{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:33:59Z","timestamp":1742913239420,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031041112"},{"type":"electronic","value":"9783031041129"}],"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\/978-3-031-04112-9_14","type":"book-chapter","created":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T17:17:13Z","timestamp":1649783833000},"page":"187-200","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Islamophobic Hate Speech Detection from\u00a0Electronic Media Using Deep Learning"],"prefix":"10.1007","author":[{"given":"Qasim","family":"Mehmood","sequence":"first","affiliation":[]},{"given":"Anum","family":"Kaleem","sequence":"additional","affiliation":[]},{"given":"Imran","family":"Siddiqi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,13]]},"reference":[{"key":"14_CR1","unstructured":"Shield for Muslims (31), 18 July 2021. https:\/\/shieldformuslims.wordpress.com\/"},{"key":"14_CR2","unstructured":"Trust, R.: Islamophobia: a challenge for us all. Runnymede Trust UK 39(11). www.runnymedetrust.org\/uploads\/publications\/pdfs\/islamophobia.pdf"},{"key":"14_CR3","doi-asserted-by":"crossref","unstructured":"Ruwandika, N., Weerasinghe, A.: Identification of hate speech in social media. In: 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer), pp. 273\u2013278 (2018). IEEE","DOI":"10.1109\/ICTER.2018.8615517"},{"key":"14_CR4","unstructured":"Inc, Y.: Youtube inc. youtube community guidelines [online]. Soc. Media Usage Policy 25, 3389\u20133402 (2020)"},{"key":"14_CR5","unstructured":"Inc, T.: Twitter inc. the twitter rules [online]. Twitter Usage Policy 9, 3389\u20133402 (2020)"},{"key":"14_CR6","unstructured":"Inc, F.: Facebook inc. facebook comment policy [online]. Facebook Usage Policy 6, 3389\u20133402 (2020)"},{"issue":"1","key":"14_CR7","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1515\/lpp-2018-0003","volume":"14","author":"M KhosraviNik","year":"2018","unstructured":"KhosraviNik, M., Esposito, E.: Online hate, digital discourse and critique: exploring digitally-mediated discursive practices of gender-based hostility. Lodz Pap. Pragmat. 14(1), 45\u201368 (2018)","journal-title":"Lodz Pap. Pragmat."},{"issue":"2","key":"14_CR8","first-page":"132","volume":"12","author":"K Weston-Scheuber","year":"2012","unstructured":"Weston-Scheuber, K.: Gender and the prohibition of hate speech. QUT Law Justice J. 12(2), 132\u201350 (2012)","journal-title":"QUT Law Justice J."},{"issue":"4","key":"14_CR9","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1111\/1471-6402.00110","volume":"27","author":"G Cowan","year":"2003","unstructured":"Cowan, G., Khatchadourian, D.: Empathy, ways of knowing, and interdependence as mediators of gender differences in attitudes toward hate speech and freedom of speech. Psychol. Women Q. 27(4), 300\u2013308 (2003)","journal-title":"Psychol. Women Q."},{"key":"14_CR10","doi-asserted-by":"crossref","unstructured":"Fr\u00edas-V\u00e1zquez, M., Arcila, C.: Hate speech against central American immigrants in Mexico: analysis of xenophobia and racism in politicians, media and citizens, pp. 956\u2013960 (2019)","DOI":"10.1145\/3362789.3362850"},{"key":"14_CR11","first-page":"805","volume":"32","author":"TK Hern\u00e1ndez","year":"2010","unstructured":"Hern\u00e1ndez, T.K.: Hate speech and the language of racism in Latin America: a lens for reconsidering global hate speech restrictions and legislation models. U. Pa. J. Int\u2019l L. 32, 805 (2010)","journal-title":"U. Pa. J. Int\u2019l L."},{"issue":"6","key":"14_CR12","doi-asserted-by":"publisher","first-page":"930","DOI":"10.1080\/1369118X.2017.1293130","volume":"20","author":"A Matamoros-Fern\u00e1ndez","year":"2017","unstructured":"Matamoros-Fern\u00e1ndez, A.: Platformed racism: The mediation and circulation of an Australian race-based controversy on twitter, facebook and youtube. Inf. Commun. Soc. 20(6), 930\u2013946 (2017)","journal-title":"Inf. Commun. Soc."},{"key":"14_CR13","doi-asserted-by":"crossref","unstructured":"Saksesi, A.S., Nasrun, M., Setianingsih, C.: Analysis text of hate speech detection using recurrent neural network. In: 2018 International Conference on Control, Electronics, Renewable Energy and Communications (ICCEREC), pp. 242\u2013248. IEEE (2018)","DOI":"10.1109\/ICCEREC.2018.8712104"},{"issue":"2","key":"14_CR14","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1177\/1468796817692626","volume":"17","author":"M Bonotti","year":"2017","unstructured":"Bonotti, M.: Religion, hate speech and non-domination. Ethnicities 17(2), 259\u2013274 (2017)","journal-title":"Ethnicities"},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"ElSherief, M., Kulkarni, V., Nguyen, D., Wang, W.Y., Belding, E.: Hate lingo: a target-based linguistic analysis of hate speech in social media. arXiv preprint arXiv:1804.04257 (2018)","DOI":"10.1609\/icwsm.v12i1.15041"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Yasseri, T., Vidgen, B.: Detecting weak and strong islamophobic hate speech on social media. J. Inf. Technol. Polit. 2019 17(1) (2019)","DOI":"10.1080\/19331681.2019.1702607"},{"issue":"4","key":"14_CR17","doi-asserted-by":"publisher","first-page":"419","DOI":"10.1007\/s10982-017-9297-1","volume":"36","author":"A Brown","year":"2017","unstructured":"Brown, A.: What is hate speech? part 1: the myth of hate. Law Philos. 36(4), 419\u2013468 (2017)","journal-title":"Law Philos."