{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T12:51:32Z","timestamp":1743079892646,"version":"3.40.3"},"publisher-location":"Cham","reference-count":20,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031126406"},{"type":"electronic","value":"9783031126413"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-031-12641-3_29","type":"book-chapter","created":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T12:49:18Z","timestamp":1658926158000},"page":"359-369","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Multimodal Fusion Technique for\u00a0Text Based Hate Speech Classification"],"prefix":"10.1007","author":[{"given":"Pranav","family":"Shah","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ankit","family":"Patel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,7,27]]},"reference":[{"key":"29_CR1","doi-asserted-by":"publisher","unstructured":"Alsafari, S., Sadaoui, S.: Semi-supervised self-learning for Arabic hate speech detection. In: 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 863\u2013868 (2021). https:\/\/doi.org\/10.1109\/SMC52423.2021.9659134","DOI":"10.1109\/SMC52423.2021.9659134"},{"key":"29_CR2","doi-asserted-by":"publisher","unstructured":"Boishakhi, F.T., Shill, P.C., Alam, M.G.R.: Multi-modal hate speech detection using machine learning. In: 2021 IEEE International Conference on Big Data (Big Data), pp. 4496\u20134499 (2021). https:\/\/doi.org\/10.1109\/BigData52589.2021.9671955","DOI":"10.1109\/BigData52589.2021.9671955"},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Chaudhari, A., Parseja, A., Patyal, A.: CNN based hate-o-meter: a hate speech detecting tool. In: 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 940\u2013944 (2020)","DOI":"10.1109\/ICSSIT48917.2020.9214247"},{"key":"29_CR4","doi-asserted-by":"crossref","unstructured":"Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv:1406.1078 (2014)","DOI":"10.3115\/v1\/D14-1179"},{"key":"29_CR5","unstructured":"Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)"},{"key":"29_CR6","doi-asserted-by":"publisher","unstructured":"Elisabeth, D., Budi, I., Ibrohim, M.O.: Hate code detection in Indonesian tweets using machine learning approach: a dataset and preliminary study. In: 2020 8th International Conference on Information and Communication Technology (ICoICT), pp. 1\u20136 (2020). https:\/\/doi.org\/10.1109\/ICoICT49345.2020.9166251","DOI":"10.1109\/ICoICT49345.2020.9166251"},{"issue":"4","key":"29_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3232676","volume":"51","author":"P Fortuna","year":"2018","unstructured":"Fortuna, P., Nunes, S.: A survey on automatic detection of hate speech in text. ACM Comput. Surv. 51(4), 1\u201330 (2018)","journal-title":"ACM Comput. Surv."},{"key":"29_CR8","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)"},{"issue":"10","key":"29_CR9","doi-asserted-by":"publisher","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","volume":"28","author":"K Greff","year":"2016","unstructured":"Greff, K., Srivastava, R.K., Koutn\u00edk, J., Steunebrink, B.R., Schmidhuber, J.: LSTM: a search space odyssey. IEEE Trans. Neural Netw. Learn. Syst. 28(10), 2222\u20132232 (2016)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"29_CR10","doi-asserted-by":"publisher","unstructured":"Huang, X., Xu, M.: An inter and intra transformer for hate speech detection. In: 2021 3rd International Academic Exchange Conference on Science and Technology Innovation (IAECST), pp. 346\u2013349 (2021). https:\/\/doi.org\/10.1109\/IAECST54258.2021.9695652","DOI":"10.1109\/IAECST54258.2021.9695652"},{"key":"29_CR11","doi-asserted-by":"publisher","unstructured":"Khan, H., Yu, F., Sinha, A., Gokhale, S.S.: A parsimonious and practical approach to detecting offensive speech. In: 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), pp. 688\u2013695 (2021). https:\/\/doi.org\/10.1109\/ICCCIS51004.2021.9397140","DOI":"10.1109\/ICCCIS51004.2021.9397140"},{"key":"29_CR12","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.neucom.2019.10.033","volume":"376","author":"J Kim","year":"2020","unstructured":"Kim, J., Jang, S., Park, E., Choi, S.: Text classification using capsules. Neurocomputing 376, 214\u2013221 (2020)","journal-title":"Neurocomputing"},{"issue":"2","key":"29_CR13","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1109\/TCSS.2019.2892037","volume":"6","author":"H Liu","year":"2019","unstructured":"Liu, H., Burnap, P., Alorainy, W., Williams, M.L.: A fuzzy approach to text classification with two-stage training for ambiguous instances. IEEE Trans. Comput. Soc. Syst. 6(2), 227\u2013240 (2019)","journal-title":"IEEE Trans. Comput. Soc. Syst."