{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T14:04:43Z","timestamp":1782828283758,"version":"3.54.5"},"reference-count":83,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"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":["Int J Speech Technol"],"published-print":{"date-parts":[[2024,9]]},"DOI":"10.1007\/s10772-024-10135-3","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T11:03:18Z","timestamp":1725015798000},"page":"793-815","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Explainable hate speech detection using LIME"],"prefix":"10.1007","volume":"27","author":[{"given":"Joan L.","family":"Imbwaga","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nagaratna B.","family":"Chittaragi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shashidhar G.","family":"Koolagudi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"issue":"2","key":"10135_CR1","doi-asserted-by":"publisher","first-page":"81","DOI":"10.48161\/qaj.v1n2a50","volume":"1","author":"DM Abdullah","year":"2021","unstructured":"Abdullah, D. M., & Abdulazeez, A. M. (2021). Machine learning applications based on SVM classification a review. Qubahan Academic Journal, 1(2), 81\u201390.","journal-title":"Qubahan Academic Journal"},{"issue":"2","key":"10135_CR2","first-page":"51","volume":"12","author":"M Ahmed","year":"2022","unstructured":"Ahmed, M., Hossain, M. S., Islam, R. U., & Andersson, K. (2022). Explainable text classification model for COVID-19 fake news detection. Journal of Internet Services and Information Security, 12(2), 51\u201369.","journal-title":"Journal of Internet Services and Information Security"},{"key":"10135_CR6","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.procs.2018.10.473","volume":"142","author":"A Alakrot","year":"2018","unstructured":"Alakrot, A., Murray, L., & Nikolov, N. S. (2018). Dataset construction for the detection of anti-social behaviour in online communication in Arabic. Procedia Computer Science, 142, 174\u2013181.","journal-title":"Procedia Computer Science"},{"key":"10135_CR7","doi-asserted-by":"publisher","first-page":"1150840","DOI":"10.3389\/fonc.2023.1150840","volume":"13","author":"MAA Albadr","year":"2023","unstructured":"Albadr, M. A. A., Ayob, M., Tiun, S., Al-Dhief, F. T., Arram, A., & Khalaf, S. (2023). Breast cancer diagnosis using the fast learning network algorithm. Frontiers in Oncology, 13, 1150840.","journal-title":"Frontiers in Oncology"},{"key":"10135_CR8","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.925901","volume":"10","author":"MAA Albadr","year":"2022","unstructured":"Albadr, M. A. A., Ayob, M., Tiun, S., Al-Dhief, F. T., & Hasan, M. K. (2022). Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection. Frontiers in Public Health, 10, 925901.","journal-title":"Frontiers in Public Health"},{"issue":"11","key":"10135_CR9","doi-asserted-by":"publisher","first-page":"1758","DOI":"10.3390\/sym12111758","volume":"12","author":"MAA Albadr","year":"2020","unstructured":"Albadr, M. A. A., Tiun, S., Ayob, M., & Al-Dhief, F. (2020). Genetic algorithm based on natural selection theory for optimization problems. Symmetry, 12(11), 1758.","journal-title":"Symmetry"},{"issue":"17","key":"10135_CR10","doi-asserted-by":"publisher","first-page":"23963","DOI":"10.1007\/s11042-022-12747-w","volume":"81","author":"MAA Albadr","year":"2022","unstructured":"Albadr, M. A. A., Tiun, S., Ayob, M., Al-Dhief, F. T., Omar, K., & Maen, M. K. (2022). Speech emotion recognition using optimized genetic algorithm-extreme learning machine. Multimedia Tools and Applications, 81(17), 23963\u201323989.","journal-title":"Multimedia Tools and Applications"},{"key":"10135_CR4","doi-asserted-by":"crossref","unstructured":"Al-Dhief, F. T., Latiff, N. M. A., Baki, M. M., Malik, N. N. N. A., Sabri, N., & Albadr, M. A. A. (2021). Voice pathology detection using support vector machine based on different number of voice signals. In 2021 26th IEEE Asia\u2013Pacific conference on communications (APCC 2021) (pp. 1\u20136). IEEE.","DOI":"10.1109\/APCC49754.2021.9609830"},{"key":"10135_CR3","doi-asserted-by":"crossref","unstructured":"Al-Dhief, F. T., Latiff, N. M. A., Malik, N. N. N. A., Baki, M. M., Sabri, N., & Albadr, M. A. A. (2022). Dysphonia detection based on voice signals using Naive Bayes classifier. In 2022 IEEE 6th international symposium on telecommunication technologies (ISTT 2022) (pp. 56\u201361). IEEE.","DOI":"10.1109\/ISTT56288.2022.9966535"},{"key":"10135_CR11","doi-asserted-by":"crossref","unstructured":"Alfina, I., Mulia, R., Fanany, M. I., & Ekanata, Y. (2017). Hate speech detection in the Indonesian language: A dataset and preliminary study. In 2017 International conference on advanced computer science and information systems (ICACSIS 2017) (pp. 233\u2013238). IEEE.","DOI":"10.1109\/ICACSIS.2017.8355039"},{"key":"10135_CR5","doi-asserted-by":"crossref","unstructured":"Al-Hassan, A., & Al-Dossari, H. (2019). Detection of hate speech in social networks: A survey on multilingual corpus. In 6th International conference on computer science and information technology (COMIT 2019) (Vol. 10, pp. 10\u201321).","DOI":"10.5121\/csit.2019.90208"},{"key":"10135_CR12","unstructured":"Aluru, S. S., Mathew, B., Saha, P., & Mukherjee, A. (2020). Deep learning models for multilingual hate speech detection. arXiv preprint. arXiv:2004.06465"},{"issue":"5","key":"10135_CR13","doi-asserted-by":"publisher","first-page":"4829","DOI":"10.1016\/j.aej.2021.03.052","volume":"60","author":"M Aminu","year":"2021","unstructured":"Aminu, M., Ahmad, N. A., & Noor, M. H. M. (2021). COVID-19 detection via deep neural network and occlusion sensitivity maps. Alexandria Engineering Journal, 60(5), 4829\u20134855.","journal-title":"Alexandria Engineering Journal"},{"key":"10135_CR14","unstructured":"Arram, A., Ayob, M., Albadr, M. A. A., Sulaiman, A., & Albashish, D. (2023). Credit card score prediction using machine learning models: A new dataset. arXiv preprint. arXiv:2310.02956"},{"key":"10135_CR15","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1016\/j.procs.2023.01.444","volume":"219","author":"LT Ava","year":"2023","unstructured":"Ava, L. T., Karim, A., Hassan, M. M., Faisal, F., Azam, S., Al Haque, A. F., & Zaman, S. (2023). Intelligent identification of hate speeches to address the increased rate of individual mental degeneration. Procedia Computer Science, 219, 1527\u20131537.","journal-title":"Procedia Computer Science"},{"key":"10135_CR16","doi-asserted-by":"crossref","unstructured":"Badjatiya, P., Gupta, S., Gupta, M., & Varma, V. (2017). Deep learning for hate speech detection in tweets. In Proceedings of the 26th international conference on World Wide Web companion, 2017 (pp. 759\u2013760).","DOI":"10.1145\/3041021.3054223"},{"key":"10135_CR17","first-page":"1","volume-title":"Modern information retrieval","author":"R Baeza-Yates","year":"1999","unstructured":"Baeza-Yates, R., Ribeiro-Neto, B. (1999). Modern information retrieval (Vol. 463, pp. 1\u2013500). ACM Press."},{"key":"10135_CR18","unstructured":"Benesch, S. (2012). Dangerous speech: A proposal to prevent group violence. Voices That Poison: Dangerous Speech Project."},{"key":"10135_CR19","unstructured":"Biere, S., Bhulai, S., & Master Business Analytics. (2018). Hate speech detection using natural language processing techniques. Master Business Analytics, Department of Mathematics, Faculty of Science."},{"key":"10135_CR20","unstructured":"Boersma, P. (2011). PRAAT: Doing phonetics by computer (computer program). http:\/\/www.praat.org\/"},{"key":"10135_CR21","unstructured":"Brownlee, J. (2016). Deep learning with Python: Develop deep learning models on Theano and TensorFlow using Keras. Machine Learning Mastery."