Detecting Arabic Offensive Language in Microblogs Using Domain-Specific Word Embeddings and Deep Learning

In recent years, social media networks are emerging as a key player by providing platforms for opinions expression, communication, and content distribution. However, users often take advantage of perceived anonymity on social media platforms to share offensive or hateful content. Thus, offensive lan...

Full description

Saved in:
Bibliographic Details
Published in:Tehnički glasnik Vol. 16; no. 3; pp. 394 - 400
Main Authors: O. Aljuhani, Khulood, H. Alyoubi, Khaled, S. Alotaibi, Fahd
Format: Journal Article
Language:English
Published: University North 21.06.2022
Subjects:
Arabic Natural Language Processing
Arabic Tweets
Offensive Language
Offensive Language Detection
Word Embeddings
ISSN:1846-6168, 1848-5588
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract In recent years, social media networks are emerging as a key player by providing platforms for opinions expression, communication, and content distribution. However, users often take advantage of perceived anonymity on social media platforms to share offensive or hateful content. Thus, offensive language has grown as a significant issue with the increase in online communication and the popularity of social media platforms. This problem has attracted significant attention for devising methods for detecting offensive content and preventing its spread on online social networks. Therefore, this paper aims to develop an effective Arabic offensive language detection model by employing deep learning and semantic and contextual features. This paper proposes a deep learning approach that utilizes the bidirectional long short-term memory (BiLSTM) model and domain-specific word embeddings extracted from an Arabic offensive dataset. The detection approach was evaluated on an Arabic dataset collected from Twitter. The results showed the highest performance accuracy of 0.93% with the BiLSTM model trained using a combination of domain-specific and agnostic-domain word embeddings.
AbstractList In recent years, social media networks are emerging as a key player by providing platforms for opinions expression, communication, and content distribution. However, users often take advantage of perceived anonymity on social media platforms to share offensive or hateful content. Thus, offensive language has grown as a significant issue with the increase in online communication and the popularity of social media platforms. This problem has attracted significant attention for devising methods for detecting offensive content and preventing its spread on online social networks. Therefore, this paper aims to develop an effective Arabic offensive language detection model by employing deep learning and semantic and contextual features. This paper proposes a deep learning approach that utilizes the bidirectional long short-term memory (BiLSTM) model and domain-specific word embeddings extracted from an Arabic offensive dataset. The detection approach was evaluated on an Arabic dataset collected from Twitter. The results showed the highest performance accuracy of 0.93% with the BiLSTM model trained using a combination of domain-specific and agnostic-domain word embeddings.
Author O. Aljuhani, Khulood
S. Alotaibi, Fahd
H. Alyoubi, Khaled
Author_xml – sequence: 1
  givenname: Khulood
  surname: O. Aljuhani
  fullname: O. Aljuhani, Khulood
  organization: Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
– sequence: 2
  givenname: Khaled
  surname: H. Alyoubi
  fullname: H. Alyoubi, Khaled
  organization: Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
– sequence: 3
  givenname: Fahd
  surname: S. Alotaibi
  fullname: S. Alotaibi, Fahd
  organization: Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
BookMark eNp9kM1OHDEQhC1EJH7CC-TkFxho_4zHPiIWyEqLOCQox1HbY4-Mdu2VbSLx9gxLFCk5cOpWddcnVZ2R45STJ-Qbg0vBNIirNnccOAcBPeMATB-RU6al7vpe6-PDrjrFlD4hF7U-AwDXQw9qOCVx5Zt3LaaZXhe00dHHEHyq8benG0zzC86exkQfoivZbvNc6VN9_17lHcbU_dh7F8Ni-5XLRG931k_Tcq4U00RX3u_pxmNJi_SVfAm4rf7izzwnT3e3P2--d5vH-_XN9aZzwvStG4yUjks7YFBOCKMkTGZSWjNnmAcMS2JjTGDCOq-D5cC46-1kEHoULIhzsv7gThmfx32JOyyvY8Y4HoRc5hFLi27rR1QySLsU5oFLZdHaYA1zg4TBDGp4Z_EP1hK-1uLDXx6D8dD92Obx3-4Xk_7P5GLDFnNqBeP2M-sb8RmKYg
CitedBy_id crossref_primary_10_7717_peerj_cs_1966
crossref_primary_10_1515_lpp_2024_0034
crossref_primary_10_1371_journal_pone_0319900
crossref_primary_10_7717_peerj_cs_1221
