{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T06:49:14Z","timestamp":1773470954109,"version":"3.50.1"},"publisher-location":"Cham","reference-count":51,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030602758","type":"print"},{"value":"9783030602765","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-60276-5_2","type":"book-chapter","created":{"date-parts":[[2020,10,4]],"date-time":"2020-10-04T07:02:44Z","timestamp":1601794964000},"page":"13-21","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Hate Speech Detection Using Transformer Ensembles on the HASOC Dataset"],"prefix":"10.1007","author":[{"given":"Pedro","family":"Alonso","sequence":"first","affiliation":[]},{"given":"Rajkumar","family":"Saini","sequence":"additional","affiliation":[]},{"given":"Gy\u00f6rgy","family":"Kov\u00e1cs","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,29]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Badjatiya, P., Gupta, S., Gupta, M., Varma, V.: Deep learning for hate speech detection in tweets. In: Proceedings of the 26th International Conference on World Wide Web Companion, WWW \u201917 Companion, pp. 759\u2013760 (2017)","DOI":"10.1145\/3041021.3054223"},{"key":"2_CR2","doi-asserted-by":"publisher","unstructured":"Barendt, E.: What is the harm of hate speech? Ethic theory, moral prac., vol. 22 (2019). https:\/\/doi.org\/10.1007\/s10677-019-10002-0","DOI":"10.1007\/s10677-019-10002-0"},{"key":"2_CR3","doi-asserted-by":"publisher","unstructured":"Basile, V., et al.: SemEval-2019 task 5: multilingual detection of hate speech against immigrants and women in Twitter. In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 54\u201363 (2019). https:\/\/doi.org\/10.18653\/v1\/S19-2007","DOI":"10.18653\/v1\/S19-2007"},{"issue":"3","key":"2_CR4","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1177\/1468796817709846","volume":"18","author":"A Brown","year":"2018","unstructured":"Brown, A.: What is so special about online (as compared to offline) hate speech? Ethnicities 18(3), 297\u2013326 (2018). https:\/\/doi.org\/10.1177\/1468796817709846","journal-title":"Ethnicities"},{"issue":"2","key":"2_CR5","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1002\/poi3.85","volume":"7","author":"P Burnap","year":"2015","unstructured":"Burnap, P., Williams, M.L.: Cyber hate speech on twitter: an application of machine classification and statistical modeling for policy and decision making. Policy Internet 7(2), 223\u2013242 (2015). https:\/\/doi.org\/10.1002\/poi3.85","journal-title":"Policy Internet"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Davidson, T., Warmsley, D., Macy, M., Weber, I.: Automated hate speech detection and the problem of offensive language. In: Proceedings of the 11th International AAAI Conference on Web and Social Media, ICWSM 2017, pp. 512\u2013515 (2017)","DOI":"10.1609\/icwsm.v11i1.14955"},{"key":"2_CR7","unstructured":"Del Vigna, F., Cimino, A., Dell\u2019Orletta, F., Petrocchi, M., Tesconi, M.: Hate me, hate me not: Hate speech detection on Facebook. In: ITASEC, January 2017"},{"key":"2_CR8","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171\u20134186. Association for Computational Linguistics, Minneapolis, Minnesota, June 2019. https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"2_CR9","doi-asserted-by":"publisher","unstructured":"Djuric, N., Zhou, J., Morris, R., Grbovic, M., Radosavljevic, V., Bhamidipati, N.: Hate speech detection with comment embeddings. In: Proceedings of the 24th International Conference on World Wide Web, WWW 2015 Companion, pp. 29\u201330. Association for Computing Machinery, New York (2015). https:\/\/doi.org\/10.1145\/2740908.2742760","DOI":"10.1145\/2740908.2742760"},{"key":"2_CR10","unstructured":"Do, H.T.T., Huynh, H.D., Nguyen, K.V., Nguyen, N.L.T., Nguyen, A.G.T.: Hate speech detection on vietnamese social media text using the bidirectional-LSTM model (2019), arXiv:1911.03648"},{"issue":"1","key":"2_CR11","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1080\/03064220500532412","volume":"35","author":"R Dworkin","year":"2006","unstructured":"Dworkin, R.: A new map of censorship. Index Censorship 35(1), 130\u2013133 (2006). https:\/\/doi.org\/10.1080\/03064220500532412","journal-title":"Index Censorship"},{"key":"2_CR12","doi-asserted-by":"publisher","unstructured":"Gamb\u00e4ck, B., Sikdar, U.K.: Using convolutional neural networks to classify hate-speech. In: Proceedings of the First Workshop on Abusive Language Online, pp. 85\u201390. Association for Computational Linguistics, Vancouver, BC, Canada, August 2017. https:\/\/doi.org\/10.18653\/v1\/W17-3013 . https:\/\/www.aclweb.org\/anthology\/W17-3013","DOI":"10.18653\/v1\/W17-3013"},{"key":"2_CR13","doi-asserted-by":"publisher","unstructured":"Greevy, E., Smeaton, A.F.: Classifying racist texts using a support vector machine. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2004, pp. 468\u2013469. Association for Computing Machinery, New York (2004). https:\/\/doi.org\/10.1145\/1008992.1009074","DOI":"10.1145\/1008992.1009074"},{"key":"2_CR14","doi-asserted-by":"publisher","unstructured":"Gr\u00f6ndahl, T., Pajola, L., Juuti, M., Conti, M., Asokan, N.: All you need is \u201clove\u201d: evading hate speech detection. In: Proceedings of the 11th ACM Workshop on Artificial Intelligence and Security, AISec 2018, pp. 2\u201312. Association for Computing Machinery, New York (2018). https:\/\/doi.org\/10.1145\/3270101.3270103","DOI":"10.1145\/3270101.3270103"},{"key":"2_CR15","unstructured":"Hern, A.: Revealed: catastrophic effects of working as a Facebook moderator. The Guardian (2019). https:\/\/www.theguardian.com\/technology\/2019\/sep\/17\/revealed-catastrophic-effects-working-facebook-moderator . Accessed 26 Apr 2020"},{"key":"2_CR16","doi-asserted-by":"publisher","unstructured":"Heyman, S.: Hate speech, public discourse, and the first amendment. In: Hare, I., Weinstein, J. (eds.) Extreme Speech and Democracy. Oxford Scholarship Online (2009). https:\/\/doi.org\/10.1093\/acprof:oso\/9780199548781.003.0010","DOI":"10.1093\/acprof:oso\/9780199548781.003.0010"},{"key":"2_CR17","unstructured":"Huynh, T.V., Nguyen, V.D., Nguyen, K.V., Nguyen, N.L.T., Nguyen, A.G.T.: Hate speech detection on Vietnamese social media text using the bi-gru-lstm-cnn model. arXiv:1911.03644 (2019)"},{"key":"2_CR18","unstructured":"Immpermium: detecting insults in social commentary. https:\/\/kaggle.com\/c\/detecting-insults-in-social-commentary . Accessed 27 April 2020"},{"key":"2_CR19","doi-asserted-by":"crossref","unstructured":"Kwok, I., Wang, Y.: Locate the hate: detecting tweets against blacks. In: Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2013, pp. 1621\u20131622. AAAI Press (2013)","DOI":"10.1609\/aaai.v27i1.8539"},{"key":"2_CR20","unstructured":"Liu, Y., et al.: Roberta: A robustly optimized bert pretraining approach (2019)"},{"key":"2_CR21","doi-asserted-by":"publisher","unstructured":"MacAvaney, S., Yao, H.R., Yang, E., Russell, K., Goharian, N., Frieder, O.: Hate speech detection: Challenges and solutions. PLOS ONE 14(8), 1\u201316 (2019). https:\/\/doi.org\/10.1371\/journal.pone.0221152","DOI":"10.1371\/journal.pone.0221152"},{"key":"2_CR22","unstructured":"Mandl, T., Modha, S., Mandlia, C., Patel, D., Patel, A., Dave, M.: