International Journal of Innovative Research in Computer Science & Technology (IJIRCST) ISSN (online): 2347-5552, Volume-11, Issue-2, March 2023 https://doi.org/10.55524/ijircst.2023.11.2.9 Article ID IRP1374, Pages 44-49 www.ijircst.org Innovative Research Publication 44 A Review Article on Detection of Fake Profile on Social-Media Shamim Ahmad 1 , and Dr. Manish MadhavaTripathi 2 1 M.Tech. Scholar, Department of Computer Science and Engineering, Integral University, Lucknow, India 2 Associate Professor, Department of Computer Science and Engineering, Integral University Lucknow, India Copyright © 2023 Made Shamim Ahmad et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ABSTRACT- Nowadays, practically everyone, from a youngster to an adult, spends more time on online social media platforms, connecting with and exchanging information with individuals all over the world. The social network is becoming a popular means to communicate with people who live in different parts of the world. Because of the tremendous interconnectedness and information sharing enabled by the internet, social media platforms. This highlights the importance of establishing a system capable of detecting fake profiles on social media networks. There has been a lot of study done in this area utilising machine learning algorithms to identify fake profile, duplicate, spam, and bot accounts, and most of the fake profile accounts were effectively recognised using machine learning algorithms. This study discusses fake profile detection on social networks using machine learning. KEYWORDS- Fake Profile, Fake Identities, Security Issues, Social Network Analysis, Machine Learning I. INTRODUCTION Every user of a social networking site has a profile and can communicate with friends, exchange updates, and network with new people. These social networks leverage Web 2.0 technology, which facilitates user communication. These social networking sites expand swiftly and alter how people connect with one another. Via the online community, people with similar interests can meet and form connections. social effect During your contemporary generation, online social connections have become integral to everyone's social life. Most people are connected to some of these websites. OSNs like Instagram, Facebook, Google+, Twitter, LinkedIn, and Pinterest, among others, are expanding as a result of their free memberships and cost-free information sharing with other users. and they have become an essential part of life in today’s generation. There are several disadvantages to the expanding use of OSNs, including a higher likelihood of fraudulent profiles, identity theft, privacy breaches, etc. False profile development has increased in tandem with the growth in social network users. This has contributed to a rise in cybercrime over the past few years. Hackers exploit these sites to disseminate rumours, hate speech, and false information and to earn money unlawfully by setting up several bogus profiles. Also, people are making fictitious profiles to further their own interests, such as obtaining referral bonuses or an increase in votes in online voting systems. Researchers use social bots, which are automated programmes, to carry out their tasks. The fake accounts exist anywhere on the internet, such as on social networks, online dating websites, discussion blogs, shopping websites, etc. As social media platforms are used more often, users and platform providers are becoming increasingly concerned about the issue of fraudulent profiles. To propagate false information, con individuals, or carry out other nefarious deeds, fake profiles might be made. By examining a variety of profile data and user behaviour, machine learning techniques can be utilised to identify these fraudulent profiles. Figure 1: Analysing Model of Real Fake Detection The detection of fake profiles using machine learning involves building a model that can classify a given profile as either genuine or fake. The model can be trained using a dataset of known fake and genuine profiles. The dataset can be collected using various methods, such as manual identification by experts, crawling social media platforms, or using crowd sourcing. The features of the profiles that can be used for classification include the profile picture, name, location, age, gender, profile description, number of followers, friends, and posts, engagement rate, activity time, and content. Machine learning algorithms, such as decision trees, random forests, support vector machines, and neural networks, can be used to extract features from the profiles and classify them as genuine or fake. Once the model is trained, it can be used to classify new profiles automatically. However, the performance of the model depends on the quality of the dataset, the choice of features, and the algorithm used. The model may also need to be updated periodically to adapt to new techniques used by fake profile creators.