},{"issue":"3","key":"10135_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3641289","volume":"15","author":"Y Chang","year":"2024","unstructured":"Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., Chen, H., Yi, X., Wang, C., Wang, Y., (2024). A survey on evaluation of large language models. ACM Transactions on Intelligent Systems and Technology, 15(3), 1\u201345.","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"10135_CR23","doi-asserted-by":"crossref","unstructured":"Davidson, T., Warmsley, D., Macy, M., & Weber, I. (2017). Automated hate speech detection and the problem of offensive language. In Proceedings of the international AAAI conference on web and social media ((ICWSM 2017) (Vol. 11, pp. 512\u2013515).","DOI":"10.1609\/icwsm.v11i1.14955"},{"key":"10135_CR24","doi-asserted-by":"crossref","unstructured":"Debele, A. G., & Woldeyohannis, M. M. (2022). Multimodal Amharic hate speech detection using deep learning. In 2022 International conference on information and communication technology for development for Africa (ICT4DA 2022) (pp. 102\u2013107). IEEE.","DOI":"10.1109\/ICT4DA56482.2022.9971436"},{"key":"10135_CR25","unstructured":"Del Vigna, F., Cimino, A., Dell\u2019Orletta, F., Petrocchi, M., & Tesconi, M. (2017). Hate me, hate me not: Hate speech detection on Facebook. In Proceedings of the first Italian conference on cybersecurity (ITASEC17) (pp. 86\u201395)."},{"key":"10135_CR26","unstructured":"Des Forges, A. (1999). Leave none to tell the story (New York: Human Rights Watch, 1999). Cited in Wertheim, \u2018A solution from hell\u2019 (Vol. 169, pp. 209\u2013211)."},{"key":"10135_CR27","unstructured":"Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint. arXiv:1702.08608"},{"issue":"4","key":"10135_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3232676","volume":"51","author":"P Fortuna","year":"2018","unstructured":"Fortuna, P., & Nunes, S. (2018). A survey on automatic detection of hate speech in text. ACM Computing Surveys (CSUR), 51(4), 1\u201330.","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"10135_CR30","doi-asserted-by":"crossref","unstructured":"Fortuna, Paula, Rocha da Silva, Jo\u00e3o, Soler-Company, Juan, Wanner, Leo  and       Nunes, S\u00e9rgio, (2019). A hierarchically-labeled Portuguese hate speech dataset. In Proceedings of the third workshop on abusive language online, 2019 (pp. 94\u2013104).","DOI":"10.18653\/v1\/W19-3510"},{"issue":"8","key":"10135_CR31","doi-asserted-by":"publisher","first-page":"13562","DOI":"10.1111\/exsy.13562","volume":"41","author":"A Gandhi","year":"2024","unstructured":"Gandhi, A., Ahir, P., Adhvaryu, K., Shah, P., Lohiya, R., Cambria, E., Poria, S., & Hussain, A. (2024). Hate speech detection: A comprehensive review of recent works. Expert Systems, 41(8), 13562.","journal-title":"Expert Systems"},{"key":"10135_CR32","doi-asserted-by":"crossref","unstructured":"Gao, L., & Huang, R. (2017). Detecting online hate speech using context aware models. arXiv preprint. arXiv:1710.07395","DOI":"10.26615\/978-954-452-049-6_036"},{"key":"10135_CR33","unstructured":"Gaydhani, A., Doma, V., Kendre, S., & Bhagwat, L. (2018). Detecting hate speech and offensive language on twitter using machine learning: An N-gram and TFIDF based approach. arXiv preprint. arXiv:1809.08651"},{"key":"10135_CR34","doi-asserted-by":"crossref","unstructured":"Ghimire, A., Thapa, S., Jha, A. K., Adhikari, S., & Kumar, A. (2020). Accelerating business growth with big data and artificial intelligence. In 2020 Fourth international conference on I-SMAC (IoT in social, mobile, analytics and cloud) (I-SMAC 2020) (pp. 441\u2013448). IEEE.","DOI":"10.1109\/I-SMAC49090.2020.9243318"},{"key":"10135_CR35","doi-asserted-by":"crossref","unstructured":"Ghimire, A., Thapa, S., Jha, A. K., Kumar, A., Kumar, A., & Adhikari, S. (2020). AI and IoT solutions for tackling COVID-19 pandemic. In 2020 4th International conference on electronics, communication and aerospace technology (ICECA 2020) (pp. 1083\u20131092). IEEE.","DOI":"10.1109\/ICECA49313.2020.9297454"},{"key":"10135_CR36","unstructured":"Ghosh, S., Burachas, G., Ray, A., & Ziskind, A. (2019). Generating natural language explanations for visual question answering using