{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:57:45Z","timestamp":1743130665700,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031337826"},{"type":"electronic","value":"9783031337833"}],"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\/978-3-031-33783-3_27","type":"book-chapter","created":{"date-parts":[[2023,6,8]],"date-time":"2023-06-08T23:02:39Z","timestamp":1686265359000},"page":"283-292","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving the\u00a0Identification of\u00a0Abusive Language Through Careful Design of\u00a0Pre-training Tasks"],"prefix":"10.1007","author":[{"given":"Horacio","family":"Jarqu\u00edn-V\u00e1squez","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hugo Jair","family":"Escalante","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Manuel","family":"Montes-y-G\u00f3mez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,9]]},"reference":[{"issue":"6","key":"27_CR1","doi-asserted-by":"publisher","first-page":"273","DOI":"10.3390\/info13060273","volume":"13","author":"F Alkomah","year":"2022","unstructured":"Alkomah, F., Ma, X.: A literature review of textual hate speech detection methods and datasets. Information 13(6), 273 (2022)","journal-title":"Information"},{"key":"27_CR2","doi-asserted-by":"crossref","unstructured":"Beltagy, I., Lo, K., Cohan, A.: SciBERT: a pretrained language model for scientific text. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing, pp. 3615\u20133620. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/D19-1371"},{"key":"27_CR3","doi-asserted-by":"crossref","unstructured":"Caselli, T., Basile, V., Mitrovi\u0107, J., Granitzer, M.: HateBERT: retraining BERT for abusive language detection in English. In: Proceedings of the 5th Workshop on Online Abuse and Harms (WOAH 2021), pp. 17\u201325. Association for Computational Linguistics, Online, August 2021","DOI":"10.18653\/v1\/2021.woah-1.3"},{"key":"27_CR4","doi-asserted-by":"crossref","unstructured":"Cecillon, N., Labatut, V., Dufour, R., Linar\u00e8s, G.: Abusive language detection in online conversations by combining content- and graph-based features. Front. Big Data 2 (2019)","DOI":"10.3389\/fdata.2019.00008"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Chakrabarty, T., Gupta, K., Muresan, S.: Pay \u201cattention\u201d to your context when classifying abusive language. In: Proceedings of the Third Workshop on Abusive Language Online, pp. 70\u201379. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/W19-3508"},{"key":"27_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 Eleventh International Conference on Web and Social Media, pp. 512\u2013515. AAAI Press (2017)","DOI":"10.1609\/icwsm.v11i1.14955"},{"key":"27_CR7","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, pp. 4171\u20134186. Association for Computational Linguistics (2019)"},{"key":"27_CR8","doi-asserted-by":"crossref","unstructured":"Fersini, E., Nozza, D., Rosso, P.: Overview of the Evalita 2018 task on automatic misogyny identification (AMI). In: Proceedings of the Sixth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian, vol. 2263, pp. 107\u2013114. CEUR-WS.org (2018)","DOI":"10.4000\/books.aaccademia.4497"},{"key":"27_CR9","doi-asserted-by":"crossref","unstructured":"Golbeck, J., et al.: A large labeled corpus for online harassment research. In: Proceedings of the 2017 ACM on Web Science Conference, pp. 229\u2013233. Association for Computing Machinery (2017)","DOI":"10.1145\/3091478.3091509"},{"key":"27_CR10","doi-asserted-by":"crossref","unstructured":"Kamath, R., Ghoshal, A., Eswaran, S., Honnavalli, P.: An enhanced context-based emotion detection model using roberta. In: 2022 IEEE International Conference on Electronics, Computing and Communication Technologies, pp. 1\u20136 (2022)","DOI":"10.1109\/CONECCT55679.2022.9865796"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Liu, P., Li, W., Zou, L.: NULI at SemEval-2019 task 6: transfer learning for offensive language detection using bidirectional transformers. In: Proceedings of the 13th International Workshop on Semantic Evaluation, pp. 87\u201391. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/S19-2011"},{"key":"27_CR12","doi-asserted-by":"crossref","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)","DOI":"10.1371\/journal.pone.0221152"},{"key":"27_CR13","doi-asserted-by":"crossref","unstructured":"Mandl, T., et al.: Overview of the HASOC track at fire 2019: hate speech and offensive content identification in Indo-European languages. In: Proceedings of the 11th Forum for Information Retrieval Evaluation, pp. 14\u201317. Association for Computing Machinery (2019)","DOI":"10.1145\/3368567.3368584"},{"key":"27_CR14","unstructured":"Marcos, Z., Shervin, M., Preslav, N., Sara, R., 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. Association for Computational Linguistics (2019)"},{"key":"27_CR15","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1007\/978-3-030-36687-2_77","volume-title":"Complex Networks and Their Applications VIII","author":"M Mozafari","year":"2020","unstructured":"Mozafari, M., Farahbakhsh, R., Crespi, N.: A BERT-based transfer learning approach for hate speech detection in online social media. In: Cherifi, H., Gaito, S., Mendes, J.F., Moro, E., Rocha, L.M. (eds.) COMPLEX NETWORKS 2019. SCI, vol. 881, pp. 928\u2013940. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-36687-2_77"},{"key":"27_CR16","doi-asserted-by":"crossref","unstructured":"Ramprasath, M., Dhanasekaran, K., Karthick, T., Velumani, R., Sudhakaran, P.: An extensive study on pretrained models for natural language processing based on transformers. In: 2022 International Conference on Electronics and Renewable Systems (ICEARS), pp. 382\u2013389 (2022)","DOI":"10.1109\/ICEARS53579.2022.9752241"},{"key":"27_CR17","doi-asserted-by":"crossref","unstructured":"Schmidt, A., Wiegand, M.: A survey on hate speech detection using natural language processing. In: Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media, pp. 1\u201310. Association for Computational Linguistics, Valencia, Spain (2017)","DOI":"10.18653\/v1\/W17-1101"},{"key":"27_CR18","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: Forum for Information Retrieval Evaluation, vol. 2517, pp. 191\u2013198 (2019)"},{"key":"27_CR19","unstructured":"Zeerak, W., Dirk, H.: 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 (2016)"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-33783-3_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,19]],"date-time":"2023-07-19T07:10:12Z","timestamp":1689750612000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-33783-3_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031337826","9783031337833"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-33783-3_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"9 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexican Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tepic","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mexico","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":"21 June 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mcpr22023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ccc.inaoep.mx\/~mcpr\/index.html","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"61","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":"30","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":"49% - 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":"2.754","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":"3.58","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)"}}]}}