{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T01:58:15Z","timestamp":1776391095907,"version":"3.51.2"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030908874","type":"print"},{"value":"9783030908881","type":"electronic"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-90888-1_26","type":"book-chapter","created":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T14:13:49Z","timestamp":1638454429000},"page":"339-354","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Controversy Detection: A Text and Graph Neural Network Based Approach"],"prefix":"10.1007","author":[{"given":"Samy","family":"Benslimane","sequence":"first","affiliation":[]},{"given":"J\u00e9rome","family":"Az\u00e9","sequence":"additional","affiliation":[]},{"given":"Sandra","family":"Bringay","sequence":"additional","affiliation":[]},{"given":"Maximilien","family":"Servajean","sequence":"additional","affiliation":[]},{"given":"Caroline","family":"Mollevi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"26_CR1","doi-asserted-by":"crossref","unstructured":"Beelen, K., Kanoulas, E., van de Velde, B.: Detecting controversies in online news media. In: 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1069\u20131072 (2017)","DOI":"10.1145\/3077136.3080723"},{"key":"26_CR2","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL-HLT Conference: Human Language Technologies, vol. 1, pp. 4171\u20134186 (2019)"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Dori-Hacohen, S., Jensen, D.D., Allan, J.: Controversy detection in wikipedia using collective classification. In: 39th International ACM SIGIR conference on Research and Development in Information Retrieval, pp. 797\u2013800 (2016)","DOI":"10.1145\/2911451.2914745"},{"issue":"1","key":"26_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13278-020-00703-1","volume":"10","author":"H Emamgholizadeh","year":"2020","unstructured":"Emamgholizadeh, H., Nourizade, M., Tajbakhsh, M.S., Hashminezhad, M., Esfahani, F.N.: A framework for quantifying controversy of social network debates using attributed networks: biased random walk (BRW). Soc. Netw. Anal. Mining 10(1), 1\u201320 (2020). https:\/\/doi.org\/10.1007\/s13278-020-00703-1","journal-title":"Soc. Netw. Anal. Mining"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Garimella, K., Morales, G.D.F., Gionis, A., Mathioudakis, M.: Quantifying controversy on social media. ACM Trans. Soc. Comput. 1(1), 3:1\u20133:27 (2018)","DOI":"10.1145\/3140565"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Garimella, K., Morales, G.D.F., Gionis, A., Mathioudakis, M.: Reducing controversy by connecting opposing views. In: Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI, pp. 5249\u20135253 (2018)","DOI":"10.24963\/ijcai.2018\/731"},{"key":"26_CR7","unstructured":"Guerra, P.H.C., Jr., W.M., Cardie, C., Kleinberg, R.: A measure of polarization on social media networks based on community boundaries. In: Seventh International Conference on Weblogs and Social Media, ICWSM. The AAAI Press (2013)"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Hessel, J., Lee, L.: Something\u2019s brewing! early prediction of controversy-causing posts from discussion features. In: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT, pp. 1648\u20131659 (2019)","DOI":"10.18653\/v1\/N19-1166"},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"Jang, M., Allan, J.: Improving automated controversy detection on the web. In: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, SIGIR, pp. 865\u2013868. ACM (2016)","DOI":"10.1145\/2911451.2914764"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Jang, M., Dori-Hacohen, S., Allan, J.: Modeling controversy within populations. In: Proceedings of the SIGIR International Conference on Theory of Information Retrieval, ICTIR, pp. 141\u2013149. ACM (2017)","DOI":"10.1145\/3121050.3121067"},{"key":"26_CR11","doi-asserted-by":"crossref","unstructured":"Jang, M., Foley, J., Dori-Hacohen, S., Allan, J.: Probabilistic approaches to controversy detection. In: 25th ACM International Conference on Information and Knowledge Management, CIKM, pp. 2069\u20132072 (2016)","DOI":"10.1145\/2983323.2983911"},{"key":"26_CR12","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR. OpenReview.net (2017)"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Mendoza, M., Parra, D., Soto, \u00c1.: GENE: graph generation conditioned on named entities for polarity and controversy detection in social media. Inf. Process. Manag. 57(6), 102366 (2020)","DOI":"10.1016\/j.ipm.2020.102366"},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Morales, A.J., Borondo, J., Losada, J.C., Benito, R.M.: Measuring political polarization: Twitter shows the two sides of venezuela. CoRR (2015)","DOI":"10.1063\/1.4913758"},{"key":"26_CR15","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1007\/978-3-030-32381-3_16","volume-title":"Chinese Computational Linguistics","author":"C Sun","year":"2019","unstructured":"Sun, C., Qiu, X., Xu, Y., Huang, X.: How to fine-tune BERT for text classification? In: Sun, M., Huang, X., Ji, H., Liu, Z., Liu, Y. (eds.) CCL 2019. LNCS (LNAI), vol. 11856, pp. 194\u2013206. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-32381-3_16"},{"key":"26_CR16","unstructured":"Sznajder, B., et al.: Controversy in context. CoRR (2019)"},{"key":"26_CR17","unstructured":"Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: 6th International Conference on Learning Representations, ICLR. OpenReview.net (2018)"},{"key":"26_CR18","unstructured":"Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W.L., Leskovec, J.: Hierarchical graph representation learning with differentiable pooling. In: Annual Conference on Neural Information Processing Systems, NeurIPS, pp. 4805\u20134815 (2018)"},{"key":"26_CR19","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/978-3-030-57855-8_12","volume-title":"Ontologies and Concepts in Mind and Machine","author":"JM Ortiz de Zarate","year":"2020","unstructured":"Ortiz de Zarate, J.M., Feuerstein, E.: Vocabulary-based method for quantifying controversy in social media. In: Alam, M., Braun, T., Yun, B. (eds.) ICCS 2020. LNCS (LNAI), vol. 12277, pp. 161\u2013176. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-57855-8_12"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Zhang, S., Xie, L.: Improving attention mechanism in graph neural networks via cardinality preservation. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pp. 1395\u20131402 (2020). ijcai.org","DOI":"10.24963\/ijcai.2020\/194"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Zhong, L., Cao, J., Sheng, Q., Guo, J., Wang, Z.: Integrating semantic and structural information with graph convolutional network for controversy detection. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL, pp. 515\u2013526. Association for Computational Linguistics (2020)","DOI":"10.18653\/v1\/2020.acl-main.49"}],"container-title":["Lecture Notes in Computer Science","Web Information Systems Engineering \u2013 WISE 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-90888-1_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,2]],"date-time":"2021-12-02T14:24:28Z","timestamp":1638455068000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-90888-1_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030908874","9783030908881"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-90888-1_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WISE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Web Information Systems Engineering","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Melbourne, VIC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Australia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"wise2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.wise-conferences.org\/2021\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"229","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":"55","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":"29","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":"24% - 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":"4","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}