[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124005-en":3,"doc-seo-124005-105":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},124005,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Silent Agony: Automated Detection of Ethnic and Religious Cyberbullying Using Machine Learning","Electronic devices and social media have expanded rapidly, bringing widespread benefits while also increasing toxicity, bullying, extremism, and cruelty online. Cyberbullying harms individuals and communities and has become especially concerning in the digital era, creating demand for automated detection systems. This study applies machine learning models—Logistic Regression, Multinomial Naive Bayes, K-Nearest Neighbor, and Extreme Gradient Boosting—to a Twitter text dataset targeting ethnic and religious cyberbullying. Text is converted to numeric representations using Bag of Words and TF-IDF, and results show XGboost and Logistic Regression deliver the highest performance.","University of Central Florida  \nSTARS  \nData Science and Data Mining  \nFall 2023  \nSilent Agony: Automated Detection of Ethnic and Religious Cyberbullying Using Machine Learning  \nEmil Agbemade  \nUniversity of Central Florida, [emil.agbemade@ucf.edu](emil.agbemade@ucf.edu)  \n Part of the Data Science 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  \nAgbemade, Emil, \"Silent Agony: Automated Detection of Ethnic and Religious Cyberbullying Using Machine Learning\"(2023) . Data Science and Data Mining. 13.  \n[https://stars.library.ucf.edu/data-science-mining/13](https://stars.library.ucf.edu/data-science-mining/13)  \nSilent Agony: Automated Detection of Ethnic and Religious Cyberbullying Using Machine Learning  \nEmil Agbemade∗1  \n1 University of Central Florida, Department of Statistics and Data Science  \nJanuary 9, 2024  \nAbstract  \nThe use of electronic mobile devices, social media, and networking websites has increased tremendously in recent years. Despite the advantages of these systems, such as exchanging ideas and information, being sociable, and providing entertainment, users may encounter adverse behaviors like toxicity, bullying, extremism, and cruelty. The prevalence of such behaviors has grown signifcantly in cyberspace, posing a threat to individuals and communities. To address this issue, there is a high demand for automated cyberbullying detection systems. Machine learning algorithms have been widely used to build such systems by classifying and detecting cyberbullying. In this study, we employed popular machine learning models such as Logistic Regression (LR), Multinomial Naive Bayes (MNB), K-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGboost) on a Twitter textual dataset to detect cyberbullying related to ethnicity and religion. To convert the textual data into numerical sets, we used feature extraction techniques such as Bag of Words and TF-IDF. Our results indicate that XGboost and LR achieve the highest performance.  \nKeywords: cyberbullying, social media, machine learning, classifcation, feature extraction  \n1 Introduction  \nSocial networking has gained popularity recently, especially among young folks. Through multimedia such as text, video, picture, and audio, they converse, exchange ideas, and learn from one another. Even if these online social networks have made it easier for people to communicate with one another, some people continue to be the victims of repeated purposeful or hostile behaviors carried out by one person or a group of people. Cyberbullying is a term used to describe this sort of behavior [10] . According to psychological research [9], cyberbullying may have a devastating efect on a person’s mental health and even lead to suicide. Since its emergence in the early 2000s, cyberbullying has increased in frequency and severity across many online social platforms, becoming a serious problem for modern communities. As a result of extensive school closures, more screen usage, and less face-to-face social interaction brought on by the COVID-19 epidemic[14], cyberbullying has become a particularly concerning problem, as evidenced by a warning issued by UNICEF on April 15th, 2020 [1] . The prevalence of cyberbullying is quite concerning. 36.5% of middle and high school kids have experienced cyberbullying, and 87% have seen it. The impacts of cyberbullying can include poor academic performance, despair, and suicide ideation. It is crucial that cyberbullying be identifed when it occurs on digital media. Automated danger and risk detection is essential","cbCaikmMHa9U7Xdq","https://ap.wps.com/l/cbCaikmMHa9U7Xdq","pdf",1480567,1,11,"English","en",105,"# Introduction\n# Related Work","[{\"question\":\"Which machine learning models are used for detecting ethnic and religious cyberbullying?\",\"answer\":\"The study evaluates Logistic Regression, Multinomial Naive Bayes, K-Nearest Neighbor, and Extreme Gradient Boosting on Twitter text data.\"},{\"question\":\"How is Twitter text converted into model-ready features?\",\"answer\":\"Text is transformed using feature extraction methods including Bag of Words (BoW) and TF-IDF to create numerical representations.\"},{\"question\":\"What preprocessing and performance results are reported in the study?\",\"answer\":\"The experiments use a Twitter textual dataset and compare model outputs after feature extraction; XGboost and Logistic Regression achieve the highest performance.\"}]","Silent Agony: Automated Detection of Ethnic and Religious Cyberbullying Using Machine Learning | PDF",1785819773,28,{"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},"silent-agony-automated-detection-of-ethnic-and-religious-cyberbullying-using-machine-learning","",{"@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/silent-agony-automated-detection-of-ethnic-and-religious-cyberbullying-using-machine-learning/124005/",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","2026-08-04",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},"Which machine learning models are used for detecting ethnic and religious cyberbullying?","Question",{"text":76,"@type":77},"The study evaluates Logistic Regression, Multinomial Naive Bayes, K-Nearest Neighbor, and Extreme Gradient Boosting on Twitter text data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is Twitter text converted into model-ready features?",{"text":81,"@type":77},"Text is transformed using feature extraction methods including Bag of Words (BoW) and TF-IDF to create numerical representations.",{"name":83,"@type":74,"acceptedAnswer":84},"What preprocessing and performance results are reported in the study?",{"text":85,"@type":77},"The experiments use a Twitter textual dataset and compare model outputs after feature extraction; 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