scene graphs and visual attention. arXiv preprint. arXiv:1902.05715"},{"issue":"4","key":"10135_CR37","doi-asserted-by":"publisher","first-page":"215","DOI":"10.14257\/ijmue.2015.10.4.21","volume":"10","author":"ND Gitari","year":"2015","unstructured":"Gitari, N. D., Zuping, Z., Damien, H., & Long, J. (2015). A lexicon-based approach for hate speech detection. International Journal of Multimedia and Ubiquitous Engineering, 10(4), 215\u2013230.","journal-title":"International Journal of Multimedia and Ubiquitous Engineering"},{"key":"10135_CR38","unstructured":"Google. (2021). YouTube data API."},{"key":"10135_CR39","doi-asserted-by":"crossref","unstructured":"Gorski, L., Ramakrishna, S., & Nowosielski, J. M. (2020). Towards Grad-CAM based explainability in a legal text processing pipeline. arXiv preprint. arXiv:2012.09603","DOI":"10.1007\/978-3-030-89811-3_11"},{"key":"10135_CR40","unstructured":"Hatzipanagos, R. (2018). How online hate turns into real-life violence. The Washington Post."},{"key":"10135_CR41","doi-asserted-by":"crossref","unstructured":"Iba\u00f1ez, M., Sapinit, R., Reyes, L. A., Hussien, M., Imperial, J. M., & Rodriguez, R. (2021). Audio-based hate speech classification from online short-form videos. In 2021 international conference on Asian language processing (IALP 2021) (pp. 72\u201377). IEEE.","DOI":"10.1109\/IALP54817.2021.9675250"},{"issue":"2","key":"10135_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10772-024-10116-6","volume":"27","author":"JL Imbwaga","year":"2024","unstructured":"Imbwaga, J. L., Chittaragi, N. B., & Koolagudi, S. G. (2024). Automatic hate speech detection in audio using machine learning algorithms. International Journal of Speech Technology, 27(2), 1\u201323.","journal-title":"International Journal of Speech Technology"},{"key":"10135_CR43","doi-asserted-by":"crossref","unstructured":"Joo, H.-T., & Kim, K.-J. (2019). Visualization of deep reinforcement learning using Grad-CAM: How AI plays Atari games? In 2019 IEEE conference on games (CoG 2019) (pp. 1\u20132). IEEE.","DOI":"10.1109\/CIG.2019.8847950"},{"key":"10135_CR44","doi-asserted-by":"crossref","unstructured":"Junaid, M. I. H., Hossain, F., & Rahman, R. M. (2021). Bangla hate speech detection in videos using machine learning. In 2021 IEEE 12th annual ubiquitous computing, electronics and mobile communication conference (UEMCON 2021) (pp. 0347\u20130351). IEEE.","DOI":"10.1109\/UEMCON53757.2021.9666550"},{"key":"10135_CR45","unstructured":"Kandakatla, R. (2016). Identifying offensive videos on YouTube. PhD Thesis, Wright State University."},{"key":"10135_CR46","doi-asserted-by":"crossref","unstructured":"Kanehira, A., Takemoto, K., Inayoshi, S., & Harada, T. (2019). Multimodal explanations by predicting counterfactuality in videos. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, (CVPR 2019) (pp. 8594\u20138602).","DOI":"10.1109\/CVPR.2019.00879"},{"key":"10135_CR47","doi-asserted-by":"crossref","unstructured":"Karim, M. R., Dey, S. K., Islam, T., Shajalal, M., & Chakravarthi, B. R. (2022). Multimodal hate speech detection from Bengali memes and texts. In International conference on speech and language technologies for low-resource languages, (SPELLL 2022) (pp. 293\u2013308). Springer.","DOI":"10.1007\/978-3-031-33231-9_21"},{"key":"10135_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2024.109153","volume":"116","author":"H Kibriya","year":"2024","unstructured":"Kibriya, H., Siddiqa, A., Khan, W. Z., & Khan, M. K. (2024). Towards safer online communities: Deep learning and explainable AI for hate speech detection and classification. Computers and Electrical Engineering, 116, 109153.","journal-title":"Computers and Electrical Engineering"},{"key":"10135_CR49","first-page":"2611","volume":"33","author":"D Kiela","year":"2020","unstructured":"Kiela, D., Firooz, H., Mohan, A., Goswami, V., Singh, A., Ringshia, P., & Testuggine, D. (2020). The hateful memes challenge: Detecting hate speech in multimodal memes. Advances in Neural Information Processing Systems, 33, 2611\u20132624.