[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121469-en":3,"doc-seo-121469-105":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},121469,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Enhancing Fake Profile Detection Through Supervised and Hybrid Machine Learning - A Comparative Analysis","Social networks generate large volumes of user-generated content, which also enables fraudulent actors to create fake profiles that spread misinformation, conduct deception, and threaten genuine users’ privacy. This study presents a comparative approach to fake profile detection using machine learning, combining unsupervised clustering (K-means and K-medoids) with supervised classifiers. Models such as KNN, SVM, Bernoulli Naïve Bayes, logistic regression, and linear SVC are trained on analyzed behaviors, engagement metrics, and content features to capture patterns distinguishing real from deceptive accounts.","Enhancing fake profile detection through supervised and hybrid machine learning: a comparative analysis  \nIsmail Bensassi, Oussama Ndama, Mohamed Kouissi, El Mokhtar En-Naimi  \nFaculty of Science and Technology of Tangier, Abdelmalek Essaadi University of Tetuan, Tangier, Morocco  \n\n| Article history:\u003Cbr>Received Jun 28, 2024 Revised Aug 28, 2024 Accepted Sep 2, 2024 |\n| --- |\n| Keywords:\u003Cbr>Bernoulli Naïve Bayes Fake profiles detection K-medoids\u003Cbr>Linear SVC and K-means Logistic regression Supervised and unsupervised machine learning algorithms as KNN, SVM\u003Cbr>User behavior |\n\nCorresponding Author:  \nIn modern times, social networks have become ubiquitous platforms facilitating widespread information dissemination, resulting in significant daily data generation. This increase in data production encompasses a wide range of user-generated content, which in turn promotes the proliferation of fraudulent users creating fake profiles and engaging in deceptive activities. This article aims to address this challenge by employing machine learning algorithms to accurately identify fake profiles. The research involves a thorough analysis of various user behaviors, engagement metrics, and content attributes within social platforms. The primary goal is to develop robust models capable of effectively detecting deceptive profiles by meticulously examining user activities and content characteristics. The study explores the application of robust methodologies such as K-means and K-medoids clustering, alongside supervised machine learning classifiers including K-nearest neighbors (KNN), support vector machine (SVM), Bernoulli Naïve Bayes (NB), logistic regression, and linear support vector classification (SVC), specifically tailored for the detection of fake profiles.  \nThis is an open access article under the CC BY-SA license.  \nIsmail Bensassi  \nFaculty of Science and Technology of Tangier, Abdelmalek Essaadi University of Tetuan B.P 416, postal code: 90000, Tangier, Morocco  \n[Email: bensassi.ismail@gmail.com](Email: bensassi.ismail@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe rapid expansion of social media platforms in recent years has profoundly impacted various societal realms, particularly in marketing and celebrity self-promotion, which have significantly broadened their follower and fan bases. While these platforms offer extensive benefits such as widespread information dissemination, they also introduce challenges related to privacy, misinformation, and online security. A critical issue among these challenges is the widespread presence of fake profiles, which pose risks by disseminating misinformation, engaging in fraudulent activities, and compromising the privacy of genuine users. Addressing the complex challenges posed by fake profiles requires a comprehensive approach integrating technological solutions aimed at detecting, preventing, and mitigating the negative impacts associated with these deceptive entities. This study focuses on harnessing machine learning algorithms to accurately detect fake profiles on social networks. By meticulously analyzing user behaviors , engagement metrics, and content characteristics within these platforms, the goal is to develop robust models capable of effectively identifying deceptive profiles.  \nExisting studies are carried out with supervised algorithms which are generally more accurate for specific tasks with labeled data, while unsupervised algorithms are better for discovering hidden structures in unlabeled data. In our approach, we have opted for a mixture of both types of algorithm, which allows us to take advantage of the strengths of each method, using unsupervised algorithms to explore and prepare the  \ndata before applying supervised methods for accurate predictions. This combination is often used to optimize performance in situations where labeled data is scarce or expensive to obtain, and also to get the best results in terms of time and accuracy in detecting false pro","cbCaiifvBSoOEUbM","https://ap.wps.com/l/cbCaiifvBSoOEUbM","pdf",610148,1,12,"English","en",105,"# Introduction\n## Problem of fake profiles in social networks\n## Motivation for supervised–unsupervised hybrid modeling\n# Studies and Method\n## Related studies\n## Detection methods and feature distinctions","[{\"question\":\"What problem does the study address?\",\"answer\":\"The study addresses the detection of fake profiles on social networks, which enable misinformation, fraudulent activities, and privacy compromise for legitimate users.\"},{\"question\":\"Which machine learning methods are used in the proposed approach?\",\"answer\":\"It combines clustering methods (K-means and K-medoids) with supervised classifiers including KNN, SVM, Bernoulli Naïve Bayes, logistic regression, and linear SVC.\"},{\"question\":\"What types of signals/features are analyzed to distinguish fake from genuine profiles?\",\"answer\":\"The study analyzes user behaviors, engagement metrics, and content characteristics, focusing on features associated with user activity and profile statistics such as follower/friend/status-related counts.\"}]","Enhancing Fake Profile Detection Through Supervised and Hybrid Machine Learning - 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