[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126145-en":3,"doc-seo-126145-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126145,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Social Networking Sites Fake Profiles Detection Using Machine Learning Techniques","The paper presents a machine learning model to determine whether a social media account is real or fake, reducing the need for manual review of large account datasets. The approach uses support vector machine classification and incorporates artificial neural networks to evaluate the likelihood that a Facebook friend request is genuine. It addresses threats such as bots and phoney profiles that enable web scraping and unauthorized access to private information. Results include a reported 7% false positive rate.","Social Networking Sites Fake Profiles Detection Using Machine Learning Techniques  \nAjaykumar Dharmireddy 1, Monika Devi Gottipalli2  \n1 Department of Electronics and Communication Engineering, 2Department of Computer Science Engineering,  \n1,2Sir C.R.Reddy College of Engineering,  \nEluru-534007 Andhra Pradesh, INDIA  \n[1](1ajaybabuji@gmail.com)[ajaybabuji@gmail.com](1ajaybabuji@gmail.com), [2](2monikadevi.g@gmail.com)[monikadevi.g@gmail.com](2monikadevi.g@gmail.com)  \nAbstract—In the present paper, we offer a model that might be applied to identify if an account is real or false. It is unnecessary to manually examine each account because our model, which uses a support vector machine as a classification technique, can simultaneously process an extensive accounts dataset. We are concerned with the community of fake accounts, and our issue is classification and clustering. We employ artificial neural networks (ANN) and machine learning (ML) to assess the likelihood that a Facebook friend request is genuine or not. The existence of bots and phoney profiles is another risk factor for personal data being collected for illicit purposes. Bots are computer programmers that can compile data about users without their knowledge. Web scraping is the term for this activity. The fact that this behaviour is legal makes it worse. Bots can be disguised or appear as false friend requests to access private information on a social networking site. Still, there is a 7% false positive rate in which our system fails to identify a fake profile correctly.  \nIndex Terms— Artificial neural networks (ANN), Machine Learning (ML), Big Data set, Phoney Profile, Fake Profile Detection.  \nI. INTRODUCTION  \nOnline social media is taking over the world these days in several ways. The amount of people utilizing social media is rapidly rising every day. The primary benefit of social media on the internet is the ease with which we can connect with others and improve our communication with them. This provided a new way of a potential attack, such as fake identity, false information, etc... A recent survey suggests that the number of accounts present on social media is far more expansive than the number of people who utilize it. Facebook is the most widely used form of social media, with 2.46 billion users worldwide as of 2017. Social networking sites are platforms to generate income from user-provided data. The typical user must know their rights are forfeited when using a Social networking site. Businesses that use social media have alot to gain at the expense of users. Facebook generates income from advertisements and data each time a user publishes a new location or new images, expresses their likes and dislikes, and tags other users in anything they post. The average American user generates roughly $26.76 per quarter. With millions of users, that sum grows quite quickly. In the current digital media, the growing reliance on computer technology has made the average person more susceptible to crimes like data breaches and potential identity theft. These attacks are  \nfrequently carried out without warning or informing the individuals whose data was compromised.  \nSocial networking sites like Facebook, Instagram, and Twitter are frequently the targets of these hacks. In the current generation, everyone's social life is now entwined with online social networking sites. Adding new friends and staying in touch with them and their updates has become a time pass. Social networking sites impact various fields, including science, education, community activism, employment, and business. Instructors may quickly reach their students using this, creating a welcoming environment for them to learn. Teachers are becoming more familiar with these sites and using them to provide online classroom pages and assignments, hold conversations, and perform other activities that greatly enhance learning. Employers can utilize these social networking sites to find brilliant candidates wh","cbCairDa86VYmBbc","https://ap.wps.com/l/cbCairDa86VYmBbc","pdf",1932809,4,1,7,"English","en",105,"# Introduction\n# Literature Survey","[{\"question\":\"What problem does the paper address in social networking sites?\",\"answer\":\"It focuses on identifying fake profiles and bots that may manipulate social platforms and enable illicit access to personal data.\"},{\"question\":\"Which machine learning methods are used for detection?\",\"answer\":\"The model uses support vector machines for classification and also employs artificial neural networks to assess the likelihood that friend requests are genuine.\"},{\"question\":\"How is detection performance characterized in the paper?\",\"answer\":\"The study reports an approximate 7% false positive rate, meaning the system occasionally fails to identify a fake profile correctly.\"}]","Social Networking Sites Fake Profiles Detection Using Machine Learning Techniques | PDF",1785903389,18,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"social-networking-sites-fake-profiles-detection-using-machine-learning-techniques","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/social-networking-sites-fake-profiles-detection-using-machine-learning-techniques/126145/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",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 paper address in social networking sites?","Question",{"text":76,"@type":77},"It focuses on identifying fake profiles and bots that may manipulate social platforms and enable illicit access to personal data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning methods are used for detection?",{"text":81,"@type":77},"The model uses support vector machines for classification and also employs artificial neural networks to assess the likelihood that friend requests are genuine.",{"name":83,"@type":74,"acceptedAnswer":84},"How is detection performance characterized in the paper?",{"text":85,"@type":77},"The study reports an approximate 7% false positive rate, meaning the system occasionally fails to identify a fake profile correctly.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]