{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T09:08:28Z","timestamp":1771664908527,"version":"3.50.1"},"reference-count":65,"publisher":"Emerald","issue":"3","license":[{"start":{"date-parts":[[2020,7,31]],"date-time":"2020-07-31T00:00:00Z","timestamp":1596153600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJWIS"],"published-print":{"date-parts":[[2020,7,31]]},"abstract":"\nPurpose<\/jats:title>\nThis paper aims to propose an approach for hate speech detection against politicians in Arabic community on social media (e.g. Youtube). In the literature, similar works have been presented for other languages such as English. However, to the best of the authors\u2019 knowledge, not much work has been conducted in the Arabic language.<\/jats:p>\n<\/jats:sec>\n\nDesign\/methodology\/approach<\/jats:title>\nThis approach uses both classical algorithms of classification and deep learning algorithms. For the classical algorithms, the authors use Gaussian NB (GNB), Logistic Regression (LR), Random Forest (RF), SGD Classifier (SGD) and Linear SVC (LSVC). For the deep learning classification, four different algorithms (convolutional neural network (CNN), multilayer perceptron (MLP), long- or short-term memory (LSTM) and bi-directional long- or short-term memory (Bi-LSTM) are applied. For extracting features, the authors use both Word2vec and FastText with their two implementations, namely, Skip Gram (SG) and Continuous Bag of Word (CBOW).<\/jats:p>\n<\/jats:sec>\n\nFindings<\/jats:title>\nSimulation results demonstrate the best performance of LSVC, BiLSTM and MLP achieving an accuracy up to 91%, when it is associated to SG model. The results are also shown that the classification that has been done on balanced corpus are more accurate than those done on unbalanced corpus.<\/jats:p>\n<\/jats:sec>\n\nOriginality\/value<\/jats:title>\nThe principal originality of this paper is to construct a new hate speech corpus (Arabic_fr_en) which was annotated by three different annotators. This corpus contains the three languages used by Arabic people being Arabic, French and English. For Arabic, the corpus contains both script Arabic and Arabizi (i.e. Arabic words written with Latin letters). Another originality is to rely on both shallow and deep leaning classification by using different model for extraction features such as Word2vec and FastText with their two implementation SG and CBOW.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijwis-08-2019-0036","type":"journal-article","created":{"date-parts":[[2020,8,4]],"date-time":"2020-08-04T07:50:58Z","timestamp":1596527458000},"page":"295-313","source":"Crossref","is-referenced-by-count":23,"title":["Detecting hate speech against politicians in Arabic community on social media"],"prefix":"10.1108","volume":"16","author":[{"given":"Imane","family":"Guellil","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ahsan","family":"Adeel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Faical","family":"Azouaou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sara","family":"Chennoufi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanene","family":"Maafi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thinhinane","family":"Hamitouche","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"issue":"2","key":"key2020100707570952400_ref001","doi-asserted-by":"crossref","first-page":"113","DOI":"10.7763\/IJKE.2015.V1.19","article-title":"Detection of abusive accounts with Arabic tweets","volume":"1","year":"2015","journal-title":"International Journal of Knowledge Engineering-Iacsit"},{"issue":"1","key":"key2020100707570952400_ref002","first-page":"75","article-title":"Detection of offensive tweets: a comparative study","volume":"1","year":"2018","journal-title":"Computer Reviews Journal"},{"issue":"2","key":"key2020100707570952400_ref003","first-page":"83","article-title":"Detection of hate speech in social networks: a survey on multilingual corpus","volume":"9","year":"2019","journal-title":"Computer Science and Information Technology (CS and IT)"},{"issue":"1A","key":"key2020100707570952400_ref004","first-page":"163","article-title":"A prototype for a standard Arabic sentiment analysis corpus","volume":"13","year":"2016","journal-title":"International Arab Journal of Information Technology"},{"key":"key2020100707570952400_ref005","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.procs.2018.10.473","article-title":"Dataset construction for the detection of anti-social behaviour in online communication in Arabic","volume":"142","year":"2018","journal-title":"Procedia Computer Science"},{"key":"key2020100707570952400_ref006","first-page":"69","article-title":"Are they our brothers? 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