{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T09:08:30Z","timestamp":1771664910483,"version":"3.50.1"},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,8,5]],"date-time":"2019-08-05T00:00:00Z","timestamp":1564963200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,8,5]],"date-time":"2019-08-05T00:00:00Z","timestamp":1564963200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soc. Netw. Anal. Min."],"published-print":{"date-parts":[[2019,12]]},"DOI":"10.1007\/s13278-019-0587-5","type":"journal-article","created":{"date-parts":[[2019,8,5]],"date-time":"2019-08-05T12:02:31Z","timestamp":1565006551000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Investigating the effect of combining GRU neural networks with handcrafted features for religious hatred detection on Arabic Twitter space"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1273-8371","authenticated-orcid":false,"given":"Nuha","family":"Albadi","sequence":"first","affiliation":[]},{"given":"Maram","family":"Kurdi","sequence":"additional","affiliation":[]},{"given":"Shivakant","family":"Mishra","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,5]]},"reference":[{"key":"587_CR1","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1016\/j.chb.2016.05.051","volume":"63","author":"MA Al-garadi","year":"2016","unstructured":"Al-garadi MA, Varathan KD, Ravana SD (2016) Cybercrime detection in online communications: the experimental case of cyberbullying detection in the twitter network. Comput Hum Behav 63:433\u2013443","journal-title":"Comput Hum Behav"},{"key":"587_CR2","doi-asserted-by":"crossref","unstructured":"Al-Twairesh N, Al-Khalifa H, AlSalman A (2016) Arasenti: large-scale twitter-specific Arabic sentiment lexicons. In: Proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: long papers), vol\u00a01, pp 697\u2013705","DOI":"10.18653\/v1\/P16-1066"},{"key":"587_CR3","doi-asserted-by":"crossref","unstructured":"Albadi N, Kurdi M, Mishra S (2018) Are they our brothers? Analysis and detection of religious hate speech in the Arabic Twittersphere. In: 2018 IEEE\/ACM international conference on advances in social networks analysis and mining (ASONAM). IEEE, pp 69\u201376","DOI":"10.1109\/ASONAM.2018.8508247"},{"key":"587_CR4","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. International World Wide Web Conferences Steering Committee, pp 759\u2013760","DOI":"10.1145\/3041021.3054223"},{"issue":"6","key":"587_CR5","doi-asserted-by":"publisher","first-page":"e156","DOI":"10.2196\/jmir.2121","volume":"14","author":"SH Burton","year":"2012","unstructured":"Burton SH, Tanner KW, Giraud-Carrier CG, West JH, Barnes MD (2012) \u201cright time, right place\u201d health communication on twitter: value and accuracy of location information. J Med Internet Res 14(6):e156","journal-title":"J Med Internet Res"},{"key":"587_CR6","doi-asserted-by":"crossref","unstructured":"Chatzakou D, Kourtellis N, Blackburn J, De\u00a0Cristofaro E, Stringhini G, Vakali A (2017) Mean birds: detecting aggression and bullying on twitter. In: Proceedings of the 2017 ACM on web science conference. ACM, pp 13\u201322","DOI":"10.1145\/3091478.3091487"},{"key":"587_CR7","first-page":"153","volume":"8","author":"A Chong","year":"2006","unstructured":"Chong A (2006) Intolerance of terror, or the terror of intolerance-religious tolerance and the response to terrorism. UTS L Rev 8:153","journal-title":"UTS L Rev"},{"key":"587_CR8","unstructured":"Chung J, G\u00fcl\u00e7ehre \u00c7, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR, arXiv:abs\/1412.3555"},{"issue":"1","key":"587_CR9","first-page":"22","volume":"16","author":"KW Church","year":"1990","unstructured":"Church KW, Hanks P (1990) Word association norms, mutual information, and