crossref_primary_10_1155_acis_5565888
ContentType Journal Article
DBID AAYXX
CITATION
DOA
DOI 10.31803/tg-20220305120018
DatabaseName CrossRef
DOAJ Directory of Open Access Journals
DatabaseTitle CrossRef
DatabaseTitleList CrossRef
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 1848-5588
EndPage 400
ExternalDocumentID oai_doaj_org_article_a64f4b001e0246babbfb91c74079767f
10_31803_tg_20220305120018
GroupedDBID AAYXX
ALMA_UNASSIGNED_HOLDINGS
CITATION
GROUPED_DOAJ
VP8
ID FETCH-LOGICAL-c395t-7944c24b7af6c339640d9d6881c91e0af180999f13bce8fb2012c5bd9a05a31f3
IEDL.DBID DOA
ISICitedReferencesCount 7
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000818884700015&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 1846-6168
IngestDate Fri Oct 03 12:35:51 EDT 2025
Tue Nov 18 21:24:55 EST 2025
Sat Nov 29 02:52:37 EST 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 3
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c395t-7944c24b7af6c339640d9d6881c91e0af180999f13bce8fb2012c5bd9a05a31f3
OpenAccessLink https://doaj.org/article/a64f4b001e0246babbfb91c74079767f
PageCount 7
ParticipantIDs doaj_primary_oai_doaj_org_article_a64f4b001e0246babbfb91c74079767f
crossref_primary_10_31803_tg_20220305120018
crossref_citationtrail_10_31803_tg_20220305120018
PublicationCentury 2000
PublicationDate 2022-06-21
PublicationDateYYYYMMDD 2022-06-21
PublicationDate_xml – month: 06
  year: 2022
  text: 2022-06-21
  day: 21
PublicationDecade 2020
PublicationTitle Tehnički glasnik
PublicationYear 2022
Publisher University North
Publisher_xml – name: University North
SSID ssj0002875067
ssib044762717
ssib025702364
ssib046624977
ssib053799675
Score 2.2410061
SecondaryResourceType review_article
Snippet In recent years, social media networks are emerging as a key player by providing platforms for opinions expression, communication, and content distribution....
SourceID doaj
crossref
SourceType Open Website
Enrichment Source
Index Database
StartPage 394
SubjectTerms Arabic Natural Language Processing
Arabic Tweets
Offensive Language
Offensive Language Detection
Word Embeddings
Title Detecting Arabic Offensive Language in Microblogs Using Domain-Specific Word Embeddings and Deep Learning
URI https://doaj.org/article/a64f4b001e0246babbfb91c74079767f
Volume 16
WOSCitedRecordID wos000818884700015&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVAON
  databaseName: DOAJ Directory of Open Access Journals
  customDbUrl:
  eissn: 1848-5588
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0002875067
  issn: 1846-6168
  databaseCode: DOA
  dateStart: 20090101
  isFulltext: true
  titleUrlDefault: https://www.doaj.org/
  providerName: Directory of Open Access Journals
– providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  customDbUrl:
  eissn: 1848-5588
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssib044762717
  issn: 1846-6168
  databaseCode: M~E
  dateStart: 20070101
  isFulltext: true
  titleUrlDefault: https://road.issn.org
  providerName: ISSN International Centre
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV05T8UwDI4QYoABcYpbGdhQRdMcbUbggRi4Bq6tStLkqYhXEBR-P3ZaoCywsLVVUjWuHftLnM-E7AaZG-XwRI90JgH8xRObcZmowJFgTgQea0benuUXF8X9vb4alPrCnLCOHrgT3L5RIgh07R68ibLG2mA1czkAEfCkecDZF6KeAZgCTcLSbMiM_nkvBNj8ALgIpQB2fJ_AlDyHuL93pA9xyQk8aSw_CwhIAb5SRXfiBkwg5fvtGLQry9BYGCYlFT-82oD8P3qpkwUy34eX9KAb1iKZ8s0SmRuQDi6TeuRx4wCuoZmxtaOXIXRp7PSsX72kdUPPMVXPwtT4SmNeAR09TUzdJLFifYBud4Bb6fHE-iruX1HTVHTk_TPtOVvHK-Tm5Pj66DTpCy4kjmvZJmCbwmXC5iYox7lWIq10pYqCOQ3yNwHJvrQOjFvni2AheMictJU2qTScBb5Kppunxq8RqrLUB6ny1AUrmJbW5xCaWadYlSmuqnXCPgVWup6NHItiPJaASqKQy3Zc_hTyOtn76vPccXH82voQ_8NXS-TRjg9Au8peu8q_tGvjP16ySWbx0zDBLGNbZLp9efPbZMa9t_Xry05U3A9fUuXN
linkProvider Directory of Open Access Journals
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Detecting+Arabic+Offensive+Language+in+Microblogs+Using+Domain-Specific+Word+Embeddings+and+Deep+Learning&rft.jtitle=Tehni%C4%8Dki+glasnik&rft.au=Khulood+O.+Aljuhani&rft.au=Khaled+H.+Alyoubi&rft.au=Fahd+S.+Alotaibi&rft.date=2022-06-21&rft.pub=University+North&rft.issn=1846-6168&rft.eissn=1848-5588&rft.volume=16&rft.issue=3&rft.spage=394&rft.epage=400&rft_id=info:doi/10.31803%2Ftg-20220305120018&rft.externalDBID=DOA&rft.externalDocID=oai_doaj_org_article_a64f4b001e0246babbfb91c74079767f
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1846-6168&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1846-6168&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1846-6168&client=summon