Exploring machine learning techniques for fake profile detection in online social networks - Article overview

Aug 4, 2026

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Online social networks have expanded rapidly, increasing the risk of compromised data integrity, privacy, and security. Fake profiles pose a major threat by intentionally hiding identities to steal or extract sensitive information and to spread rumors or misuse online data through seemingly friendly interactions. This article reviews machine learning approaches for fake profile detection and aims to support further improvement of models for faster and more effective results in online social network environments.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Online social networks and security concerns

Fake profiles and detection motivation

Online social network

Overview of OSN platforms and user scale

Online social network security threats

Traditional threats

Machine learning for fake profile detection

Role of ML in detection

Challenges and future scope

Threat detection challenges

Conclusion

Exploring machine learning techniques for fake profile detection in online social networks Bharti, Nasib Singh Gill, Preeti Gulia Department of Computer Science and Applications, Maharshi Dayanand University, Rohtak, India Article Info ABSTRACT Article history: Received May 27, 2022 Revised Jul 20, 2022 Accepted Aug 18, 2022 Keywords: Fake profile Machine learning Online social network Security threats Unsupervised learning Corresponding Author: The online social network is the largest network, more than 4 billion users use social media and with its rapid growth, the risk of maintaining the integrity of data has tremendously increased. There are several kinds of security challenges in online social networks (OSNs). Many abominable behaviors try to hack social sites and misuse the data available on these sites. Therefore, protection against such behaviors has become an essential requirement. Though there are many types of security threats in online social networks but, one of the significant threats is the fake profile. Fake profiles are created intentionally with certain motives, and such profiles may be targeted to steal or acquire sensitive information and/or spread rumors on online social networks with specific motives. Fake profiles are primarily used to steal or extract information by means of friendly interaction online and/or misusing online data available on social sites. Thus, fake profile detection in social media networks is attracting the attention of researchers. This paper aims to discuss various machine learning (ML) methods used by researchers for fake profile detection to explore the further possibility of improvising the machine learning models for speedy results. This is an open access article under the CC BY-SA license. Bharti Department of Computer Science and Applications, Maharshi Dayanand University Rohtak, Haryana, 124001, India Email: [bharti.rs.dcsa@mdurohtak.ac.in](bharti.rs.dcsa@mdurohtak.ac.in) 1. INTRODUCTION Online social network is the most heard term used these days at every place. With the growth in technology, especially the internet, the craze for online social networks (OSNs) is increasing day by day. OSNare transforming how individuals communicate with one another [1]. About 4 billion people use different social media sites to connect with friends, family, and professional colleagues. So, risks of maintaining the privacy and security of users arise when the user’s uploaded content are multimedia such as photos, and videos, and this information can be viral for a specific purpose [2]. With the rapid growth in technology, there is a growth in the number of users who use social media platforms. Billions of users have accounts on these sites. Some users create accounts on these sites and, for unethical purposes, hide their identities. Such user accounts are called fake profiles. Some people create fake accounts only for using social media for personal use like entertainment, education, and news. Still, there are some other users who hide their identity with mischievous aims. Such accounts are hazardous to our society. Detecting such profiles is essential in terms of security. Only a few researches have been done to identify fake profiles on social media platforms. Various machine learning (ML) methods are used to do this task [3] . The paper is further organized into the following sections: section 2 gives the idea about online social network where a brief discussion is made about social media. Section 3 represents online social network Security Threats in which various security issues are discussed. Section 4 deals with the concept of ML. Section 5 specifies the role ofML in fake profile detection. Section 6 presents different challenges faced during threat detection and section 7 is about the conclusion and future scope of the study. 2. ONLINE SOCIAL NETWORK Online social networks (OSN) are used significantly in the current scenario with the availabilit

FAQs

Why are fake profiles considered a significant security threat in online social networks?

Fake profiles are created to hide identities and motives, enabling attackers to steal or extract sensitive information and/or spread rumors using online interactions and misuse of available data.

What is the main goal of the paper on fake profile detection?

The paper discusses various machine learning methods used by researchers for fake profile detection and explores possibilities for improving machine learning models for quicker results.

How does the article structure its discussion after the introduction?

It outlines sections covering online social networks, security threats, the concept of machine learning, the role of ML in fake profile detection, detection challenges, and then the conclusion and future scope.

Aug 4, 2026

Upload by Jiven