[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118263-en":3,"doc-seo-118263-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},118263,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Approaches for Cyberbullying Detection","Cyberbullying is bullying carried out through electronic communication and internet platforms, with documented harmful effects on victims’ emotions and long-term outcomes such as depression, anxiety, harassment, and suicide. The study addresses the need for automated detection across social media by combining natural language processing feature extraction (Bag-of-Words and TF-IDF) with supervised machine learning classifiers. Models compared include Logistic Regression, Naive Bayes, K-Nearest Neighbor, and Extreme Gradient Boosting, trained on Twitter text. Results compare performance and accuracy, showing XGBoost as the best classifier regardless of whether Bag-of-Words or TF-IDF features are used.","University of Central Florida  \nSTARS  \nData Science and Data Mining  \nSpring 2024  \nMachine Learning Approaches for Cyberbullying Detection  \nRoland Fiagbe  \nUniversity of Central Florida, [ro210333@ucf.edu](ro210333@ucf.edu)  \n Part of the Analysis Commons, Applied Statistics Commons, Data Science Commons, Probability Commons, Statistical Methodology Commons, Statistical Models Commons, and the Statistical Theory Commons  \nFind similar works at: [https://stars.library.ucf.edu/data-science-mining](https://stars.library.ucf.edu/data-science-mining)  \nUniversity of Central Florida Libraries [http://library.ucf.edu](http://library.ucf.edu)  \nThis Article is brought to you for free and open access by STARS. It has been accepted for inclusion in Data Science and Data Mining by an authorized administrator of STARS. For more information, please [contact STARS@ucf.edu](contact STARS@ucf.edu).  \nSTARS Citation  \nFiagbe, Roland, \"Machine Learning Approaches for Cyberbullying Detection\" (2024) . Data Science and Data Mining. 18.  \n[https://stars.library.ucf.edu/data-science-mining/18](https://stars.library.ucf.edu/data-science-mining/18)  \nMachine Learning Approaches for Cyberbullying  \nDetection*  \nRoland Fiagbe  \nDepartment of Statistics and Data Science  \nUniversity of Central Florida  \nOrlando, United States  \n[fagberoland@knights.ucf.edu](fagberoland@knights.ucf.edu)  \nAbstract—Cyberbullying refers to the act of bullying using electronic means and the internet. In recent years, this act has been identifed to be a major problem among young people and even adults. It can negatively impact one’s emotions and lead to adverse outcomes like depression, anxiety, harassment, and suicide, among others. This has led to the need to employ machine learning techniques to automatically detect cyberbullying and prevent them on various social media platforms. In this study, we want to analyze the combination of some Natural Language Processing (NLP) algorithms (such as Bag-of-Words and TFIDF) with some popular machine learning algorithms (such as Logistic Regression (LR), Naive Bayes (NB), K-Nearest Neighbor (KNN), and Extreme Gradient Boosting( XGboost)) to detect cyberbullying on Twitter. The NLP methods were employed to extract features from tweets and convert them to numerical vectors and these features were analyzed with the machine learning algorithms. Comparing their performances and accuracy, the Extreme Gradient Boosting( XGboost) model emerged as the best-performing classifer irrespective of whether it uses features from bag-of-words or TF-IDF.  \nIndex Terms—Cyberbullying, Twitter, classifcation  \nI. INTRODUCTION  \nIn recent years, the use of social media has been very popular among young people around the world. It has been a popular medium where people discuss societal issues, communicate and share ideas and knowledge. Social media has made this possible through the use of texts, images, audio, videos, etc. Although social media have advantageously impacted lives, however, it also comes with some disadvantages. Oneof these major problems is the aggressive intentional act or behavior that is carried out by people, via electronic forms of communication, continuously against victims who are unable to easily defend themselves. This act is being performed via the use of social media and is referred to as ”Cyberbullying”  \n[6] . Some types of these social bullying are physical, verbal, relational, age, sex, and also indirect (eg. rumor spreading) . Cyberbullying has gained much popularity on social media platforms like Facebook, Instagram, and Twitter among others. Psychologically, cyberbullying negatively affects one’s emotions and has the long-run effect of even committing suicide. Research has shown that cyberbullying can lead to adverse outcomes like depression, anxiety, harassment, and suicide, among others [8] . Therefore, in recent times, there has been a need for researchers to come up with ways to automatically detect this bullying ","cbCaifr0yWX8vdIS","https://ap.wps.com/l/cbCaifr0yWX8vdIS","pdf",726428,1,5,"English","en",105,"# Introduction\n## Background and problem motivation\n## Related work and prior detection approaches\n## Study goal and proposed pipeline","[{\"question\":\"What problem does the document focus on?\",\"answer\":\"It focuses on detecting cyberbullying on social media, especially Twitter, using machine learning methods.\"},{\"question\":\"Which NLP feature extraction methods are compared in the study?\",\"answer\":\"The study uses Bag-of-Words and TF-IDF to convert tweets into numerical feature vectors.\"},{\"question\":\"Which machine learning model performs best for cyberbullying detection?\",\"answer\":\"Extreme Gradient Boosting (XGBoost) emerges as the best-performing classifier, regardless of using Bag-of-Words or TF-IDF features.\"}]","Machine Learning Approaches for Cyberbullying Detection | PDF",1785682709,13,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-approaches-for-cyberbullying-detection","",{"@graph":36,"@context":86},[37,54,69],{"@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/machine-learning-approaches-for-cyberbullying-detection/118263/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the document focus on?","Question",{"text":76,"@type":77},"It focuses on detecting cyberbullying on social media, especially Twitter, using machine learning methods.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which NLP feature extraction methods are compared in the study?",{"text":81,"@type":77},"The study uses Bag-of-Words and TF-IDF to convert tweets into numerical feature vectors.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning model performs best for cyberbullying detection?",{"text":85,"@type":77},"Extreme Gradient Boosting (XGBoost) emerges as the best-performing classifier, regardless of using Bag-of-Words or TF-IDF features.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},"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":21,"slug":138},19,"General","general"]