lexicography. Comput Linguist 16(1):22\u201329","journal-title":"Comput Linguist"},{"key":"587_CR10","doi-asserted-by":"crossref","unstructured":"Darwish K, Magdy W, Mourad A (2012) Language processing for Arabic microblog retrieval. In: Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, pp 2427\u20132430","DOI":"10.1145\/2396761.2398658"},{"key":"587_CR11","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 11th international AAAI conference on web and social media. ICWSM \u201917, pp 512\u2013515","DOI":"10.1609\/icwsm.v11i1.14955"},{"key":"587_CR12","doi-asserted-by":"crossref","unstructured":"Djuric N, Zhou J, Morris R, Grbovic M, Radosavljevic V, Bhamidipati N (2015) Hate speech detection with comment embeddings. In: Proceedings of the 24th international conference on world wide web. ACM, pp 29\u201330","DOI":"10.1145\/2740908.2742760"},{"key":"587_CR13","doi-asserted-by":"crossref","unstructured":"Duwairi RM, Marji R, Sha\u2019ban N, Rushaidat S (2014) Sentiment analysis in Arabic tweets. In: 2014 5th international conference on information and communication systems (ICICS). IEEE, pp 1\u20136","DOI":"10.1109\/IACS.2014.6841964"},{"key":"587_CR14","first-page":"1289","volume":"3","author":"G Forman","year":"2003","unstructured":"Forman G (2003) An extensive empirical study of feature selection metrics for text classification. J Mach Learn Res 3:1289\u20131305","journal-title":"J Mach Learn Res"},{"key":"587_CR15","doi-asserted-by":"crossref","unstructured":"Forman G (2008) Bns feature scaling: an improved representation over tf-idf for svm text classification. In: Proceedings of the 17th ACM conference on information and knowledge management. ACM, pp 263\u2013270","DOI":"10.1145\/1458082.1458119"},{"key":"587_CR16","doi-asserted-by":"crossref","unstructured":"Founta AM, Chatzakou D, Kourtellis N, Blackburn J, Vakali A, Leontiadis I (2019) A unified deep learning architecture for abuse detection. In: Proceedings of the 10th ACM conference on web science. ACM, pp 105\u2013114","DOI":"10.1145\/3292522.3326028"},{"issue":"4","key":"587_CR17","doi-asserted-by":"publisher","first-page":"215","DOI":"10.14257\/ijmue.2015.10.4.21","volume":"10","author":"ND Gitari","year":"2015","unstructured":"Gitari ND, Zuping Z, Damien H, Long J (2015) A lexicon-based approach for hate speech detection. Int J Multimed Ubiquitous Eng 10(4):215\u2013230","journal-title":"Int J Multimed Ubiquitous Eng"},{"key":"587_CR18","unstructured":"Gouws S, Metzler D, Cai C, Hovy E (2011) Contextual bearing on linguistic variation in social media. In: Proceedings of the workshop on languages in social media. Association for Computational Linguistics, pp 20\u201329"},{"key":"587_CR19","doi-asserted-by":"crossref","unstructured":"Kaati L, Omer E, Prucha N, Shrestha A (2015) Detecting multipliers of jihadism on twitter. In: 2015 IEEE international conference on data mining workshop (ICDMW). IEEE, pp 954\u2013960","DOI":"10.1109\/ICDMW.2015.9"},{"key":"587_CR20","unstructured":"Kaji N, Kitsuregawa M (2007) Building lexicon for sentiment analysis from massive collection of html documents. In: Proceedings of the 2007 joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL)"},{"key":"587_CR21","unstructured":"Kulshrestha J, Kooti F, Nikravesh A, Gummadi KP (2012) Geographic dissection of the twitter network. In: Sixth international AAAI conference on weblogs and social media. AAAI"},{"key":"587_CR22","doi-asserted-by":"crossref","unstructured":"Kwok I, Wang Y (2013) Locate the hate: detecting tweets against blacks. In: AAAI","DOI":"10.1609\/aaai.v27i1.8539"},{"issue":"4","key":"587_CR23","doi-asserted-by":"publisher","first-page":"1246","DOI":"10.1109\/JBHI.2015.2403839","volume":"19","author":"ME Larsen","year":"2015","unstructured":"Larsen