{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T16:27:18Z","timestamp":1782577638798,"version":"3.54.5"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2020,7,13]],"date-time":"2020-07-13T00:00:00Z","timestamp":1594598400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,7,13]],"date-time":"2020-07-13T00:00:00Z","timestamp":1594598400000},"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":["Multimedia Systems"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s00530-020-00672-7","type":"journal-article","created":{"date-parts":[[2020,7,13]],"date-time":"2020-07-13T02:02:36Z","timestamp":1594605756000},"page":"2027-2041","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Multi-input integrative learning using deep neural networks and transfer learning for cyberbullying detection in real-time code-mix data"],"prefix":"10.1007","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4263-7168","authenticated-orcid":false,"given":"Akshi","family":"Kumar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nitin","family":"Sachdeva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,7,13]]},"reference":[{"issue":"1","key":"672_CR1","doi-asserted-by":"publisher","first-page":"e5107","DOI":"10.1002\/cpe.5107","volume":"32","author":"A Kumar","year":"2020","unstructured":"Kumar, A., Jaiswal, A.: Systematic literature review of sentiment analysis on Twitter using soft computing techniques. Concurr. Comput. Pract. Exp. 32(1), e5107 (2020)","journal-title":"Concurr. Comput. Pract. Exp."},{"issue":"2","key":"672_CR2","first-page":"6","volume":"14","author":"A Kumar","year":"2017","unstructured":"Kumar, A., Sharma, A.: Systematic literature review on opinion mining of big data for government intelligence. Webology 14(2), 6\u201347 (2017)","journal-title":"Webology"},{"key":"672_CR3","unstructured":"Brown L (2012) New Harvard study shows why social media is so addictive for many. [online] WTWH Marketing Lab. https:\/\/www.marketing.wtwhmedia.com\/new-harvard-study-shows-why-social-media-is-so-addictive-for-many\/. Accessed 27 Jan 2020"},{"issue":"1","key":"672_CR4","first-page":"68","volume":"15","author":"MA Campbell","year":"2005","unstructured":"Campbell, M.A.: Cyber bullying: an old problem in a new guise? J. Psychol. Couns. Sch. 15(1), 68\u201376 (2005)","journal-title":"J. Psychol. Couns. Sch."},{"key":"672_CR5","volume-title":"Online Safety and Internet Addiction (A Study Conducted Amongst Adolescents in Delhi-NCR)","author":"Child Rights and You (CRY)","year":"2020","unstructured":"Child Rights and You (CRY): Online Safety and Internet Addiction (A Study Conducted Amongst Adolescents in Delhi-NCR). Child Rights and You, New Delhi (2020)"},{"issue":"17","key":"672_CR6","doi-asserted-by":"publisher","first-page":"23973","DOI":"10.1007\/s11042-019-7234-z","volume":"78","author":"A Kumar","year":"2019","unstructured":"Kumar, A., Sachdeva, N.: Cyberbullying detection on social multimedia using soft computing techniques: a meta-analysis. Multimed. Tools Appl. 78(17), 23973\u201324010 (2019)","journal-title":"Multimed. Tools Appl."},{"key":"672_CR7","unstructured":"Patra, B.G., Das, D., Das, A.: Sentiment analysis of code-mixed Indian languages: an overview of SAIL_Code-Mixed Shared Task@ ICON-2017. arXiv preprint. arXiv:1803.06745 (2018)"},{"key":"672_CR8","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1016\/j.physa.2016.01.015","volume":"449","author":"RD Parshad","year":"2016","unstructured":"Parshad, R.D., Bhowmick, S., Chand, V., Kumari, N., Sinha, N.: What is India speaking? Exploring the \u201cHinglish\u201d invasion. Phys. A 449, 375\u2013389 (2016)","journal-title":"Phys. A"},{"key":"672_CR9","doi-asserted-by":"publisher","first-page":"106198","DOI":"10.1016\/j.asoc.2020.106198","volume":"91","author":"D Jain","year":"2020","unstructured":"Jain, D., Kumar, A., Garg, G.: Sarcasm detection in mash-up language using soft-attention based bi-directional LSTM and feature-rich CNN. Appl. Soft Comput. 91, 106198 (2020). https:\/\/doi.org\/10.1016\/j.asoc.2020.106198","journal-title":"Appl. Soft Comput."},{"key":"672_CR10","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1016\/j.chb.2018.12.021","volume":"93","author":"H Rosa","year":"2019","unstructured":"Rosa, H., Pereira, N., Ribeiro, R., Ferreira, P.C., Carvalho, J.P., Oliveira, S., Trancoso, I.: Automatic cyberbullying detection: a systematic review. Comput. Hum. Behav. 