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The study surveys prior classification approaches and then builds an optimized framework for online hate detection using two machine learning models, Multinomial Naive Bayes and Logistic Regression. Classifier outputs are improved with bio-inspired optimization, including Particle Swarm Optimization and Genetic Algorithms, combined with fuzzy logic to better capture sentiment-oriented distinctions and interpret text classes more realistically.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-multi-stage-machine-learning-and-fuzzy-approach-to-cyber-hate-detection-journal-article/290968/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-multi-stage-machine-learning-and-fuzzy-approach-to-cyber-hate-detection-journal-article/290968.png","ImageObject",300,407,{"name":92,"@type":93},"Seraphina","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the study target?","Question",{"text":113,"@type":114},"The study targets cyber-hate detection in online social media, motivated by the increasing prevalence and impact of cyber-hate.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which machine learning classifiers are used?",{"text":118,"@type":114},"Multinomial Naive Bayes and Logistic Regression are applied to four online hate datasets for classification.",{"name":120,"@type":111,"acceptedAnswer":121},"How are results optimized in the proposed approach?",{"text":122,"@type":114},"Particle Swarm Optimization and Genetic Algorithms optimize classifier results, and fuzzy logic is incorporated to improve how text sentiment-oriented distinctions are handled.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290968,1789643067,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":130,"read_time":145,"preview_page_count":4},962075114101,"https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165","A multi-stage machine learning and fuzzy approach to cyber-hate detection  \nArticle  \nPublished Version  \nCreative Commons: Attribution-Noncommercial-No Derivative Works 4.0  \nOpen Access  \nKetsbaia, L. , Issac, B. , Chen, X. ORCID:  \n[https://orcid.org/0000-0001-9267-355X and Mary](https://orcid.org/0000-0001-9267-355X and Mary) Jacob, S.(2023) A multi-stage machine learning and fuzzy approach to cyber-hate detection . IEEE Access, 11. pp. 56046-56065. ISSN 2169-3536 doi:  \n[https://doi.org/10.1109/ACCESS.2023.3282834 Available](https://doi.org/10.1109/ACCESS.2023.3282834 Available) at  \n[https://centaur. reading.ac. uk/1](https://centaur. reading.ac. uk/1) 16490/  \nIt is advisable to refer to the publisher’s version if you intend to cite from the work. See Guidance on citing.  \nTo link to this article DOI: [http://dx.doi.org/10.1109/ACCESS.2023.3282834](http://dx.doi.org/10.1109/ACCESS.2023.3282834)  \nPublisher: IEEE  \nAll outputs in CentAUR are protected by Intellectual Property Rights law, including copyright law. Copyright and IPR is retained by the creators or other copyright holders . Terms and conditions for use of this material are defined in the End User Agreement  .  \n[www. reading.ac. uk/centaur](www. reading.ac. uk/centaur)  \nCentAUR  \nCentral Archive at the University of Reading  \nReading’s research outputs online  \nReceived 6 April 2023, accepted 20 April 2023, date of publication 5 June 2023, date of current version 9 June 2023. Digital Object Identifier 10.1109/ACCESS.2023.3282834  \nA Multi-Stage Machine Learning and Fuzzy Approach to Cyber-Hate Detection  \nLIDA KETSBAIA 1, BIJU ISSAC1,(Senior Member, IEEE), XIAOMIN CHEN 1, AND SEIBU MARY JACOB2,(Member, IEEE)  \n1Department of Computer and Information Sciences, Northumbria University, NE1 8ST Newcastle upon Tyne, U.K.  \n2 School of Computing, Engineering & Digital Technologies, Teesside University, TS1 3BX Middlesbrough, U.K.  \nCorresponding author: Biju Issac ([bissac@ieee.org](bissac@ieee.org))  \nABSTRACT Social media has revolutionized the way individuals connect and share information globally. However, the rise of these platforms has led to the proliferation of cyber-hate, which is a significant concern that has garnered attention from researchers. To combat this issue, various solutions have been proposed, utilizing Machine learning and Deep learning techniques such as Naive Bayes, Logistic Regression, Convolutional Neural Networks, and Recurrent Neural Networks. These methods rely on a mathematical approach to distinguish one class from another. However, when dealing with sentiment-oriented data, a more ‘‘critical thinking’’ perspective is needed for accurate classification, as it provides a more realistic representation of how people interpret online messages. Based on a literature review conducted to explore efficient classification techniques, this study applied two machine learning classifiers, Multinomial Naive Bayes and Logistic Regression, to four online hate datasets. The results of the classifiers were optimized using bio-inspired optimization techniques such as Particle Swarm Optimization and Genetic Algorithms, in conjunction with Fuzzy Logic, to gain a deeper understanding of the text in the datasets.  \nINDEX TERMS Cyberbullying, fuzzy logic, logistic regression, multinomial Naive Bayes, PSO, VADER.  \nI. INTRODUCTION  \nIt was the advancement of technology and the impulse of human communication that led to the evolution of social media, which altered how individuals interact online. Prior to the introduction of Information Communication Technology (ICT), human interactions were largely confined to geographical locations; however, Online Social Networks (OSNs) have eliminated geographical barriers [1] .  \nIt has become increasingly apparent that cyber-hate is a widespread issue duetothe pervasiveness of easy-to-use technologies. Social media platforms have emerged as a medium for the perpetration of aggressiveness and bullying, making ita dange","cbCailiNxEibxgOS","https://ap.wps.com/l/cbCailiNxEibxgOS","pdf",2467282,22,"English","# Abstract\n# Index Terms\n# I. Introduction\n# II. Methods and Framework\n## Multi-stage machine learning classifiers\n## Fuzzy logic integration\n## Bio-inspired optimization (PSO, Genetic Algorithms)","[{\"question\":\"What problem does the study target?\",\"answer\":\"The study targets cyber-hate detection in online social media, motivated by the increasing prevalence and impact of cyber-hate.\"},{\"question\":\"Which machine learning classifiers are used?\",\"answer\":\"Multinomial Naive Bayes and Logistic Regression are applied to four online hate datasets for classification.\"},{\"question\":\"How are results optimized in the proposed approach?\",\"answer\":\"Particle Swarm Optimization and Genetic Algorithms optimize classifier results, and fuzzy logic is incorporated to improve how text sentiment-oriented distinctions are handled.\"}]","A Multi-Stage Machine Learning and Fuzzy Approach to Cyber-Hate Detection - Journal Article | PDF",55]