[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120185-en":3,"doc-seo-120185-105":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":4,"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},120185,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",6,"Technology","Suspicious Account Detection Using Machine Learning Techniques - Article","Social networking sites have become central to everyday communication, while exposing users to security risks such as identity theft, privacy violations, fraud, and harassment. This work focuses on distinguishing genuine profiles from suspicious profiles on platforms like Facebook, Instagram, and Twitter using machine learning and natural language processing techniques. The study targets the effectiveness of text and profile-based signals to detect threats regardless of location or time. Approaches discussed include support vector machine (SVM) and Naive Bayes algorithms to improve classification accuracy.","Journal of Science and Technology  \nISSN: 2456-5660 Volume 8, Issue 07 (July-2023)  \n [www.jst.org.in](www.jst.org.in) DOI:[https://doi.org/10.46243/jst.2023.v8.i07.pp124-132](https://doi.org/10.46243/jst.2023.v8.i07.pp124-132)   \nSuspicious Account Detection Using Machine  \nLearning Techniques  \nAfshan Anjum1 | B.Anvesh kumar2 |Dr.V.Bapuji3  \n1Department of MCA, Vaageswari College of Engineering, Karimnagar, 2 Assistant Professor,Department of MCA,Vaageswari College of Engineering, Karimnagar, 3 Professor & HoD,Department of MCA, Vaageswari College of Engineering, Karimnagar,  \nTo Cite this Article  \n. Afshan Anjum, B.Anvesh kumar, Dr.V.Bapuji,“Suspicious Account Detection Using Machine Learning Techniques” Journal of Science and Technology, Vol. 08, Issue 07,- July 2023, pp124-132  \nArticle Info  \nReceived: 04-06-2023 Revised: 09-07-2023 Accepted: 17-07-2023 Published: 27-07-2023  \nABSTRACT  \nIn the current generation, social networking sites have become an integral part of life for most people. On social networking sites such as Facebook, Instagram, and Twitter, thousands of people create their profiles daily, interacting with each based on the classification for detecting Suspicious accounts on social networks. Here the traditionally way has been used for different classification methods in this paper.  \nThe implementation of machine learning and natural language processor (NLP) techniques are done to enhance the accuracy of others regardless of location and time. Our goal is to understand who encourages threats in social networking profiles. To determine which social network profiles are genuine and which ones are Suspicious profiles, The support vector machine (SVM) and Naves bays algorithm technique can also be applied to achieve this strategy.  \nKEYWORDS: Online Social network, Classification, Natural language processing (NLP), Facebook, Support vector machine (SVM).  \nINTRODUCTION  \nMillions of participants and billions of minutes of usage make social networking a well-liked online network. However, there are many security issues and protection concerns, particularly with the threat of identity theft. The privacy regulations imposed by social networking service providers tend to be inadequate, making them vulnerable to manipulation and misuse. The advance in technology has led to an evolution of knowledge. Machine learning algorithms have emerged, emphasizing analyzing obstacles as well as data. Although handheld devices and social media outlets revolutionize communication and improve decision-making, they tend to be prone to violation of privacy. To identify users who conceal their identities. Research has focused on trained and untrained machine learning algorithms, with the vast majority accomplishing a precision of 50% -96% . The techniques in use have become effective at ensuring personal information about users from harmful behavior.  \nIn addition to the impact of the increasing popularity of social networking sites on the web, individuals are becoming more susceptible to increasing security dangers and weaknesses including being subjected to violations of privacy, fraud identities, unsafe programs, suspicious profiles, and harassment based on gender. To resolve those problems, security providers provide protective systems and surveillance technologies. Online interaction monitoring tools like those offered by monitoring make it easy to identify users and address security issues. as open social network (OSN) usage increases, it will become critical to address such problems by developing feasible solutions.  \nPublished by: Longman Publishers [www.jst.org.in](www.jst.org.in)  \nPage | 124  \nI. RELATED WORK  \nSocial media platforms have gotten progressively More widespread since users upload data as well as personal data on websites. Multiple techniques have been employed by hackers and cybercriminals to obtain user account credentials and private data Researchers and organizations are working on tools to spot suspi","cbCaiktDdfDahfAR","https://ap.wps.com/l/cbCaiktDdfDahfAR","pdf",360916,1,9,"English","en",105,"# Article Info\n## Abstract and Keywords\n## Introduction\n## Related Work\n## Proposed Detection Framework (Figure 1)\n## Cycle for Detection (Steps)","[{\"question\":\"What problem does the paper address?\",\"answer\":\"The paper addresses how to detect suspicious accounts on online social networks and reduce threats such as identity theft and privacy abuse.\"},{\"question\":\"Which machine learning methods are used for detection?\",\"answer\":\"It discusses using support vector machine (SVM) and Naive Bayes algorithms for classifying profiles as genuine or suspicious.\"},{\"question\":\"How does natural language processing contribute?\",\"answer\":\"Natural language processing is used to analyze profile-related text information to enhance the accuracy of suspicious account detection.\"}]","Suspicious Account Detection Using Machine Learning Techniques - Article | PDF",1785728599,23,{"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},"suspicious-account-detection-using-machine-learning-techniques-article","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/suspicious-account-detection-using-machine-learning-techniques-article/120185/",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","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"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 how to detect suspicious accounts on online social networks and reduce threats such as identity theft and privacy abuse.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning methods are used for detection?",{"text":80,"@type":76},"It discusses using support vector machine (SVM) and Naive Bayes algorithms for classifying profiles as genuine or suspicious.",{"name":82,"@type":73,"acceptedAnswer":83},"How does natural language processing contribute?",{"text":84,"@type":76},"Natural language processing is used to analyze profile-related text information to enhance the accuracy of suspicious account detection.","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},[93,97,101,105,110,113,118,123,127,130,134],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"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"]