93, 333\u2013345 (2019)","journal-title":"Comput. Hum. Behav."},{"key":"672_CR11","first-page":"1","volume":"1","author":"S Salawu","year":"2017","unstructured":"Salawu, S., He, Y., Lumsden, J.: Approaches to automated detection of cyberbullying: a survey. IEEE Trans. Affect. Comput. 1, 1\u201320 (2017)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"672_CR12","doi-asserted-by":"crossref","unstructured":"Reynolds, K., Kontostathis. A., Edwards, L.: Using machine learning to detect cyberbullying. In: Machine Learning and Applications and Workshops (ICMLA), 2011 10th International Conference, vol. 2, pp. 241\u2013244. IEEE (2011)","DOI":"10.1109\/ICMLA.2011.152"},{"key":"672_CR13","unstructured":"Dinakar, K., Reichart, R., Lieberman, H.: Modeling the detection of textual cyberbullying. In: International AAAI Conference on Web and Social Media, North America, July 2011 (2016)"},{"key":"672_CR14","doi-asserted-by":"crossref","unstructured":"Dadvar, M., Trieschnigg, D., Ordelman, R., de Jong, F.: Improving cyberbullying detection with user context. In: European Conference on Information Retrieval, pp. 693\u2013696. Springer, Berlin, Heidelberg (2013)","DOI":"10.1007\/978-3-642-36973-5_62"},{"key":"672_CR15","doi-asserted-by":"crossref","unstructured":"Dadvar, M., Trieschnigg, D., de Jong, F.: Experts and machines against bullies: a hybrid approach to detect cyberbullies. In: Canadian Conference on Artificial Intelligence, pp. 275\u2013281. Springer, Cham (2014)","DOI":"10.1007\/978-3-319-06483-3_25"},{"key":"672_CR16","doi-asserted-by":"crossref","unstructured":"Kontostathis, A., Reynolds, K., Garron, A., Edwards, L.: Detecting cyberbullying: query terms and techniques. In: Proceedings of the 5th Annual ACM web Science Conference, pp. 195\u2013204 (2013)","DOI":"10.1145\/2464464.2464499"},{"key":"672_CR17","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.knosys.2015.12.021","volume":"96","author":"N Potha","year":"2016","unstructured":"Potha, N., Maragoudakis, M., Lyras, D.: A biology-inspired, data mining framework for extracting patterns in sexual cyberbullying data. Knowl. Based Syst. 96, 134\u2013155 (2016)","journal-title":"Knowl. Based Syst."},{"key":"672_CR18","doi-asserted-by":"crossref","unstructured":"Hosseinmardi, H., Rafiq, R.I., Han, R., Lv, Q., Mishra, S.: Prediction of cyberbullying incidents in a media based social network. In: Proceedings of the 2016 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 186\u2013192 (2016)","DOI":"10.1109\/ASONAM.2016.7752233"},{"key":"672_CR19","doi-asserted-by":"crossref","unstructured":"Hammer, H.L.: Automatic detection of hateful comments in online discussion. In: International Conference on Industrial Networks and Intelligent Systems, pp 164\u2013173. Springer, Cham (2016)","DOI":"10.1007\/978-3-319-52569-3_15"},{"issue":"2","key":"672_CR20","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/s13042-015-0463-1","volume":"8","author":"G Sarna","year":"2017","unstructured":"Sarna, G., Bhatia, M.P.: Content based approach to find the credibility of user in social networks: an application of cyberbullying. Int. J. Mach. Learn. Cybern. 8(2), 677\u2013689 (2017)","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"672_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, X., Tong, J., Vishwamitra, N., Whittaker, E., Mazer, J.P., Kowalski, R., Hu, H., Luo, F., Macbeth, J., Dillon, E.: Cyberbullying detection with a pronunciation based convolutional neural network. In: 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 740\u2013745 (2016)","DOI":"10.1109\/ICMLA.2016.0132"},{"issue":"3","key":"672_CR22","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1109\/TAFFC.2016.2531682","volume":"8","author":"R Zhao","year":"2017","unstructured":"Zhao, R., Mao, K.: Cyberbullying detection based on semantic-enhanced marginalized denoising autoencoder. IEEE Trans. Affect. Comput. 8(3), 328\u2013339 (2017)","journal-title":"IEEE Trans. Affect. Comput."