HASOC - Hate Speech and Offensive Content identification in indo-European languages. https:\/\/hasoc2019.github.io . Accessed 20 Sep 2019"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Mandl, T., Modha, S., Patel, D., Dave, M., Mandlia, C., Patel, A.: Overview of the HASOC track at FIRE 2019: Hate Speech and Offensive Content Identification in Indo-European Languages). In: Proceedings of the 11th Annual Meeting of the Forum for Information Retrieval Evaluation, December 2019","DOI":"10.1145\/3368567.3368584"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Matsuda, M.J.: Public response to racist spech: considering the victim\u2019s story. In: Matsuda, M.J., Lawrence III, C.R. (ed.) Words that Wound: Critical Race Theory, Assaultive Speech, and the First Amendment, pp. 17\u201352. Routledge, New York (1993)","DOI":"10.4324\/9780429502941-2"},{"key":"2_CR25","doi-asserted-by":"publisher","unstructured":"Mehdad, Y., Tetreault, J.: Do characters abuse more than words? In: Proceedings of the SIGDIAL2016 Conference, pp. 299\u2013303, January 2016. https:\/\/doi.org\/10.18653\/v1\/W16-3638","DOI":"10.18653\/v1\/W16-3638"},{"key":"2_CR26","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the NIPS, pp. 3111\u20133119 (2013)"},{"key":"2_CR27","doi-asserted-by":"publisher","unstructured":"Mondal, M., Silva, L.A., Benevenuto, F.: A measurement study of hate speech in social media. In: Proceedings of the 28th ACM Conference on Hypertext and Social Media, HT 2017, pp. 85\u201394. Association for Computing Machinery, New York (2017). https:\/\/doi.org\/10.1145\/3078714.3078723","DOI":"10.1145\/3078714.3078723"},{"key":"2_CR28","unstructured":"Nina-Alcocer, V.: Vito at HASOC 2019: Detecting hate speech and offensive content through ensembles. In: Mehta, P., Rosso, P., Majumder, P., Mitra, M. (eds.) Working Notes of FIRE 2019 - Forum for Information Retrieval Evaluation, Kolkata, India, 12\u201315 December, 2019. CEUR Workshop Proceedings, vol. 2517, pp. 214\u2013220. CEUR-WS.org (2019). http:\/\/ceur-ws.org\/Vol-2517\/T3-5.pdf"},{"key":"2_CR29","doi-asserted-by":"publisher","unstructured":"Njagi, D., Zuping, Z., Hanyurwimfura, D., Long, J.: A lexicon-based approach for hate speech detection. Int. J. Multimed. Ubiquitous Eng. 10, 215\u2013230 (2015). https:\/\/doi.org\/10.14257\/ijmue.2015.10.4.21","DOI":"10.14257\/ijmue.2015.10.4.21"},{"key":"2_CR30","doi-asserted-by":"publisher","unstructured":"Nourbakhsh, A., Vermeer, F., Wiltvank, G., van der Goot, R.: sthruggle at SemEval-2019 task 5: an ensemble approach to hate speech detection. In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 484\u2013488. Association for Computational Linguistics, Minneapolis, Minnesota, June 2019. https:\/\/doi.org\/10.18653\/v1\/S19-2086","DOI":"10.18653\/v1\/S19-2086"},{"key":"2_CR31","doi-asserted-by":"crossref","unstructured":"Otter, D.W., Medina, J.R., Kalita, J.K.: A survey of the usages of deep learning for natural language processing. IEEE Trans. Neural Networks Learn. Syst., 1\u201321 (2020)","DOI":"10.1109\/TNNLS.2020.2979670"},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Park, J., Fung, P.: One-step and two-step classification for abusive language detection on Twitter. In: ALW1: 1st Workshop on Abusive Language Online, June 2017","DOI":"10.18653\/v1\/W17-3006"},{"key":"2_CR33","unstructured":"Alonso, P., Rajkumar Saini, G.K.: The North at HASOC 2019 hate speech detection in social media data. In: Proceedings of the 11th Anual Meeting of the Forum for Information Retrieval Evaluation, December 