Fake Profile Detection Research Articles

A Survey on the Use of Large Language Models (LLMs) in Fake News

The proliferation of fake news and fake profiles on social media platforms poses significant threats to information integrity and societal trust. Traditional detection methods, including rule-based approaches, metadata analysis, and human fact-checking, have been employed to combat disinformation, but these methods often fall short in the face of increasingly sophisticated fake content. This review article explores the emerging role of Large Language Models (LLMs) in enhancing the detection of fake news and fake profiles. We provide a comprehensive overview of the nature and spread of disinformation, followed by an examination of existing detection methodologies. The article delves into the capabilities of LLMs in generating both fake news and fake profiles, highlighting their dual role as both a tool for disinformation and a powerful means of detection. We discuss the various applications of LLMs in text classification, fact-checking, verification, and contextual analysis, demonstrating how these models surpass traditional methods in accuracy and efficiency. Additionally, the article covers LLM-based detection of fake profiles through profile attribute analysis, network analysis, and behavior pattern recognition. Through comparative analysis, we showcase the advantages of LLMs over conventional techniques and present case studies that illustrate practical applications. Despite their potential, LLMs face challenges such as computational demands and ethical concerns, which we discuss in more detail. The review concludes with future directions for research and development in LLM-based fake news and fake profile detection, underscoring the importance of continued innovation to safeguard the authenticity of online information.

Enhancing authenticity and trust in social media: an automated approach for detecting fake profiles

<p class="Abstract">Fake profile detection on social media is a critical task intended for detecting and alleviating the existence of deceptive or fraudulent user profiles. These fake profiles, frequently generated with malicious intent, could engage in different forms of spreading disinformation, online fraud, or spamming. A range of techniques is employed to solve these problems such as natural language processing (NLP), machine learning (ML), and behavioural analysis, to examine engagement patterns, user-generated content, and profile characteristics. This paper proposes an automated fake profile detection using the coyote optimization algorithm with deep learning (FPD-COADL) method on social media. This multifaceted approach scrutinizes user-generated content, engagement patterns, and profile attributes to differentiate genuine user accounts from deceptive ones, ultimately reinforcing the authenticity and trustworthiness of social networking platforms. The presented FPD-COADL method uses robust data pre-processing methods to enhance the uniformness and quality of data. Besides, the FPD-COADL method applies deep belief network (DBN) for the recognition and classification of fake accounts. Extensive experiments and evaluations on own collected social media datasets underscore the effectiveness of the approach, showcasing its potential to identify fake profiles with high scalability and precision.</p>

Fake Profile Detection on Social Networking Websites Using Machine Learning

In an age where social media has become an integral part of our lives, the challenge of detecting fake accounts on platforms like Instagram has gained significant importance. This project, titled "Instagram Fake Account Detection using Machine Learning," employs Python as its primary tool to tackle this problem. It leverages two powerful machine learning algorithms, the Random Forest Classifier and the Decision Tree Classifier, to accomplish this task. The Random Forest Classifier demonstrates remarkable performance, achieving a 100% accuracy on the training dataset and an impressive 93% accuracy on the test dataset. Meanwhile, the Decision Tree Classifier exhibits its effectiveness with a training accuracy of 92% and a test accuracy of 92%. The dataset employed in this project is composed of 576 records, each characterized by 12 distinct features. These features encompass critical aspects of Instagram profiles, including the presence of a profile picture, the ratio of numerical characters in usernames, the breakdown of full names into word tokens, the ratio of numerical characters in full names, the equality between usernames and full names, the length of user bios, the existence of external URLs, the privacy status of accounts, the number of posts, the count of followers, the number of accounts followed, and the ultimate classification of an account as "Fake" or "Not." By harnessing the capabilities of Python and these advanced machine learning models, this project endeavors to provide a robust and efficient solution for the identification of fake Instagram accounts. In doing so, it contributes to the preservation of the platform's integrity and the security of its users.