},{"key":"672_CR23","doi-asserted-by":"crossref","unstructured":"Zhao, R., Zhou, A., Mao, K.: Automatic detection of cyberbullying on social networks based on bullying features. In: Proceedings of the 17th International Conference on Distributed Computing and Networking, pp. 43\u201348 (2016)","DOI":"10.1145\/2833312.2849567"},{"key":"672_CR24","doi-asserted-by":"crossref","unstructured":"Raisi, E., Huang, B.: Cyberbullying detection with weakly supervised machine learning. In: Proceedings of the 2017 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 409\u2013416. ACM (2017)","DOI":"10.1145\/3110025.3110049"},{"key":"672_CR25","doi-asserted-by":"crossref","unstructured":"Rakib, T.B., Soon, L.K.: Using the Reddit Corpus for cyberbully detection. In: Asian Conference on Intelligent Information and Database Systems, p. 180. Springer, Cham (2018)","DOI":"10.1007\/978-3-319-75417-8_17"},{"key":"672_CR26","unstructured":"Ptaszynski, M., Pieciukiewicz, A., Dyba\u0142a, P.: Results of the PolEval 2019 shared task 6: first dataset and open shared task for automatic cyberbullying detection in Polish Twitter. In: Proceedings of the PolEval2019 Workshop, p. 89 (2019)"},{"key":"672_CR27","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.sbspro.2016.12.022","volume":"236","author":"D Gordeev","year":"2016","unstructured":"Gordeev, D.: Automatic detection of verbal aggression for Russian and American image boards. Procedia Soc. Behav. Sci. 236, 71\u201375 (2016)","journal-title":"Procedia Soc. Behav. Sci."},{"key":"672_CR28","doi-asserted-by":"crossref","unstructured":"Ibrohim, M.O., Budi, I.: Multi-label hate speech and abusive language detection in Indonesian Twitter. In: Proceedings of the Third Workshop on Abusive Language Online, pp. 46\u201357 (2019)","DOI":"10.18653\/v1\/W19-3506"},{"key":"672_CR29","doi-asserted-by":"crossref","unstructured":"Pratiwi, N.I., Budi, I., Jiwanggi, M.A.: Hate Speech Identification using the Hate Codes for Indonesian Tweets. In: Proceedings of the 2019 2nd International Conference on Data Science and Information Technology, pp. 128\u2013133 (2019)","DOI":"10.1145\/3352411.3352432"},{"key":"672_CR30","doi-asserted-by":"crossref","unstructured":"Haidar, B., Chamoun, M., Serhrouchni, A.: Multilingual cyberbullying detection system: detecting cyberbullying in Arabic content. In: 2017 1st Cyber Security in Networking Conference (CSNet), pp. 1\u20138. IEEE (2017)","DOI":"10.1109\/CSNET.2017.8242005"},{"issue":"6","key":"672_CR31","doi-asserted-by":"publisher","first-page":"275","DOI":"10.25046\/aj020634","volume":"2","author":"B Haidar","year":"2017","unstructured":"Haidar, B., Chamoun, M., Serhrouchni, A.: A multilingual system for cyberbullying detection: Arabic content detection using machine learning. Adv. Sci. Technol. Eng. Syst J. 2(6), 275\u2013284 (2017)","journal-title":"Adv. Sci. Technol. Eng. Syst J."},{"key":"672_CR32","doi-asserted-by":"crossref","unstructured":"Pawar, R., Raje, R.R.: Multilingual cyberbullying detection system. In: 2019 IEEE International Conference on Electro Information Technology (EIT), pp. 040\u2013044. IEEE (2019)","DOI":"10.1109\/EIT.2019.8833846"},{"key":"672_CR33","doi-asserted-by":"crossref","unstructured":"Arreerard, R., Senivongse, T.: Thai defamatory text classification on social media. In: 2018 IEEE International Conference on Big Data, Cloud Computing, Data Science and Engineering (BCD), pp. 73\u201378. IEEE (2018)","DOI":"10.1109\/BCD2018.2018.00019"},{"key":"672_CR34","doi-asserted-by":"crossref","unstructured":"Tarwani, S., Jethanandani, M., Kant, V.: Cyberbullying detection in Hindi\u2013English code-mixed language using sentiment classification. In: International Conference on Advances in Computing and Data Sciences, pp. 543\u2013551. Springer, Singapore (2019)","DOI":"10.1007\/978-981-13-9942-8_51"},{"key":"672_CR35","doi-asserted-by":"crossref","unstructured":"Bohra, A., Vijay, D., Singh, V., Akhtar, S.S., Shrivastava, M.: A dataset of Hindi\u2013English 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":"672_CR36","doi-asserted-by":"crossref","unstructured":"Singh, V., Varshney, A., Akhtar, S. S., Vijay, D., Shrivastava, M.: Aggression detection on social media text using deep neural networks. In: Proceedings of the 2nd Workshop on Abusive Language Online (ALW2) ,pp. 43\u201350 (2018)","DOI":"10.18653\/v1\/W18-5106"},{"key":"672_CR37","doi-asserted-by":"crossref","unstructured":"Santosh, T.Y.S.S., Aravind, K.V.S.: Hate speech detection in Hindi\u2013English code-mixed social media text. In: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data, pp. 310\u2013313 (2019)","DOI":"10.1145\/3297001.3297048"},{"key":"672_CR38","unstructured":"Gupta, V.K.: \u201cHinglish\u201d language-modeling a messy code-mixed language. arXiv