2019"},{"key":"2_CR34","doi-asserted-by":"publisher","unstructured":"Ross, B., Rist, M., Carbonell, G., Cabrera, B., Kurowsky, N., Wojatzki, M.: Measuring the reliability of hate speech annotations: the case of the European refugee crisis. In: Bei\u00dfwenger, M., Wojatzki, M., Zesch, T. (eds.) Proceedings of NLP4CMC III: 3rd Workshop on Natural Language Processing for Computer-Mediated Communication, pp. 6\u20139, September 2016. https:\/\/doi.org\/10.17185\/duepublico\/42132","DOI":"10.17185\/duepublico\/42132"},{"key":"2_CR35","doi-asserted-by":"crossref","unstructured":"Seganti, A., Sobol, H., Orlova, I., Kim, H., Staniszewski, J., Krumholc, T., Koziel, K.: Nlpr@srpol at semeval-2019 task 6 and task 5: linguistically enhanced deep learning offensive sentence classifier. In: SemEval@NAACL-HLT (2019)","DOI":"10.18653\/v1\/S19-2126"},{"key":"2_CR36","unstructured":"Spertus, E.: Smokey: Automatic recognition of hostile messages. In: Proceedings of the 14th National Conference on Artificial Intelligence and 9th Innovative Applications of Artificial Intelligence Conference (AAAI-97\/IAAI-97), pp. 1058\u20131065. AAAI Press, Menlo Park (1997. http:\/\/www.ai.mit.edu\/people\/ellens\/smokey.ps"},{"key":"2_CR37","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":"2_CR38","unstructured":"Wang, B., Ding, Y., Liu, S., Zhou, X.: Ynu$$\\_$$wb at HASOC 2019: Ordered neurons LSTM with attention for identifying hate speech and offensive language. In: Mehta, P., Rosso, P., Majumder, P., Mitra, M. (eds.) Working Notes of FIRE 2019 - Forum for Information Retrieval Evaluation, Kolkata, India, 12\u201315 December, 2019, pp. 191\u2013198 (2019). http:\/\/ceur-ws.org\/Vol-2517\/T3-2.pdf"},{"key":"2_CR39","unstructured":"Warner, W., Hirschberg, J.: Detecting hate speech on the world wide web. In: Proceedings of the Second Workshop on Language in Social Media, pp. 19\u201326. Association for Computational Linguistics, Montr\u00e9al, Canada, June 2012. https:\/\/www.aclweb.org\/anthology\/W12-2103"},{"key":"2_CR40","doi-asserted-by":"publisher","unstructured":"Waseem, Z., Hovy, D.: Hateful symbols or hateful people? predictive features for hate speech detection on Twitter. In: Proceedings of the NAACL Student Research Workshop, pp. 88\u201393. Association for Computational Linguistics, San Diego, California, June 2016. https:\/\/doi.org\/10.18653\/v1\/N16-2013 . https:\/\/www.aclweb.org\/anthology\/N16-2013","DOI":"10.18653\/v1\/N16-2013"},{"key":"2_CR41","doi-asserted-by":"publisher","first-page":"92","DOI":"10.3390\/info8030092","volume":"8","author":"X Wei","year":"2017","unstructured":"Wei, X., Lin, H., Yang, L., Yu, Y.: A convolution-LSTM-based deep neural network for cross-domain MOOC forum post classification. Information 8, 92 (2017). https:\/\/doi.org\/10.3390\/info8030092","journal-title":"Information"},{"key":"2_CR42","unstructured":"Wiegand, M., Siegel, M., Ruppenhofer, J.: Overview of the germeval 2018 shared task on the identification of offensive language. In: Proceedings of the GermEval 2018 Workshop, pp. 1\u201311 (2018)"},{"key":"2_CR43","unstructured":"Wolf, T., et al.: Huggingface\u2019s transformers: State-of-the-art natural language processing. arXiv:1910.03771 (2019)"},{"key":"2_CR44","unstructured":"Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing (2017), arXiv:1708.02709 Comment: Added BERT, ELMo, Transformer"},{"key":"2_CR45","doi-asserted-by":"publisher","unstructured":"Yuan, S., Wu, X., Xiang, Y.: A two phase deep learning model for identifying discrimination from tweets. In: Pitoura, E., et al. (eds.) Proceedings of the 19th International Conference on Extending Database Technology, EDBT 