[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122762-en":3,"doc-seo-122762-105":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":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},122762,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Classification of Cyberbullying Detection in Social Networking with Audio using Machine Learning - Approach","Rising social media usage increases exposure to cyberbullying, where harmful, false, and damaging content is posted and spread online, producing significant psychological and emotional harm for victims. Text-based analysis dominates prior work, yet cyberbullying also occurs through audio and other media. This paper presents a machine learning approach to detect cyberbullying in social data, classifying Twitter messages as cyberbullying or non-cyberbullying using datasets and comparing models with metrics such as accuracy, precision, recall, and F1 score.","Classification of Cyberbullying Detection in Social Networking with Audio using Machine Learning  \nApproach  \nBandari Saichandana1, Dr. Pille Kamakshi2  \n1Dept. of Information Technology, Kakatiya Institute of Technology & Science  \nWarangal, India  \n[M21ds001@kitsw.ac.in](M21ds001@kitsw.ac.in)  \n2Dept. of Information Technology, Kakatiya Institute of Technology & Science  \nWarangal, India  \n[pk.it@kitsw.ac.in](pk.it@kitsw.ac.in)  \nAbstract—Every day, more people use the internet and social media, which leads to an increase in cyberbullying vulnerabilities. By transmitting, posting, and spreading damaging, false, and bad stuff online, it is taking place. For those impacted, it causes psychological and emotional issues. Therefore, the development of automated tools for cyberbullying identification and prevention is essential. Recent research on identifying cyberbullying has largely focused on text-based analysis. The two most significant media in cases of cyberbullying are text and audio. In this paper, a machine learning model for detecting cyberbullying in two types of social data, namely text and audio is presented. This paper is focused on detecting majors form of Cyberbullying: cyberbullying dataset on Twitter and classify them as containing Cyberbullying or not. This paper used datasets, namely, ‘cyberbullying Dataset’. In this paper, the implementation can be done with machine learning algorithms such as logistic regression, naive bayesian classifier, and support vector machine. Also, these three algorithms were compared and evaluated with performance metrics like accuracy, precision, recall, and f1 score. The main aim is to detecting cyberbullying messages on any type of social media platform.  \nKeywords- Cyberbullying; machine learning; support vector machine; naïve bayesian classifier; logistic regression; Twitter; social media platform.  \nI. INTRODUCTION  \nAmong the millions of young people who frequent social networking sites, online information exchange is common. Social networks enable communication and information sharing with anybody, at any time, and with a large group of individuals all at once. Globally, there are more than 3 billion users of social media. The National Crime Security Council defines cyberbullying as the intentional harm or public humiliation of another person while utilizing a mobile device, a video game app, or any other method to communicate or share text, audio, photos, or videos online (NCPC). Every day of the week, at any hour, anyone can be the victim of cyberbullying. Cyberbullying can take the form of text, audio, images, or video that is posted in an anonymous way. Finding the author of this post is sometimes impossible and can be complicated. Additionally, it was impossible to delete these communications later. The most frequent bullying websites on the internet are Twitter, Instagram, Facebook, YouTube, Snapchat, Skype, and a number of social media platforms. There are certain social networking sites, like Facebook, that offer advice on how to stop bullying. It includes a section specifically explaining how  \nto report cyberbullying and avoid user blocking. When someone posts something on Instagram that the user finds uncomfortable, they might be blocked or monitored. Users can also recommend changes to the app and report violations ofour community.[1]  \nBy the use of multi-mode input sources including text, photographs, and videos, cyberbullying can take many different forms. Most study studies have mostly focused on evaluating textual content, such as comments and text messages, because at first, cyberbullying was unstructured and the usage medium was purely text. Emoji, memes, text, and image characters are still used in communication, making it difficult to spot cyberbullying. Bullying incidents are now a well-organized, multi-media data source. Websites for social media networking place a strong focus on photo sharing. As a result of these tendencies, cyberbullying behaviours ","cbCaipHqyja5pafm","https://ap.wps.com/l/cbCaipHqyja5pafm","pdf",282928,1,7,"English","en",105,"# Abstract\n# I. INTRODUCTION\n# II. REVIEW OF LITERATURE","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper addresses the detection of cyberbullying on social networking platforms, where damaging content is posted and can cause psychological and emotional effects.\"},{\"question\":\"How does the proposed approach classify cyberbullying?\",\"answer\":\"It uses machine learning models to classify social data (focused on Twitter text and audio context) into cyberbullying or non-cyberbullying categories.\"},{\"question\":\"Which performance metrics are used to evaluate the models?\",\"answer\":\"The models are compared using accuracy, precision, recall, and F1 score.\"}]","Classification of Cyberbullying Detection in Social Networking with Audio using Machine Learning - Approach | PDF",1785812772,18,{"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},"classification-of-cyberbullying-detection-in-social-networking-with-audio-using-machine-learning-approach","",{"@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/classification-of-cyberbullying-detection-in-social-networking-with-audio-using-machine-learning-approach/122762/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the paper address?","Question",{"text":75,"@type":76},"The paper addresses the detection of cyberbullying on social networking platforms, where damaging content is posted and can cause psychological and emotional effects.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed approach classify cyberbullying?",{"text":80,"@type":76},"It uses machine learning models to classify social data (focused on Twitter text and audio context) into cyberbullying or non-cyberbullying categories.",{"name":82,"@type":73,"acceptedAnswer":83},"Which performance metrics are used to evaluate the models?",{"text":84,"@type":76},"The models are compared using accuracy, precision, recall, and F1 score.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]