{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T16:16:56Z","timestamp":1778602616831,"version":"3.51.4"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030878016","type":"print"},{"value":"9783030878023","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\/978-3-030-87802-3_15","type":"book-chapter","created":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T23:36:52Z","timestamp":1632267412000},"page":"157-165","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Toxic Comment Classification Service in Social Network"],"prefix":"10.1007","author":[{"given":"Mikhail","family":"Dolgushin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dayana","family":"Ismakova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuliya","family":"Bidulya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Igor","family":"Krupkin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Galina","family":"Barskaya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anastasiya","family":"Lesiv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,9,22]]},"reference":[{"key":"15_CR1","doi-asserted-by":"crossref","unstructured":"Georgakopoulos, S.V., Tasoulis, S.K., Vrahatis, A.G., Plagianakos, V.P.: Convolutional neural networks for toxic comment classification. arXiv preprint arXiv:1802.09957 (2018)","DOI":"10.1145\/3200947.3208069"},{"key":"15_CR2","unstructured":"Medialogiya\u2013monitoring and analysis of media and social networks (rus.). https:\/\/www.mlg.ru"},{"issue":"2","key":"15_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3377323.hal-02972184","volume":"20","author":"M Corazza","year":"2020","unstructured":"Corazza, M., Menini, S., Cabrio, E., Tonelli, S., Villata, S.: A multilingual evaluation for online hate speech detection. ACM Trans. Internet Technol. Assoc. Comput. Mach. 20(2), 1\u201322 (2020). https:\/\/doi.org\/10.1145\/3377323.hal-02972184","journal-title":"ACM Trans. Internet Technol. Assoc. Comput. Mach."},{"key":"15_CR4","unstructured":"Russian Language Toxic Comments. https:\/\/www.kaggle.com\/blackmoon\/russian-language-toxic-comments"},{"key":"15_CR5","unstructured":"\u201cToxicology\u201d project: vk_comments_DS. https:\/\/github.com\/mihatronych\/files\/blob\/main\/ds_of_toxic_messages_from_vk\/our_toxic_vk_comments_data.csv"},{"key":"15_CR6","doi-asserted-by":"crossref","first-page":"49","DOI":"10.21248\/jlcl.34.2020.224","volume":"34","author":"R Shekhar","year":"2020","unstructured":"Shekhar, R., Pranji\u0107, M., Pollak, S., Pelicon, A., Purver, M.: Automating news comment moderation with limited resources: benchmarking in croatian and estonian. J. Lang. Technol. Comput. Linguist. 34, 49\u201379 (2020)","journal-title":"J. Lang. Technol. Comput. Linguist."},{"key":"15_CR7","doi-asserted-by":"crossref","unstructured":"Pavlopoulos, J., Malakasiotis, P., Androutsopoulos, I.: Deeper attention to abusive user content moderation. In: EMNLP, pp. 1125\u20131135. Copenghagen, Denmark (2017)","DOI":"10.18653\/v1\/D17-1117"},{"key":"15_CR8","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1007\/978-3-030-26061-3_28","volume-title":"Speech and Computer","author":"D Levonevskiy","year":"2019","unstructured":"Levonevskiy, D., Malov, D., Vatamaniuk, I.: Estimating aggressiveness of russian texts by means of machine learning. In: Salah, A.A., Karpov, A., Potapova, R. (eds.) SPECOM 2019. LNCS (LNAI), vol. 11658, pp. 270\u2013279. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-26061-3_28"},{"issue":"3","key":"15_CR9","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1007\/s11390-014-1446-5","volume":"29","author":"J-T Lee","year":"2014","unstructured":"Lee, J.-T., Yang, M.-C., Rim, H.-C.: Discovering high-quality threaded discussions in online forums. J. Comput. Sci. Technol. 29(3), 519\u2013531 (2014)","journal-title":"J. Comput. Sci. Technol."