ME, Boonstra TW, Batterham PJ, O\u2019Dea B, Paris C, Christensen H (2015) We feel: mapping emotion on twitter. IEEE J Biomed Health Inform 19(4):1246\u20131252","journal-title":"IEEE J Biomed Health Inform"},{"key":"587_CR24","unstructured":"Le Q, Mikolov T (2014) Distributed representations of sentences and documents. In: International conference on machine learning, pp 1188\u20131196"},{"key":"587_CR25","doi-asserted-by":"crossref","unstructured":"Magdy W, Darwish K, Abokhodair N, Rahimi A, Baldwin T (2016a) # isisisnotislam or# deportallmuslims? Predicting unspoken views. In: Proceedings of the 8th ACM conference on web science. ACM, pp 95\u2013106","DOI":"10.1145\/2908131.2908150"},{"key":"587_CR26","doi-asserted-by":"crossref","unstructured":"Magdy W, Darwish K, Weber I (2016b) # failedrevolutions: using twitter to study the antecedents of ISIS support. First Monday 21(2)","DOI":"10.5210\/fm.v21i2.6372"},{"issue":"2","key":"587_CR27","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/coin.12024","volume":"31","author":"SM Mohammad","year":"2015","unstructured":"Mohammad SM, Kiritchenko S (2015) Using hashtags to capture fine emotion categories from tweets. Comput Intell 31(2):301\u2013326","journal-title":"Comput Intell"},{"key":"587_CR28","unstructured":"Mohammad SM, Kiritchenko S, Zhu X (2013) Nrc-canada: building the state-of-the-art in sentiment analysis of tweets. In: Proceedings of the seventh international workshop on semantic evaluation exercises (SemEval-2013), Atlanta, Georgia, USA"},{"key":"587_CR29","doi-asserted-by":"crossref","unstructured":"Mubarak H, Darwish K, Magdy W (2017) Abusive language detection on Arabic social media. In: Proceedings of the first workshop on abusive language online, pp 52\u201356","DOI":"10.18653\/v1\/W17-3008"},{"key":"587_CR30","doi-asserted-by":"crossref","unstructured":"M\u00fcller K, Schwarz C, et\u00a0al. (2018) Fanning the flames of hate: social media and hate crime. Technical reports. Competitive Advantage in the Global Economy (CAGE)","DOI":"10.2139\/ssrn.3082972"},{"key":"587_CR31","doi-asserted-by":"crossref","unstructured":"Olteanu A, Castillo C, Diaz F, Vieweg S (2014) Crisislex: a lexicon for collecting and filtering microblogged communications in crises. In: ICWSM","DOI":"10.1609\/icwsm.v8i1.14538"},{"key":"587_CR32","first-page":"1094","volume":"14","author":"A Pasha","year":"2014","unstructured":"Pasha A, Al-Badrashiny M, Diab MT, El Kholy A, Eskander R, Habash N, Pooleery M, Rambow O, Roth R (2014) Madamira: a fast, comprehensive tool for morphological analysis and disambiguation of Arabic. LREC 14:1094\u20131101","journal-title":"LREC"},{"issue":"302","key":"587_CR33","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1080\/14786440009463897","volume":"50","author":"K Pearson","year":"1900","unstructured":"Pearson K (1900) X on the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Lond Edinb Dublin Philos Mag J Sci 50(302):157\u2013175","journal-title":"Lond Edinb Dublin Philos Mag J Sci"},{"key":"587_CR34","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, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"587_CR35","unstructured":"Pew Research Center, Washington, DC (2009) Mapping the global Muslim population. http:\/\/www.pewforum.org\/2009\/10\/07\/mapping-the-global-muslim-population\/ . Accessed 24 October 2018"},{"key":"587_CR36","unstructured":"Pew Research Center, Washington, DC (2015) Religious composition by country, 2010\u20132050. http:\/\/www.pewforum.org\/2015\/04\/02\/religious-projection-table\/2010\/percent\/Middle_East-North_Africa\/ . Accessed 24 October 2018"},{"key":"587_CR37","unstructured":"Pew Research Center, Washington, DC (2017) Global restrictions on religion rise