{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T18:27:12Z","timestamp":1773080832880,"version":"3.50.1"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031416811","type":"print"},{"value":"9783031416828","type":"electronic"}],"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\/978-3-031-41682-8_9","type":"book-chapter","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T07:02:59Z","timestamp":1692342179000},"page":"132-148","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["\u201cExplain Thyself Bully\u201d: Sentiment Aided Cyberbullying Detection with\u00a0Explanation"],"prefix":"10.1007","author":[{"given":"Krishanu","family":"Maity","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prince","family":"Jha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raghav","family":"Jain","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sriparna","family":"Saha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pushpak","family":"Bhattacharyya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,19]]},"reference":[{"key":"9_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1007\/978-3-319-76941-7_11","volume-title":"Advances in Information Retrieval","author":"S Agrawal","year":"2018","unstructured":"Agrawal, S., Awekar, A.: Deep learning for detecting cyberbullying across multiple social media platforms. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds.) ECIR 2018. LNCS, vol. 10772, pp. 141\u2013153. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-76941-7_11"},{"key":"9_CR2","doi-asserted-by":"crossref","unstructured":"Artetxe, M., Labaka, G., Agirre, E.: Learning bilingual word embeddings with (almost) no bilingual data. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 451\u2013462 (2017)","DOI":"10.18653\/v1\/P17-1042"},{"key":"9_CR3","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, pp. 759\u2013760 (2017)","DOI":"10.1145\/3041021.3054223"},{"key":"9_CR4","doi-asserted-by":"publisher","first-page":"101710","DOI":"10.1016\/j.cose.2019.101710","volume":"90","author":"V Balakrishnan","year":"2020","unstructured":"Balakrishnan, V., Khan, S., Arabnia, H.R.: Improving cyberbullying detection using twitter users\u2019 psychological features and machine learning. Comput. Secur. 90, 101710 (2020)","journal-title":"Comput. Secur."},{"key":"9_CR5","doi-asserted-by":"crossref","unstructured":"Bohra, A., Vijay, D., Singh, V., Akhtar, S.S., Shrivastava, M.: A dataset of Hindi-English code-mixed social media text for hate speech detection. In: Proceedings of the Second Workshop on Computational Modeling of People\u2019s Opinions, Personality, and Emotions in Social Media, pp. 36\u201341 (2018)","DOI":"10.18653\/v1\/W18-1105"},{"key":"9_CR6","unstructured":"Camburu, O.M., Rockt\u00e4schel, T., Lukasiewicz, T., Blunsom, P.: e-snli: Natural language inference with natural language explanations. Adv. Neural Inf. Process. Syst. 31 (2018)"},{"key":"9_CR7","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)","DOI":"10.3115\/v1\/W14-4012"},{"key":"9_CR8","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1007\/978-3-319-06483-3_25","volume-title":"Advances in Artificial Intelligence","author":"M Dadvar","year":"2014","unstructured":"Dadvar, M., Trieschnigg, D., de Jong, F.: Experts and machines against bullies: a hybrid approach to detect cyberbullies. In: Sokolova, M., van Beek, P. (eds.) AI 2014. LNCS (LNAI), vol. 8436, pp. 275\u2013281. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-06483-3_25"},{"key":"9_CR9","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"DeYoung, J., et al.: Eraser: a benchmark to evaluate rationalized nlp models. arXiv preprint arXiv:1911.03429 (2019)","DOI":"10.18653\/v1\/2020.acl-main.408"},{"key":"9_CR11","unstructured":"Dinakar, K., Reichart, R., Lieberman, H.: Modeling the detection of textual cyberbullying. In: Proceedings of the International Conference on Weblog and Social Media 2011, Citeseer (2011)"},{"issue":"5","key":"9_CR12","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1037\/h0031619","volume":"76","author":"JL Fleiss","year":"1971","unstructured":"Fleiss, J.L.: Measuring nominal scale agreement among many raters. Psychol. Bull. 76(5), 378 (1971)","journal-title":"Psychol. Bull."},{"key":"9_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1007\/978-3-030-99739-7_15","volume-title":"Advances in Information Retrieval","author":"S Ghosh","year":"2022","unstructured":"Ghosh, S., Roy, S., Ekbal, A., Bhattacharyya, P.: CARES: CAuse recognition for emotion in suicide notes. In: Hagen, M., et al. (eds.) ECIR 2022. LNCS, vol. 13186, pp. 128\u2013136. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-99739-7_15"},{"key":"9_CR14","unstructured":"Grave, E., Bojanowski, P., Gupta, P., Joulin, A., Mikolov, T.: Learning word vectors for 157 languages. arXiv preprint arXiv:1802.06893 (2018)"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G.Z.: Xai-explainable artificial intelligence. Sci. Robot. 4(37), eaay7120 (2019)","DOI":"10.1126\/scirobotics.aay7120"},{"key":"9_CR16","unstructured":"Kamble, S., Joshi, A.: Hate speech detection from code-mixed Hindi-English tweets using deep learning models. arXiv preprint arXiv:1811.05145 (2018)"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Karim, M.R., et al.: Deephateexplainer: explainable hate speech detection in under-resourced Bengali language. In: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), pp. 1\u201310. IEEE (2021)","DOI":"10.1109\/DSAA53316.2021.9564230"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"9_CR19","unstructured":"Kumar, R., Reganti, A.N., Bhatia, A., Maheshwari, T.: Aggression-annotated corpus of Hindi-English code-mixed data. arXiv preprint arXiv:1803.09402 (2018)"},{"key":"9_CR20","unstructured":"Lewis, M., Haviland-Jones, J.M., Barrett, L.F.: Handbook of emotions. Guilford Press, New York (2010)"},{"key":"9_CR21","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1007\/978-3-030-80599-9_13","volume-title":"Natural Language Processing and Information Systems","author":"K Maity","year":"2021","unstructured":"Maity, K., Saha, S.: BERT-capsule model for cyberbullying detection in code-mixed Indian languages. In: M\u00e9tais, E., Meziane, F., Horacek, H., Kapetanios, E. (eds.) NLDB 2021. LNCS, vol. 12801, pp. 147\u2013155. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-80599-9_13"},{"key":"9_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1007\/978-3-030-92273-3_36","volume-title":"Neural Information Processing","author":"K Maity","year":"2021","unstructured":"Maity, K., Saha, S.: A multi-task model for\u00a0sentiment aided cyberbullying detection in\u00a0code-mixed Indian languages. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds.) ICONIP 2021. LNCS, vol. 13111, pp. 440\u2013451. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-92273-3_36"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"Maity, K., Sen, T., Saha, S., Bhattacharyya, P.: Mtbullygnn: a graph neural network-based multitask framework for cyberbullying detection. IEEE Trans. Comput. Soc. Syst. (2022)","DOI":"10.1109\/TCSS.2022.3230974"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Mathew, B., Saha, P., Yimam, S.M., Biemann, C., Goyal, P., Mukherjee, A.: Hatexplain: a benchmark dataset for explainable hate speech detection. arXiv preprint arXiv:2012.10289 (2020)","DOI":"10.1609\/aaai.v35i17.17745"},{"key":"9_CR25","unstructured":"Myers-Scotton, C.: Duelling languages: Grammatical structure in codeswitching. Oxford University Press, Oxford (1997)"},{"key":"9_CR26","unstructured":"NCRB: Crime in india - 2020. National Crime Records Bureau (2020)"},{"key":"9_CR27","doi-asserted-by":"publisher","unstructured":"Paul, Sayanta, Saha, Sriparna: CyberBERT: BERT for cyberbullying identification. Multimedia Syst. 1\u20138 (2020). https:\/\/doi.org\/10.1007\/s00530-020-00710-4","DOI":"10.1007\/s00530-020-00710-4"},{"key":"9_CR28","doi-asserted-by":"crossref","unstructured":"Pramanick, S., et al.: Detecting harmful memes and their targets. arXiv preprint arXiv:2110.00413 (2021)","DOI":"10.18653\/v1\/2021.findings-acl.246"},{"key":"9_CR29","doi-asserted-by":"crossref","unstructured":"Rajani, N.F., McCann, B., Xiong, C., Socher, R.: Explain yourself! leveraging language models for commonsense reasoning. arXiv preprint arXiv:1906.02361 (2019)","DOI":"10.18653\/v1\/P19-1487"},{"key":"9_CR30","unstructured":"Regulation, P.: Regulation (EU) 2016\/679 of the European parliament and of the council. Regulation (EU) 679, 2016 (2016)"},{"key":"9_CR31","doi-asserted-by":"crossref","unstructured":"Reynolds, K., Kontostathis, A., Edwards, L.: Using machine learning to detect cyberbullying. In: 2011 10th International Conference on Machine Learning and Applications and Workshops, vol. 2, pp. 241\u2013244. IEEE (2011)","DOI":"10.1109\/ICMLA.2011.152"},{"key":"9_CR32","doi-asserted-by":"crossref","unstructured":"Rijhwani, S., Sequiera, R., Choudhury, M., Bali, K., Maddila, C.S.: Estimating code-switching on twitter with a novel generalized word-level language detection technique. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (volume 1: long papers), pp. 1971\u20131982 (2017)","DOI":"10.18653\/v1\/P17-1180"},{"key":"9_CR33","doi-asserted-by":"crossref","unstructured":"Saha, T., Upadhyaya, A., Saha, S., Bhattacharyya, P.: A multitask multimodal ensemble model for sentiment-and emotion-aided tweet act classification. IEEE Trans. Comput. Soc. Syst. (2021)","DOI":"10.1109\/TCSS.2021.3088714"},{"issue":"1","key":"9_CR34","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1007\/s12559-021-09844-7","volume":"14","author":"A Singh","year":"2021","unstructured":"Singh, A., Saha, S., Hasanuzzaman, M., Dey, K.: Multitask learning for complaint identification and sentiment analysis. Cogn. Comput. 14(1), 212\u2013227 (2021). https:\/\/doi.org\/10.1007\/s12559-021-09844-7","journal-title":"Cogn. Comput."},{"issue":"4","key":"9_CR35","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1111\/j.1469-7610.2007.01846.x","volume":"49","author":"PK Smith","year":"2008","unstructured":"Smith, P.K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., Tippett, N.: Cyberbullying: its nature and impact in secondary school pupils. J. Child Psychol. Psychiatry 49(4), 376\u2013385 (2008)","journal-title":"J. Child Psychol. Psychiatry"},{"issue":"1","key":"9_CR36","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1002\/casp.2136","volume":"23","author":"F Sticca","year":"2013","unstructured":"Sticca, F., Ruggieri, S., Alsaker, F., Perren, S.: Longitudinal risk factors for cyberbullying in adolescence. J. Community Appl. Soc. Psychol. 23(1), 52\u201367 (2013)","journal-title":"J. Community Appl. Soc. Psychol."},{"key":"9_CR37","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998\u20136008 (2017)"},{"key":"9_CR38","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 NAACL Student Research Workshop, pp. 88\u201393 (2016)","DOI":"10.18653\/v1\/N16-2013"},{"issue":"4","key":"9_CR39","doi-asserted-by":"publisher","first-page":"e1169","DOI":"10.1542\/peds.2006-0815","volume":"118","author":"ML Ybarra","year":"2006","unstructured":"Ybarra, M.L., Mitchell, K.J., Wolak, J., Finkelhor, D.: Examining characteristics and associated distress related to internet harassment: findings from the second youth internet safety survey. Pediatrics 118(4), e1169\u2013e1177 (2006)","journal-title":"Pediatrics"},{"key":"9_CR40","unstructured":"Yin, W., Kann, K., Yu, M., Sch\u00fctze, H.: Comparative study of CNN and RNN for natural language processing. arXiv preprint arXiv:1702.01923 (2017)"},{"key":"#cr-split#-9_CR41.1","unstructured":"Zaidan, O., Eisner, J., Piatko, C.: Using \"Annotator rationales\" to improve machine learning for text categorization. In: Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics"},{"key":"#cr-split#-9_CR41.2","unstructured":"Proceedings of the Main Conference, pp. 260-267 (2007)"},{"key":"9_CR42","unstructured":"Zhang, Z., Sabuncu, M.: Generalized cross entropy loss for training deep neural networks with noisy labels. Adv. Neural Inf. Process. Syst. 31 (2018)"}],"container-title":["Lecture Notes in Computer Science","Document Analysis and Recognition - ICDAR 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-41682-8_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T07:17:38Z","timestamp":1692343058000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-41682-8_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031416811","9783031416828"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-41682-8_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"19 August 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICDAR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Document Analysis and Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"San Jos\u00e9, CA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","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 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icdar2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icdar2023.org\/","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":"316","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":"154","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":"2.89","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":"1.50","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Number and type of other papers accepted : IJDAR track papers","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)"}}]}}