2016, Bordeaux, France, March 15\u201316, 2016, Bordeaux, France, 15\u201316 March, 2016, pp. 696\u2013697. OpenProceedings.org (2016). https:\/\/doi.org\/10.5441\/002\/edbt.2016.92","DOI":"10.5441\/002\/edbt.2016.92"},{"key":"2_CR46","doi-asserted-by":"crossref","unstructured":"Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N., Kumar, R.: Semeval-2019 task 6: Identifying and categorizing offensive language in social media (offenseval). In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 75\u201386 (2019)","DOI":"10.18653\/v1\/S19-2010"},{"key":"2_CR47","doi-asserted-by":"crossref","unstructured":"Zampieri, M., et al.: SemEval-2020 Task 12: multilingual offensive language identification in social media (OffensEval 2020). In: Proceedings of SemEval (2020)","DOI":"10.18653\/v1\/2020.semeval-1.188"},{"key":"2_CR48","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Luo, L.: Hate speech detection: a solved problem? the challenging case of long tail on twitter. Semantic Web Accepted, October 2018. https:\/\/doi.org\/10.3233\/SW-180338","DOI":"10.3233\/SW-180338"},{"key":"2_CR49","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"745","DOI":"10.1007\/978-3-319-93417-4_48","volume-title":"The Semantic Web","author":"Z Zhang","year":"2018","unstructured":"Zhang, Z., Robinson, D., Tepper, J.: Detecting hate speech on Twitter using a convolution-GRU based deep neural network. In: Gangemi, A., Navigli, R., Vidal, M.-E., Hitzler, P., Troncy, R., Hollink, L., Tordai, A., Alam, M. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 745\u2013760. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93417-4_48"},{"key":"2_CR50","first-page":"237","volume":"17","author":"PG Zimbardo","year":"1969","unstructured":"Zimbardo, P.G.: The human choice: individuation, reason, and order versus deindividuation, impulse, and chaos. Nebr. Symp. Motiv. 17, 237\u2013307 (1969)","journal-title":"Nebr. Symp. Motiv."},{"key":"2_CR51","unstructured":"Zimmerman, S., Kruschwitz, U., Fox, C.: Improving hate speech detection with deep learning ensembles. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA), Miyazaki, Japan, May 2018. https:\/\/www.aclweb.org\/anthology\/L18-1404"}],"container-title":["Lecture Notes in Computer Science","Speech and Computer"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-60276-5_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,22]],"date-time":"2022-11-22T02:42:25Z","timestamp":1669084945000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-60276-5_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030602758","9783030602765"],"references-count":51,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60276-5_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"29 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SPECOM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Speech and Computer","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"St. Petersburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Russia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 October 2020","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":"specom2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/specom.nw.ru\/2020\/","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":"160","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":"65","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":"41% - 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":"5","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)"}},{"value":"Due to the Corona pandemic SPECOM 2020 was held as a virtual event","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}