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"10135_CR50","unstructured":"Koroteev, M. V. (2021). BERT: A review of applications in natural language processing and understanding. arXiv preprint. arXiv:2103.11943"},{"key":"10135_CR51","doi-asserted-by":"crossref","unstructured":"Malmasi, S., & Zampieri, M. (2017). Detecting hate speech in social media. arXiv preprint. arXiv:1712.06427","DOI":"10.26615\/978-954-452-049-6_062"},{"issue":"6","key":"10135_CR52","doi-asserted-by":"publisher","first-page":"3077","DOI":"10.1007\/s00521-023-09169-6","volume":"36","author":"F Mehmood","year":"2024","unstructured":"Mehmood, F., Ghafoor, H., Asim, M. N., Ghani, M. U., Mahmood, W., & Dengel, A. (2024). Passion-Net: A robust precise and explainable predictor for hate speech detection in Roman Urdu text. Neural Computing and Applications, 36(6), 3077\u20133100.","journal-title":"Neural Computing and Applications"},{"key":"10135_CR53","unstructured":"Montariol, S., Riabi, A., & Seddah, D. (2022). Multilingual auxiliary tasks training: Bridging the gap between languages for zero-shot transfer of hate speech detection models. arXiv preprint. arXiv:2210.13029"},{"issue":"3","key":"10135_CR54","doi-asserted-by":"publisher","first-page":"1493","DOI":"10.11591\/ijeecs.v21.i3.pp1493-1502","volume":"21","author":"L Mookdarsanit","year":"2021","unstructured":"Mookdarsanit, L., & Mookdarsanit, P. (2021). Combating the hate speech in Thai textual memes. Indonesian Journal of Electrical Engineering and Computer Science, 21(3), 1493\u20131502.","journal-title":"Indonesian Journal of Electrical Engineering and Computer Science"},{"key":"10135_CR55","doi-asserted-by":"crossref","unstructured":"Mossie, Z., & Wang, J.-H. (2018). Social network hate speech detection for Amharic language. In Computer science and information technology, (CS & IT-CSCP2018) (pp. 41\u201355).","DOI":"10.5121\/csit.2018.80604"},{"key":"10135_CR56","unstructured":"Navlani, A. (2018). Understanding random forests classifiers in Python. DataCamp."},{"key":"10135_CR57","doi-asserted-by":"crossref","unstructured":"Ombui, E., Muchemi, L., & Wagacha, P. (2019). Hate speech detection in code-switched text messages. In 2019 3rd international symposium on multidisciplinary studies and innovative technologies (ISMSIT 2019) (pp. 1\u20136). IEEE.","DOI":"10.1109\/ISMSIT.2019.8932845"},{"key":"10135_CR58","doi-asserted-by":"publisher","first-page":"21496","DOI":"10.1109\/ACCESS.2020.2968173","volume":"8","author":"O Oriola","year":"2020","unstructured":"Oriola, O., & Kotz\u00e9, E. (2020). Evaluating machine learning techniques for detecting offensive and hate speech in South African tweets. IEEE Access, 8, 21496\u201321509.","journal-title":"IEEE Access"},{"key":"10135_CR59","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., (2011). Scikit-learn: Machine learning in Python. The Journal of Machine Learning Research, 12, 2825\u20132830.","journal-title":"The Journal of Machine Learning Research"},{"key":"10135_CR60","doi-asserted-by":"crossref","unstructured":"Pelle, R. P., & Moreira, V. P. (2017). Offensive comments in the Brazilian web: A dataset and baseline results. In Anais do VI Brazilian workshop on social network analysis and mining, 2017. SBC.","DOI":"10.5753\/brasnam.2017.3260"},{"issue":"7","key":"10135_CR61","doi-asserted-by":"publisher","first-page":"34","DOI":"10.3390\/mti5070034","volume":"5","author":"K Perifanos","year":"2021","unstructured":"Perifanos, K., & Goutsos, D. (2021). Multimodal hate speech detection in Greek social media. Multimodal Technologies and Interaction, 5(7), 34.","journal-title":"Multimodal Technologies and Interaction"},{"issue":"9","key":"10135_CR62","first-page":"2577","volume":"12","author":"V Preethi","year":"2021","unstructured":"Preethi, V., et al. (2021). Survey on text transformation using Bi-LSTM in natural language processing with text data. Turkish Journal