},{"key":"15_CR10","doi-asserted-by":"crossref","unstructured":"Plaza-del Arco, F.M., Molina-Gonzalez, D., Mart\u0131n-Valdivia, T., Urena-Lopez, A.: SINAI at SemEval-2019 Task 6: incorporating lexicon knowledge into SVM learning to identify and categorize offensive language in social media. In: The 13th International Workshop on Semantic Evaluation (SemEval) (2019)","DOI":"10.18653\/v1\/S19-2129"},{"key":"15_CR11","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1007\/978-3-030-60276-5_11","volume-title":"Speech and Computer","author":"A Chernyaev","year":"2020","unstructured":"Chernyaev, A., Spryiskov, A., Ivashko, A., Bidulya, Y.: A rumor detection in Russian tweets. In: Karpov, A., Potapova, R. (eds.) SPECOM 2020. LNCS (LNAI), vol. 12335, pp. 108\u2013118. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-60276-5_11"},{"key":"15_CR12","doi-asserted-by":"crossref","unstructured":"Pavlopoulos, J., Thain, N., Dixon, L., Androutsopoulos, I.: ConvAI at SemEval-2019 Task 6: offensive language identification and categorization with perspective and BERT. In: SemEval, Minneapolis, USA (2019)","DOI":"10.18653\/v1\/S19-2102"},{"key":"15_CR13","unstructured":"Pietro, M.D.: Text Classification with NLP: tf-idf vs Word2Vec vs BERT. https:\/\/towardsdatascience.com\/text-classification-with-nlp-tf-idf-vs-word2vec-vs-bert-41ff868d1794"},{"key":"15_CR14","doi-asserted-by":"crossref","unstructured":"Camacho-Collados, J., Pilehvar, M.T.: From word to sense embeddings: a survey on vector representations of meaning. arXiv:1805.04032. Bibcode:2018arXiv180504032C (2018)","DOI":"10.1613\/jair.1.11259"},{"key":"15_CR15","doi-asserted-by":"crossref","unstructured":"Waseem, Z., Hovy, D.: Hateful symbols or hateful people? predictive features for hate speech detection on Twitter. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, pp. 88\u201393 (2016)","DOI":"10.18653\/v1\/N16-2013"},{"key":"15_CR16","unstructured":"NLTK documentation. https:\/\/www.nltk.org"},{"key":"15_CR17","unstructured":"Morphological analyzer pymorphy2. https:\/\/pymorphy2.readthedocs.io"},{"key":"15_CR18","unstructured":"Document-term matrix. https:\/\/en.wikipedia.org\/wiki\/Document-term_matrix"},{"key":"15_CR19","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825\u20132830. JMLR (2011)"},{"key":"15_CR20","unstructured":"Rehurek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: LREC 2010 Workshop on New Challenges for NLP Frameworks, pp. 45\u201350. Valletta, Malta, May. ELRA (2010). http:\/\/is.muni.cz\/publication\/884893\/en"},{"key":"15_CR21","unstructured":"Gensim: Doc2vec. https:\/\/radimrehurek.com\/gensim\/models\/doc2vec.html"},{"key":"15_CR22","unstructured":"Mestre, M.: FastText: stepping through the code. https:\/\/medium.com\/@mariamestre\/fasttext-stepping-through-the-code-259996d6ebc4"},{"key":"15_CR23","unstructured":"Dostoevsky: Sentiment Analysis Library for Russian Language. https:\/\/pypi.org\/project\/dostoevsky"},{"key":"15_CR24","unstructured":"SpaCy: Industrial-Strength Natural Language Processing. https:\/\/spacy.io"},{"key":"15_CR25","unstructured":"Wang, S., Manning, C.D.: Baselines and bigrams: simple, good sentiment and topic classification, Department of Computer Science, Stanford University, Stanford 94305. https:\/\/nlp.stanford.edu\/pubs\/sidaw12_simple_sentiment.pdf"},{"key":"15_CR26","unstructured":"Wang, Z.: NBSVM. https:\/\/www.kaggle.com\/ziliwang\/nbsvm"}],"container-title":["Lecture Notes in Computer Science","Speech and Computer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-87802-3_15","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T22:00:41Z","timestamp":1673301641000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-87802-3_15"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030878016","9783030878023"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-87802-3_15","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":"22 September 2021","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"specom2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/specom.nw.ru\/2021\/","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":"163","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":"74","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":"45% - 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":"3.5","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":"5.5","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":"The conference was held online due to the COVID-19 pandemic.","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)"}}]}}