},{"key":"29_CR14","doi-asserted-by":"publisher","unstructured":"Mayda,, Demir, Y.E., Dalyan, T., Diri, B.: Hate speech dataset from Turkish tweets. In: 2021 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1\u20136 (2021). https:\/\/doi.org\/10.1109\/ASYU52992.2021.9599042","DOI":"10.1109\/ASYU52992.2021.9599042"},{"key":"29_CR15","doi-asserted-by":"publisher","unstructured":"Naidu, T.A., Kumar, S.: Hate speech detection using multi-channel convolutional neural network. In: 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), pp. 908\u2013912 (2021). https:\/\/doi.org\/10.1109\/ICAC3N53548.2021.9725696","DOI":"10.1109\/ICAC3N53548.2021.9725696"},{"key":"29_CR16","doi-asserted-by":"crossref","unstructured":"Naseem, U., Razzak, I., Eklund, P.W.: A survey of pre-processing techniques to improve short-text quality: a case study on hate speech detection on twitter. Multimedia Tools Appl. 1\u201328 (2020)","DOI":"10.1007\/s11042-020-10082-6"},{"issue":"2","key":"29_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3371276","volume":"20","author":"D Paschalides","year":"2020","unstructured":"Paschalides, D., et al.: Mandola: a big-data processing and visualization platform for monitoring and detecting online hate speech. ACM Trans. Internet Technol. 20(2), 1\u201321 (2020)","journal-title":"ACM Trans. Internet Technol."},{"key":"29_CR18","doi-asserted-by":"publisher","unstructured":"Sachdeva, J., Chaudhary, K.K., Madaan, H., Meel, P.: Text based hate-speech analysis. In: 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), pp. 661\u2013668 (2021). https:\/\/doi.org\/10.1109\/ICAIS50930.2021.9396013","DOI":"10.1109\/ICAIS50930.2021.9396013"},{"key":"29_CR19","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1109\/TASLP.2019.2959251","volume":"28","author":"J Wang","year":"2020","unstructured":"Wang, J., Yu, L., Lai, K.R., Zhang, X.: Tree-structured regional CNN-LSTM model for dimensional sentiment analysis. IEEE\/ACM Trans. Audio Speech Lang. Process. 28, 581\u2013591 (2020)","journal-title":"IEEE\/ACM Trans. Audio Speech Lang. Process."},{"key":"29_CR20","doi-asserted-by":"publisher","first-page":"13825","DOI":"10.1109\/ACCESS.2018.2806394","volume":"6","author":"H Watanabe","year":"2018","unstructured":"Watanabe, H., Bouazizi, M., Ohtsuki, T.: Hate speech on twitter: a pragmatic approach to collect hateful and offensive expressions and perform hate speech detection. IEEE Access 6, 13825\u201313835 (2018). https:\/\/doi.org\/10.1109\/ACCESS.2018.2806394","journal-title":"IEEE Access"}],"container-title":["Communications in Computer and Information Science","Advances in Computing and Data Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-12641-3_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,12]],"date-time":"2023-02-12T18:32:47Z","timestamp":1676226767000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-12641-3_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031126406","9783031126413"],"references-count":20,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-12641-3_29","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"27 July 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICACDS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advances in Computing and Data Sciences","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kumool","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 April 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 April 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icacds2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icacds.com\/","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":"411","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":"69","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":"17% - 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":"2","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)"}}]}}