of Computer and Mathematics Education, 12(9), 2577\u20132585.","journal-title":"Turkish Journal of Computer and Mathematics Education"},{"key":"10135_CR63","doi-asserted-by":"crossref","unstructured":"Putra, I. G. M., & Nurjanah, D. (2020). Hate speech detection in Indonesian language Instagram. In 2020 international conference on advanced computer science and information systems (ICACSIS 2020) (pp. 413\u2013420). IEEE.","DOI":"10.1109\/ICACSIS51025.2020.9263084"},{"key":"10135_CR64","unstructured":"Rakotomamonjy, A. (2004). Optimizing area under ROC curve with SVMs. In ROCAI, 2004 (pp. 71\u201380)."},{"key":"10135_CR65","unstructured":"Rana, A., & Jha, S. (2022). Emotion based hate speech detection using multimodal learning. arXiv preprint. arXiv:2202.06218"},{"issue":"2","key":"10135_CR66","doi-asserted-by":"publisher","first-page":"76","DOI":"10.1109\/MIS.2019.2899143","volume":"34","author":"JC Reis","year":"2019","unstructured":"Reis, J. C., Correia, A., Murai, F., Veloso, A., & Benevenuto, F. (2019). Supervised learning for fake news detection. IEEE Intelligent Systems, 34(2), 76\u201381.","journal-title":"IEEE Intelligent Systems"},{"key":"10135_CR67","doi-asserted-by":"crossref","unstructured":"Reynolds, K., Kontostathis, A., & Edwards, L. (2011). Using machine learning to detect cyberbullying. In 2011 10th international conference on machine learning and applications and workshops, (ICMLA 2011) (Vol. 2, pp. 241\u2013244). IEEE.","DOI":"10.1109\/ICMLA.2011.152"},{"key":"10135_CR68","doi-asserted-by":"crossref","unstructured":"Romim, N., Ahmed, M., Talukder, H., & Islam, S., (2021). Hate speech detection in the Bengali language: A dataset and its baseline evaluation. In Proceedings of international joint conference on advances in computational intelligence, (IJCACI 2021) (pp. 457\u2013468). Springer.","DOI":"10.1007\/978-981-16-0586-4_37"},{"key":"10135_CR69","unstructured":"Sanguinetti, M., Poletto, F., Bosco, C., Patti, V., & Stranisci, M. (2018). An Italian Twitter corpus of hate speech against immigrants. In Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018)."},{"key":"10135_CR70","first-page":"1","volume-title":"Introduction to information retrieval","author":"H Sch\u00fctze","year":"2008","unstructured":"Sch\u00fctze, H., Manning, C. D., & Raghavan, P. (2008). Introduction to information retrieval (Vol. 39, pp. 1\u2013500). Cambridge University Press."},{"key":"10135_CR71","unstructured":"Silva, L., Mondal, M., Correa, D., Benevenuto, F., & Weber, I. (2016). Analyzing the targets of hate in online social media. In Tenth international AAAI conference on web and social media, (ICWSM-16)."},{"key":"10135_CR72","unstructured":"Suryawanshi, S., Chakravarthi, B. R., Arcan, M., & Buitelaar, P. (2020). Multimodal meme dataset (multioff) for identifying offensive content in image and text. In Proceedings of the second workshop on trolling, aggression and cyberbullying, 2020 (pp. 32\u201341)."},{"key":"10135_CR73","doi-asserted-by":"crossref","unstructured":"Tiwari, R. S. (2024). Hate speech detection using LSTM and explanation by LIME (local interpretable model-agnostic explanations). In Computational intelligence methods for sentiment analysis in natural language processing applications (pp. 93\u2013110). Elsevier.","DOI":"10.1016\/B978-0-443-22009-8.00005-7"},{"issue":"11","key":"10135_CR74","doi-asserted-by":"publisher","first-page":"4793","DOI":"10.1109\/TNNLS.2020.3027314","volume":"32","author":"E Tjoa","year":"2020","unstructured":"Tjoa, E., & Guan, C. (2020). A survey on explainable artificial intelligence (XAI): Toward medical XAI. IEEE Transactions on Neural Networks and Learning Systems, 32(11), 4793\u20134813.