preprint. arXiv:1912.13109 (2019)"},{"key":"672_CR39","doi-asserted-by":"crossref","unstructured":"Haidar, B., Chamoun, M., Yamout, F.: Cyberbullying detection: a survey on multilingual techniques. In: 2016 European Modelling Symposium (EMS), pp. 165\u2013171. IEEE (2016)","DOI":"10.1109\/EMS.2016.037"},{"key":"672_CR40","doi-asserted-by":"crossref","unstructured":"Al-Hassan, A., Al-Dossari, H.: Detection of hate speech in social networks: a survey on multilingual corpus. In: 6th International Conference on Computer Science and Information Technology (2019)","DOI":"10.5121\/csit.2019.90208"},{"issue":"3","key":"672_CR41","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/MCI.2018.2840738","volume":"13","author":"T Young","year":"2018","unstructured":"Young, T., Hazarika, D., Poria, S., Cambria, E.: Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13(3), 55\u201375 (2018)","journal-title":"IEEE Comput. Intell. Mag."},{"key":"672_CR42","unstructured":"Araci, D.: FinBERT: financial sentiment analysis with pre-trained language models. arXiv preprint. arXiv:1908.10063 (2019)"},{"key":"672_CR43","unstructured":"Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Advances in Neural Information Processing Systems, pp. 3856\u20133866 (2017)"},{"issue":"1","key":"672_CR44","doi-asserted-by":"publisher","first-page":"102141","DOI":"10.1016\/j.ipm.2019.102141","volume":"57","author":"A Kumar","year":"2020","unstructured":"Kumar, A., Srinivasan, K., Cheng, W.H., Zomaya, A.Y.: Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data. Inf. Process. Manag. 57(1), 102141 (2020)","journal-title":"Inf. Process. Manag."},{"key":"672_CR45","doi-asserted-by":"crossref","unstructured":"Loper, E., Bird, S.: NLTK: The natural language toolkit. In: Proceedings of the ACL-02 Workshop on Effective Tools and Methodologies for Teaching Natural Language Processing and Computational Linguistics, vol. 1, pp. 63\u201370. Association for Computational Linguistics (2002)","DOI":"10.3115\/1118108.1118117"},{"issue":"4","key":"672_CR46","first-page":"599","volume":"24","author":"K Knight","year":"1998","unstructured":"Knight, K., Graehl, J.: Machine transliteration. Comput. Linguist. 24(4), 599\u2013612 (1998)","journal-title":"Comput. Linguist."},{"issue":"20","key":"672_CR47","doi-asserted-by":"publisher","first-page":"29529","DOI":"10.1007\/s11042-019-7278-0","volume":"78","author":"A Kumar","year":"2019","unstructured":"Kumar, A., Jaiswal, A.: Swarm intelligence based optimal feature selection for enhanced predictive sentiment accuracy on Twitter. Multimed. Tools Appl. 78(20), 29529\u201329553 (2019)","journal-title":"Multimed. Tools Appl."},{"key":"672_CR48","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"672_CR49","unstructured":"Zhao, W., Ye, J., Yang, M., Lei, Z., Zhang, S., Zhao, Z.: Investigating capsule networks with dynamic routing for text classification. arXiv preprint. arXiv:1804.00538 (2018)"},{"key":"672_CR50","doi-asserted-by":"crossref","unstructured":"Graves, A., Jaitly, N., Mohamed, A.R.: Hybrid speech recognition with deep bidirectional LSTM. In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, pp. 273\u2013278. IEEE (2013)","DOI":"10.1109\/ASRU.2013.6707742"},{"key":"672_CR51","doi-asserted-by":"crossref","unstructured":"Srivastava, S., Khurana, P., Tewari, V.: Identifying aggression and toxicity in comments using capsule network. In: Proceedings of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC-2018), pp. 98\u2013105 (2018)","DOI":"10.18653\/v1\/W19-3517"}],"container-title":["Multimedia Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-020-00672-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00530-020-00672-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00530-020-00672-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T00:18:11Z","timestamp":1669162691000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00530-020-00672-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,7,13]]},"references-count":51,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["672"],"URL":"https:\/\/doi.org\/10.1007\/s00530-020-00672-7","relation":{},"ISSN":["0942-4962","1432-1882"],"issn-type":[{"value":"0942-4962","type":"print"},{"value":"1432-1882","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,7,13]]},"assertion":[{"value":"13 July 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}