[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118345-en":3,"doc-seo-118345-105":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},118345,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","E-Sport Engagement Prediction Using Machine Learning Classification Algorithms","E-sports has grown rapidly, drawing diverse audiences and making reliable engagement prediction essential for game developers, tournament organizers, sponsors, and marketers. A study applies data-mining predictive modeling to classify engagement into four levels: strongly agree, either agree or disagree, disagree, and strongly disagree. Using statistical techniques, it categorizes users based on 59 attributes from 106 instances and trains six classification algorithms across two model groups to learn patterns linked to engagement. Accuracies range from 76% to 92%, and feature selection highlights activity participation, exchanging ideas, and playing with like-minded gamers as key engagement dimensions.","JIM International Journal of  \nInteractive Mobile Technologies  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJIM | eISSN: 1865-7923 | Vol. 18 No. 21 (2024) |   \n[https://doi.org/10.3991/ijim.v18i21.50553](https://doi.org/10.3991/ijim.v18i21.50553)  \nPAPER  \nE-Sport Engagement Prediction Using Machine Learning Classification Algorithms  \nMasrur Mohd Khir1, Nur Atiqah Rochin Demong2(􀀍), Siti Noorsuriani Maon1  \n1Department of International Business and Management Studies, Faculty of Business and Management, Universiti Teknologi MARA, Selangor, Malaysia  \n2Department of Technology and Supply Chain Management Studies, Faculty of Business and Management, Universiti Teknologi MARA, Selangor, Malaysia  \n[rochin@uitm.edu.my](rochin@uitm.edu.my)  \nABSTRACT  \nIn recent years, e-sports has experienced a rapid surge in popularity, attracting a vast and diverse audience. As this industry continues to evolve, understanding and predicting e-sport engagement becomes increasingly vital for stakeholders, including game developers, tournament organizers, sponsors, and marketers. Machine learning classification algorithms offera powerful approach to analyse and forecast user engagement in e-sports, thereby enabling the industry to tailor experiences to individual preferences and behaviours. Thus, this study investigates the level of engagement classification technique of data mining using predictive modelling operations with four different classes, namely strongly agree, either agree or disagree, disagree, and strongly disagree. Machine learning algorithms, particularly classification models, have proven to be effective in analysing large and complex datasets related to e-sport engagement. This study applies statistical techniques to categorize users based on 59 attributes of 106 instances to predict the engagement levels. By training on historical user data, six classification algorithms from two groups, namely bayes and rules, have been used to identify patterns and trends that are indicative of different engagement levels, with the accuracy ranges from 76% to 92%. For feature selection, the result shows that participating in activities, enjoying exchanging ideas, and playing with like-minded gamers were the top three ranking dimensions contributing to the level of engagement. Machine learning classification algorithms have the potential to revolutionize how e-sport engagement is understood and optimized. By analysing diverse data points and leveraging advanced predictive techniques, machine learning algorithms enable stakeholders to tailor e-sport experiences to individual preferences and behaviours, ultimately enhancing user engagement and satisfaction.  \nKEYWORDS  \ne-sport engagement, machine learning, classification algorithms, prediction, user engagement  \n1 INTRODUCTION  \nThe trend of e-sports continues to grow and gain popularity, especially among the younger generations. As e-sports become more popular, understanding what  \nKhir, M. M., Demong, N.A. R., Maon, S. N. (2024) . E-Sport Engagement Prediction Using Machine Learning Classification Algorithms. International Journal of Interactive Mobile Technologies (iJIM), 18(21), pp. 185–199. [https://doi.org/10.3991/ijim.v18i21.50553](https://doi.org/10.3991/ijim.v18i21.50553)[ ](https://doi.org/10.3991/ijim.v18i21.50553)[Article submitted 2024-06-13. Revision uploaded 2024-08-19. Final acceptance 2024-08-19.](Article submitted 2024-06-13. Revision uploaded 2024-08-19. Final acceptance 2024-08-19.)  \n© 2024 by the authors of this article. Published under CC-BY.  \niJIM | Vol. 18 No. 21 (2024) International Journal of Interactive Mobile Technologies (iJIM) 185  \nKhir et al.  \ncontributes to e-sports engagement is increasingly important. Predicting e-sport interaction engagement classification using machine learning algorithms has revolutionized the way we understand and enhance gaming experiences. By leveraging advanced mac","cbCaijZpkqQPII8h","https://ap.wps.com/l/cbCaijZpkqQPII8h","pdf",836947,1,15,"English","en",105,"# Abstract\n# Keywords\n# Introduction","[{\"question\":\"How is e-sport engagement classified in the study?\",\"answer\":\"Engagement is classified into four classes: strongly agree, either agree or disagree, disagree, and strongly disagree.\"},{\"question\":\"What data and attributes are used to predict engagement levels?\",\"answer\":\"The study uses 59 attributes from 106 instances and applies predictive modeling on historical user data.\"},{\"question\":\"Which machine learning models are used and what accuracy range is achieved?\",\"answer\":\"Six classification algorithms from two groups (bayes and rules) are used, achieving accuracy in the range of 76% to 92%.\"}]","E-Sport Engagement Prediction Using Machine Learning Classification Algorithms | PDF",1785683205,38,{"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},"e-sport-engagement-prediction-using-machine-learning-classification-algorithms","",{"@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/e-sport-engagement-prediction-using-machine-learning-classification-algorithms/118345/",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","2026-08-02",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},"How is e-sport engagement classified in the study?","Question",{"text":75,"@type":76},"Engagement is classified into four classes: strongly agree, either agree or disagree, disagree, and strongly disagree.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data and attributes are used to predict engagement levels?",{"text":80,"@type":76},"The study uses 59 attributes from 106 instances and applies predictive modeling on historical user data.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models are used and what accuracy range is achieved?",{"text":84,"@type":76},"Six classification algorithms from two groups (bayes and rules) are used, achieving accuracy in the range of 76% to 92%.","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"]