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS), pp.\u00a01\u20135 (2021). https:\/\/doi.org\/10.1109\/IEMTRONICS52119.2021.9422605","DOI":"10.1109\/IEMTRONICS52119.2021.9422605"},{"issue":"2","key":"11_CR14","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1109\/TCSS.2021.3103515","volume":"9","author":"T Khaund","year":"2022","unstructured":"Khaund, T., Kirdemir, B., Agarwal, N., Liu, H., Morstatter, F.: Social bots and their coordination during online campaigns: a survey. IEEE Trans. Comput. Soc. Syst. 9(2), 530\u2013545 (2022). https:\/\/doi.org\/10.1109\/TCSS.2021.3103515","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"11_CR15","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1016\/j.ins.2018.08.019","volume":"467","author":"S Kudugunta","year":"2018","unstructured":"Kudugunta, S., Ferrara, E.: Deep neural networks for bot detection. Inf. Sci. 467, 312\u2013322 (2018)","journal-title":"Inf. Sci."},{"issue":"11","key":"11_CR16","doi-asserted-by":"publisher","first-page":"1799","DOI":"10.3390\/math10111799","volume":"10","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Zhang, Z., Liu, Y., Zhu, Y.: Gatsmote: improving imbalanced node classification on graphs via attention and homophily. Mathematics 10(11), 1799 (2022)","journal-title":"Mathematics"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Qi, S., AlKulaib, L., Broniatowski, D.A.: Detecting and characterizing bot-like behavior on twitter. In: Social, Cultural, and Behavioral Modeling: 11th International Conference, SBP-BRiMS 2018, Washington, DC, USA, 10\u201313 July 2018, Proceedings 11, pp. 228\u2013232. Springer (2018)","DOI":"10.1007\/978-3-319-93372-6_26"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Sleeman\u00a0IV, W.C., Krawczyk, B.: Multi-class imbalanced big data classification on spark. Knowl.-Based Syst. 106598 (2020)","DOI":"10.1016\/j.knosys.2020.106598"},{"key":"11_CR19","unstructured":"Weeks, B.E., Ard\u00e8vol-Abreu, A., Gil\u00a0de Z\u00fa\u00f1iga, H.: Online influence? Social media use, opinion leadership, and political persuasion. Int. J. Public Opin. Res. 29(2), 214\u2013239 (2017)"},{"key":"11_CR20","doi-asserted-by":"publisher","unstructured":"Wu, J., Teng, E., Cao, Z.: Twitter bot detection through unsupervised machine learning. In: 2022 IEEE International Conference on Big Data (Big Data), pp. 5833\u20135839 (2022). https:\/\/doi.org\/10.1109\/BigData55660.2022.10020983","DOI":"10.1109\/BigData55660.2022.10020983"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Zhao, T., Zhang, X., Wang, S.: Graphsmote: imbalanced node classification on graphs with graph neural networks. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 833\u2013841 (2021)","DOI":"10.1145\/3437963.3441720"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Zhou, D., Huang, J., Sch\u00f6lkopf, B.: Learning with hypergraphs: clustering, classification, and embedding. Adv. Neural Inf. Process. Syst. 19 (2006)","DOI":"10.7551\/mitpress\/7503.003.0205"}],"container-title":["Lecture Notes in Computer Science","Social Networks Analysis and Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78554-2_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,18]],"date-time":"2025-04-18T15:07:40Z","timestamp":1744988860000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78554-2_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031785535","9783031785542"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78554-2_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ASONAM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advances in Social Networks Analysis and Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rende","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 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":"asonam-12024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/asonam.cpsc.ucalgary.ca\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}