},{"issue":"1","key":"14_CR18","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1111\/j.1460-2466.1997.tb02690.x","volume":"47","author":"C Calvert","year":"1997","unstructured":"Calvert, C.: Hate speech and its harms: a communication theory perspective. J. Commun. 47(1), 4\u201319 (1997)","journal-title":"J. Commun."},{"key":"14_CR19","doi-asserted-by":"crossref","unstructured":"Al-Hassan, A., Al-Dossari, H.: Detection of hate speech in social networks: a survey on multilingual corpus (2019)","DOI":"10.5121\/csit.2019.90208"},{"key":"14_CR20","unstructured":"Gaydhani, A., Doma, V., Kendre, S., Bhagwat, L.: Detecting hate speech and offensive language on twitter using machine learning: An n-gram and tfidf based approach. arXiv preprint arXiv:1809.08651 (2018)"},{"issue":"3","key":"14_CR21","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1007\/s10791-010-9161-5","volume":"14","author":"K Sarvabhotla","year":"2011","unstructured":"Sarvabhotla, K., Pingali, P., Varma, V.: Sentiment classification: a lexical similarity based approach for extracting subjectivity in documents. Inf. Retr. 14(3), 337\u2013353 (2011)","journal-title":"Inf. Retr."},{"issue":"2\u20133","key":"14_CR22","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1080\/016909697386826","volume":"12","author":"MC MacDonald","year":"1997","unstructured":"MacDonald, M.C.: Lexical representations and sentence processing: an introduction. Lang. Cogn. Process. 12(2\u20133), 121\u2013136 (1997)","journal-title":"Lang. Cogn. Process."},{"key":"14_CR23","doi-asserted-by":"crossref","unstructured":"Gitari, N.D., Zuping, Z., Damien, H., Long, J.: A lexicon-based approach for hate speech detection. Int. J. Multimed. Ubiquitous Eng. 2015 10(4), 215\u2013230 (2015)","DOI":"10.14257\/ijmue.2015.10.4.21"},{"key":"14_CR24","doi-asserted-by":"crossref","unstructured":"Davidson, T., Warmsley, D., Macy, M., Weber, I.: Automated hate speech detection and the problem of offensive language. arXiv preprint arXiv:1703.04009 (2017)","DOI":"10.1609\/icwsm.v11i1.14955"},{"key":"14_CR25","doi-asserted-by":"crossref","unstructured":"\u015eahi, H., K\u0131l\u0131\u00e7, Y., Sa\u01e7lam, R.B.: Automated detection of hate speech towards woman on twitter. 2018 3rd International Conference on Computer Science and Engineering (UBMK) 2018, pp. 533\u2013536. IEEE (2018)","DOI":"10.1109\/UBMK.2018.8566304"},{"key":"14_CR26","doi-asserted-by":"crossref","unstructured":"Wester, A., \u00d8vrelid, L., Velldal, E., Hammer, H.L.: Threat detection in online discussions. In: Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pp. 66\u201371 (2016)","DOI":"10.18653\/v1\/W16-0413"},{"key":"14_CR27","doi-asserted-by":"crossref","unstructured":"Hegde, S.U., Zaiba, A., Nagaraju, Y., et al.: Hybrid CNN-LSTM model with glove word vector for sentiment analysis on football specific tweets, pp. 1\u20138. IEEE (2021)","DOI":"10.1109\/ICAECT49130.2021.9392516"},{"issue":"11","key":"14_CR28","doi-asserted-by":"publisher","first-page":"2298","DOI":"10.1109\/TPAMI.2016.2646371","volume":"39","author":"B Shi","year":"2016","unstructured":"Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2298\u20132304 (2016)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"14_CR29","unstructured":"Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep learning 1 (2016)"},{"key":"14_CR30","doi-asserted-by":"crossref","unstructured":"Mengistie, T.T., Kumar, D.: Deep learning based sentiment analysis on COVID-19 public reviews, pp. 444\u2013449. IEEE (2021)","DOI":"10.1109\/ICAIIC51459.2021.9415191"},{"key":"14_CR31","doi-asserted-by":"crossref","unstructured":"Vimali, J., Murugan, S.: A text based sentiment analysis model using bi-directional LSTM networks, pp. 1652\u20131658. IEEE (2021)","DOI":"10.1109\/ICCES51350.2021.9489129"},{"key":"14_CR32","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"}],"container-title":["Communications in Computer and Information Science","Pattern Recognition and Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-04112-9_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,22]],"date-time":"2024-09-22T01:41:16Z","timestamp":1726969276000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-04112-9_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031041112","9783031041129"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-04112-9_14","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":"13 April 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MedPRAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Mediterranean Conference on Pattern Recognition and Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Instanbul","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turkey","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":"17 December 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 December 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"medprai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/medprai2021.sciencesconf.org\/","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":"72","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":"28","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":"4","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":"39% - 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":"4.23","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":"Due to the COVID-19 pandemic, MedPRAI 2021 was held fully online.","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)"}}]}}