[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124300-en":3,"doc-seo-124300-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124300,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","DETECTION OF HATE SPEECH ON SOCIAL MEDIA UTILIZING MACHINE LEARNING","This article examines how machine learning and deep learning can identify hate speech on social media. The study evaluates multiple approaches using metrics including F-measure, AUC-ROC, precision, accuracy, and recall to measure effectiveness across tactics. Results show that deep learning models—especially the bidirectional LSTM (BiLSTM)—consistently outperform alternatives in text categorization. The research supports earlier hostile-content detection and cyberbullying prevention, contributing to safer, more inclusive online environments and informing future work such as real-time or hybrid detection systems.","DOI: 10. 37943/22SKSG8575  \n© Aziza Zhidebayeva, Sabira Akhmetova, Satmyrza Mamikov, Mukhtar Kerimbekov, Sapargali Aldeshov, Guldana Shaimerdenova  \n71  \nDOI: 10.37943/22SKSG8575  \nAziza Zhidebayeva  \nCandidate of Technical Sciences, Senior Lecturer, Department of Computer Science and Mathematics  \n[aziza_68.kz@mail.ru](aziza_68.kz@mail.ru), [orcid.org/0000-0002-3768-4835](orcid.org/0000-0002-3768-4835)  \nAcademician A.Kuatbekov Peoples' Friendship University, Kazakhstan  \nSabiraAkhmetova  \nCandidate of Physical and Mathematical Sciences, Associate Professor, Department of Information system  \n[sabdas65@mail.ru](sabdas65@mail.ru), [orcid.org/0000-0001-5164-2028](orcid.org/0000-0001-5164-2028)[ ](orcid.org/0000-0001-5164-2028)Mukhtar Auezov South Kazakhstan University, Kazakhstan  \nSatmyrza Mamikov  \nCandidate of Pedagogical Sciences, Associate Professor, Department of Computer Science and Mathematics  \n[satmyrza_mamikov@mail.ru](satmyrza_mamikov@mail.ru), [orcid.org/0009-0009-3476-2920](orcid.org/0009-0009-3476-2920)[ ](orcid.org/0009-0009-3476-2920)Academician A.Kuatbekov Peoples' Friendship University, Kazakhstan  \nMukhtar Kerimbekov  \nCandidate of pedagogical Sciences, Associate Professor, Department of Computer Science and Mathematics  \n[mukhtar_m@mail.ru](mukhtar_m@mail.ru), [orcid.org/0009-0002-3310-1556](orcid.org/0009-0002-3310-1556)  \nAcademician A.Kuatbekov Peoples' Friendship University, Kazakhstan  \nSapargali Aldeshov  \nCandidate of Pedagogical Sciences, Associate Professor, Department of Computer Science and Mathematics  \n[aldeshov4@mail.ru](aldeshov4@mail.ru), [orcid.org/0000-0001-7735-2299](orcid.org/0000-0001-7735-2299)[ ](orcid.org/0000-0001-7735-2299)Ozbekali Zhanibekov South Kazakhstan Pedagogical University, Kazakhstan  \nGuldana Shaimerdenova  \nPhD, Associate Professor, Department of Information Communication Technologies  \n[danel101kz@gmail.com](danel101kz@gmail.com), [orcid.org/0000-0001-8685-7125](orcid.org/0000-0001-8685-7125)[ ](orcid.org/0000-0001-8685-7125)Mukhtar Auezov South Kazakhstan University, Kazakhstan  \nDETECTION OF HATE SPEECH ON SOCIAL MEDIA UTILIZING  \nMACHINE LEARNING  \nAbstract: This article investigates the identification of hate speech on social media using machine learning and deep learning techniques. The research uses metrics such as F-measure, AUC-ROC, precision, accuracy, and recall assessing the effectiveness of various tactics. The findings indicate that deep learning models, particularly the bidirectional long short-term memory (BiLSTM) architecture, consistently outperform other methods in categorization tasks. The research emphasizes the importance of sophisticated neural network designs in identifying the intricacies of hostile and offensive content online. The study offers insights for promoting early identification and prevention of cyberbullying, improving secure and inclusive online environments. Future research may explore real-time detection systems, hybrid approaches, orthe integration of complementary components to enhance and improve innovative technology in tackling this significant social issue.  \nCopyright © 2025, Authors. This is an open access article under the Creative Commons CC BY-NC-ND license Received: 16.12.2024 Accepted: 25.06.2025 Published: 30.06.2025  \n72  \nScientific Journal of Astana IT University ISSN (P): 2707-9031 ISSN (E): 2707-904X VOLUME 22, JUNE 2025  \nA sample tweet was annotated by specialists who categorize tweets as hate speech, offensive language, or neutral. The researchers applied shallow learning methodologies and integrated word embeddings like Word2Vec and GloVe to enhance the efficacy of deep learning models. The results indicate that BiLSTM surpasses shallow learning methods in detecting hate speech on Twitter, highlighting the efficacy of deep learning approaches in recognizing and tracking hate speech on social media platforms. When comparing different deep learning and machine learning models on different datasets, the results","cbCaiksle4VGpbXQ","https://ap.wps.com/l/cbCaiksle4VGpbXQ","pdf",3613371,1,17,"English","en",105,"# Introduction\n## Aim of article\n## Assess Performance Metrics\n# Model Comparison and Evaluation\n## Performance metrics used\n## Results across datasets\n# Classification Outcomes\n## Best-performing model and scores\n# Implications and Future Work\n## Real-time systems and alternative architectures","[{\"question\":\"What problem does the article address?\",\"answer\":\"The article investigates identifying hate speech on social media using machine learning and deep learning techniques.\"},{\"question\":\"Which performance metrics are used to evaluate models?\",\"answer\":\"Models are assessed with F-measure, AUC-ROC, precision, accuracy, and recall.\"},{\"question\":\"Which approach performs best for hate speech detection?\",\"answer\":\"Deep learning models, particularly the BiLSTM architecture, consistently outperform other methods, with a reported best model achieving strong classification performance.\"}]","DETECTION OF HATE SPEECH ON SOCIAL MEDIA UTILIZING MACHINE LEARNING | PDF",1785821476,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"detection-of-hate-speech-on-social-media-utilizing-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/detection-of-hate-speech-on-social-media-utilizing-machine-learning/124300/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the article address?","Question",{"text":75,"@type":76},"The article investigates identifying hate speech on social media using machine learning and deep learning techniques.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which performance metrics are used to evaluate models?",{"text":80,"@type":76},"Models are assessed with F-measure, AUC-ROC, precision, accuracy, and recall.",{"name":82,"@type":73,"acceptedAnswer":83},"Which approach performs best for hate speech detection?",{"text":84,"@type":76},"Deep learning models, particularly the BiLSTM architecture, consistently outperform other methods, with a reported best model achieving strong classification performance.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]