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"10135_CR75","doi-asserted-by":"crossref","unstructured":"Velankar, A., Patil, H., & Joshi, R. (2022). Mono vs multilingual BERT for hate speech detection and text classification: A case study in Marathi. In IAPR workshop on artificial neural networks in pattern recognition, (ANNPR 2022) (pp. 121\u2013128). Springer.","DOI":"10.1007\/978-3-031-20650-4_10"},{"key":"10135_CR76","unstructured":"Vilone, G., & Longo, L. (2020). Explainable artificial intelligence: A systematic review. arXiv preprint. arXiv:2006.00093"},{"key":"10135_CR77","doi-asserted-by":"crossref","unstructured":"Vlad, G.-A., Zaharia, G.-E., Cercel, D.-C., & Dascalu, M. (2020). UPB@ DANKMEMES: Italian memes analysis\u2014Employing visual models and graph convolutional networks for meme identification and hate speech detection. In EVALITA evaluation of NLP and speech tools for Italian, 2020 (p. 288).","DOI":"10.4000\/books.aaccademia.7360"},{"key":"10135_CR78","unstructured":"Warner, W., & Hirschberg, J. (2012). Detecting hate speech on the World Wide Web. In Proceedings of the second workshop on language in social media, (LSM'12) (pp. 19\u201326)."},{"key":"10135_CR79","doi-asserted-by":"crossref","unstructured":"Wich, M., Mosca, E., Gorniak, A., Hingerl, J., & Groh, G. (2021). Explainable abusive language classification leveraging user and network data. In Machine learning and knowledge discovery in databases. Applied data science track: European conference, ECML PKDD 2021: Proceedings, Part V 21, Bilbao, Spain, September 13\u201317, 2021 (pp. 481\u2013496). Springer.","DOI":"10.1007\/978-3-030-86517-7_30"},{"key":"10135_CR80","doi-asserted-by":"crossref","unstructured":"Wickramaarachchi, W., Subasinghe, S. S., Wijerathna, K. R. T., Athukorala, A. S. U., Abeywardhana, L., & Karunasena, A. (2023). Identifying false content and hate speech in Sinhala YouTube videos by analyzing the audio. In 2023 5th international conference on advancements in computing (ICAC 2023) (pp. 364\u2013369). IEEE.","DOI":"10.1109\/ICAC60630.2023.10417565"},{"key":"10135_CR81","unstructured":"Wiegand, M., Siegel, M., & Ruppenhofer, J. (2018). Overview of the GermEval 2018 shared task on the identification of offensive language."},{"key":"10135_CR82","doi-asserted-by":"crossref","unstructured":"Wu, C. S., & Bhandary, U. (2020). Detection of hate speech in videos using machine learning. In 2020 international conference on computational science and computational intelligence (CSCI 2020) (pp. 585\u2013590). IEEE.","DOI":"10.1109\/CSCI51800.2020.00104"},{"key":"10135_CR83","doi-asserted-by":"crossref","unstructured":"Yang, F., Peng, X., Ghosh, G., Shilon, R., Ma, H., Moore, E., & Predovic, G. (2019). Exploring deep multimodal fusion of text and photo for hate speech classification. In Proceedings of the third workshop on abusive language online, 2019 (pp. 11\u201318).","DOI":"10.18653\/v1\/W19-3502"},{"key":"10135_CR84","doi-asserted-by":"publisher","first-page":"598","DOI":"10.7717\/peerj-cs.598","volume":"7","author":"W Yin","year":"2021","unstructured":"Yin, W., & Zubiaga, A. (2021). Towards generalisable hate speech detection: A review on obstacles and solutions. PeerJ Computer Science, 7, 598.","journal-title":"PeerJ Computer Science"}],"container-title":["International Journal of Speech Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10772-024-10135-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10772-024-10135-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10772-024-10135-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T12:15:46Z","timestamp":1726143346000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10772-024-10135-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,30]]},"references-count":83,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["10135"],"URL":"https:\/\/doi.org\/10.1007\/s10772-024-10135-3","relation":{},"ISSN":["1381-2416","1572-8110"],"issn-type":[{"value":"1381-2416","type":"print"},{"value":"1572-8110","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,30]]},"assertion":[{"value":"17 May 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial or personal relationships that could be viewed as influencing the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}