modestly in 2015, reversing downward trend - appendix b: social hostilities index. http:\/\/assets.pewresearch.org\/wp-content\/uploads\/sites\/11\/2017\/04\/07154135\/Appendix-B.pdf . Accessed 24 October 2018"},{"key":"587_CR38","doi-asserted-by":"crossref","unstructured":"Ribeiro MH, Calais PH, Santos YA, Almeida VA, Meira\u00a0Jr W (2018) Characterizing and detecting hateful users on twitter. In: Twelfth international AAAI conference on web and social media","DOI":"10.1609\/icwsm.v12i1.15057"},{"key":"587_CR39","unstructured":"Salem F (2017) Social media and the internet of things towards data-driven policymaking in the Arab world: potential, limits and concerns. MBR School of Government 7, Dubai"},{"key":"587_CR40","unstructured":"Silva LA, Mondal M, Correa D, Benevenuto F, Weber I (2016) Analyzing the targets of hate in online social media. In: Proceedings of the 11th international AAAI conference on web and social media. ICWSM\u201916, pp 687\u2013690"},{"key":"587_CR41","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1016\/j.procs.2017.10.117","volume":"117","author":"AB Soliman","year":"2017","unstructured":"Soliman AB, Eissa K, El-Beltagy SR (2017) Aravec: a set of Arabic word embedding models for use in Arabic NLP. Proc Comput Sci 117:256\u2013265","journal-title":"Proc Comput Sci"},{"key":"587_CR42","doi-asserted-by":"crossref","unstructured":"Taghva K, Elkhoury R, Coombs J (2005) Arabic stemming without a root dictionary. In: International conference on information technology: coding and computing, 2005, ITCC 2005. IEEE, vol\u00a01, pp 152\u2013157","DOI":"10.1109\/ITCC.2005.90"},{"key":"587_CR43","unstructured":"Twitter Safety (2017) Enforcing new rules to reduce hateful conduct and abusive behavior. https:\/\/blog.twitter.com\/official\/en_us\/topics\/company\/2017\/safetypoliciesdec2017.html . Accessed 27 November 2018"},{"key":"587_CR44","doi-asserted-by":"crossref","unstructured":"Waseem Z, Hovy D (2016) Hateful symbols or hateful people? predictive features for hate speech detection on twitter. In: Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics\u2014human language technologies. NAACL-HLT\u201916, pp 88\u201393","DOI":"10.18653\/v1\/N16-2013"},{"key":"587_CR45","unstructured":"Wiktorowicz Q, Amanullah S (2015) How tech can fight extremism. https:\/\/www.cnn.com\/2015\/02\/16\/opinion\/wiktorowicz-tech-fighting-extremism\/index.html . Accessed 24 October 2018"},{"key":"587_CR46","doi-asserted-by":"crossref","unstructured":"Yang J, Jiang YG, Hauptmann AG, Ngo CW (2007) Evaluating bag-of-visual-words representations in scene classification. In: Proceedings of the international workshop on multimedia information retrieval. ACM, pp 197\u2013206","DOI":"10.1145\/1290082.1290111"}],"container-title":["Social Network Analysis and Mining"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-019-0587-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s13278-019-0587-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-019-0587-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,18]],"date-time":"2023-09-18T21:00:21Z","timestamp":1695070821000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s13278-019-0587-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,8,5]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["587"],"URL":"https:\/\/doi.org\/10.1007\/s13278-019-0587-5","relation":{},"ISSN":["1869-5450","1869-5469"],"issn-type":[{"value":"1869-5450","type":"print"},{"value":"1869-5469","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,8,5]]},"assertion":[{"value":"12 December 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 July 2019","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2019","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"41"}}