[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118077-en":3,"doc-seo-118077-105":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},118077,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Cyberbullying Messages Detection Using Machine Learning and Deep Learning","Cyberbullying has become a major contemporary concern, with severe psychological and social consequences, particularly for children. This paper presents a machine learning framework to detect cyberbullying messages and distinguish them from normal communications. A social-media dataset labeled as normal, offensive, and hate is adapted for binary cyberbullying classification. Two pipelines are explored: TF-IDF with multiple traditional classifiers and text-embedding models with deep learning. Tests show a voting classifier achieves about 96.5% accuracy, supported by RFECV evaluation.","Cyberbullying Messages Detection Using Machine Learning and  \nDeep Learning  \nJinan Redha Mutar  \nDepartment of Computer Science  \nCollage of Education, Mustansiriyah University  \nBaghdad, Iraq  \nABSTRACT  \nCyberbullying has emerged as a significant concern in contemporary times, particularly due to its severe consequences, especially for children. In this paper, we propose an innovative machine learning-based approach aimed at accurately detecting cyberbullying messages and mitigating their harmful effects. The primary objectives of our research were twofold: developing a model capable of precisely identifying cyberbullying messages while distinguishing them from regular messages. To achieve this, we utilized a dataset of social media messages, labeled as normal, offensive, or hate messages. We adapted this dataset for binary classification, differentiating between cyberbullying and non-bullying messages. Our approach involved two distinct methods: firstly, utilizing Term Frequency-Inverse Document Frequency (TF-IDF) for traditional machine learning algorithms, and secondly, embedding texts for deep learning algorithms. We employed a total of 15 classifiers and performes a comprehensive comparison. The most successful algorithms from the first method were combined into a voting classifier, which demonstrated the highest accuracy of 96.5% during testing. Additionally, we assessed the impact of Recursive Feature Elimination with Cross-Validation (RFECV) on the model's performance and compared it with our baseline approach. Although the results exhibited slight fluctuations, the voting classifier consistently outperformed others with 96. 6% accuracy. Our findings underline the effectiveness of the voting classifier based on machine learning algorithms, which delivered the most promising results. This approach holds the potential to be implemented in social media platforms or chat applications, serving as a valuable tool in the ongoing efforts to combat cyberbullying.  \nKey Words: Cyberbullying, Machine Learning, Natural Language Processing, Recursive Feature Elimination, Text Classification.  \n1. INTRODUCTION  \nThe advent of the internet and social networks has revolutionized the way people communicate, offering easy and anonymous means of interaction. However, this newfound freedom of expression has also given rise to a disturbing phenomenon known as cyberbullying, which we define as the malicious use of digital communication tools to harass, humiliate, or threaten individuals or groups [1] . Cyberbullying takes various forms, including the dissemination of pictures, videos, voice recordings, and text messages. While it was once considered a low-prevalence issue [2], the prevalence of cyberbullying has surged over the years, largely due to increased exposure on social media platforms [3] . Today, cyberbullying affects individuals of all age groups, either as victims or perpetrators. Unfortunately, social networks often fall short in providing adequate protection, resulting in severe consequences such as stress and enduring psychological effects like anxiety, depression, and tragically, even suicide [4] . Children are particularly vulnerable to these harmful effects [5-7] and the lack of effective prevention measures only exacerbates the problem [8 - 10] . These factors underscore the urgent need for tools capable of detecting and preventing cyberbullying. The vast volume of messages exchanged on social media has made cyberbullying harder to combat, as manual analysis becomes nearly impossible. Machine learning, with its ability to analyze massive amounts of data, offers a promising approach to addressing this problem.  \n[https://ijasre.net/](https://ijasre.net/ Page)[ Page](https://ijasre.net/ Page) 19  \nThe primary objective of this paper is to propose a machine learning-based approach for accurately identifying cyberbullying messages. Our approach relies on a dataset of social media messages, encompassing hate speech, offensi","cbCaifpdjuozngAL","https://ap.wps.com/l/cbCaifpdjuozngAL","pdf",632211,1,11,"English","en",105,"# Introduction\n# Related Work","[{\"question\":\"How does the paper detect cyberbullying messages?\",\"answer\":\"It builds a binary classification system to distinguish cyberbullying from non-bullying messages using both traditional TF-IDF features and deep learning text embeddings.\"},{\"question\":\"What dataset labeling strategy is used?\",\"answer\":\"The dataset contains normal, offensive, and hate labels, and the offensive and hate classes are merged into a single cyberbullying class for binary classification.\"},{\"question\":\"Which approach achieved the best results and what accuracy was reported?\",\"answer\":\"The voting classifier combining the best traditional machine learning models produced the highest reported accuracy, around 96.5% in testing (and about 96.6% consistently in comparisons).\"}]","Cyberbullying Messages Detection Using Machine Learning and Deep Learning | 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does the paper detect cyberbullying messages?","Question",{"text":76,"@type":77},"It builds a binary classification system to distinguish cyberbullying from non-bullying messages using both traditional TF-IDF features and deep learning text embeddings.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What dataset labeling strategy is used?",{"text":81,"@type":77},"The dataset contains normal, offensive, and hate labels, and the offensive and hate classes are merged into a single cyberbullying class for binary classification.",{"name":83,"@type":74,"acceptedAnswer":84},"Which approach achieved the best results and what accuracy was reported?",{"text":85,"@type":77},"The voting classifier combining the best traditional machine learning models produced the highest reported accuracy, around 96.5% in testing (and about 96.6% consistently in 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