Fake Profile Detection in Instagram

Abstract: In today's society, online communities have become an integral part of daily life, with individuals actively engaging in social media interactions regardless of their location or schedule. However, the prevalence of pseudo personas has contributed to sophisticated persistent attacks and other malicious activities, raising concerns about the privacy of personal data among online community members. This research seeks to identify and address the pressing issue of fraudulent identity projection on social media platforms, particularly focusing on Instagram. By employing automated techniques such as predictive modeling and image identification combined with text analysis, the study aims to enhance the detection of fake profiles. The research utilizes the chi-square method for feature selection and applies learner-centered algorithms like Random Forest (RF) and logistic regression for categorization. The evaluation of outcomes will be based on metrics including relevance, specificity, recall, f1- value efficiency, and accuracy. The exponential growth and influence of social media globally have underscored the urgency of mitigating the proliferation of fraudulent identities, which pose challenges ranging from propaganda to racial profiling. The findings of this research are expected to contribute to the development of effective strategies for identifying and addressing fake profiles, thus safeguarding the integrity and security of online communities

Detection of Fake Profiles on Social Networking

The detection of fake profiles on social networking platforms is a pressing concern due to the proliferation of fraudulent accounts that undermine user trust and platform integrity. This paper proposes a novel framework for the automatic detection of fake profiles, leveraging the private information available within social networking platforms while respecting user privacy. The proposed scheme utilizes advanced algorithms and machine learning models to analyze various parameters, including user activity patterns, account creation details, and communication behavior, to identify potentially fraudulent accounts. Importantly, this approach ensures the preservation of user privacy by conducting analysis solely within the platform's closed environment without compromising sensitive personal information. Furthermore, the framework incorporates an alert system to notify platform administrators and users of suspicious activity indicative of fake identity creation, enabling proactive measures to prevent the spread of fake profiles and mitigate potential risks. Through the implementation of this framework, social networking companies can effectively combat the proliferation of fake profiles while upholding user privacy and fostering a safer and more trustworthy online environment for all users

Machine Learning-Based Identification of False Profiles

In the current digital era, phony profiles are a problem for social media sites like Instagram. An important problem that has surfaced on these platforms is the increase in phony profiles, which can result in fraud, false information, and privacy violations, among other problems. Because of the huge number of users, manually identifying bogus profiles takes a lot of effort and is frequently ineffective. This study explores the use of machine learning approaches for Instagram false profile detection. The study uses Random Forest methods and artificial neural networks (ANN) to identify patterns suggestive of fraudulent accounts by utilizing a large dataset. The accuracy with which these approaches can identify bogus profiles is evaluated through a thorough process of experimentation and evaluation. The outcomes demonstrate how well ANN and Random Forest work to differentiate between real Keywords - Fake profile detection,Social media fraud detection, Machine learning, ANN.

Detection of Fake Profiles on Online Social Network Platforms: Performance Evaluation of Artificial Intelligence Techniques

Detection of Fake Profiles on Online Social Network Platforms: Performance Evaluation of Artificial Intelligence Techniques

Social Net Secure using Natural Language Processing and Machine Learning

Abstract: In our contemporary era, social networking platforms have seamlessly integrated into the daily lives of the majority of individuals. With each passing day, a multitude of users create profiles on these platforms, fostering connections with others irrespective of geographical barriers or time constraints. While these platforms offer invaluable benefits to users, they also present inherent security challenges, particularly concerning the safeguarding of users' personal data. Addressing the risks associated with social networking platforms necessitates a systematic analysis of user profiles to discern potential threats. Through classification, we aim to distinguish between authentic and fraudulent profiles within these networks. Although various classification methods exist for detecting fake profiles, achieving optimal precision rates remains a formidable task. To enhance the precision rate of fake profile detection within social systems, this paper proposes the utilization of advanced Machine Learning (ML) and Natural Language Processing (NLP) techniques. Leveraging algorithms such as Support Vector Machine (SVM) and Naïve Bayes, we endeavor to refine the accuracy of identifying fraudulent profiles.

Machine Learning-Based Fake Profile Detection on Social Networking Websites

Machine Learning-Based Fake Profile Detection on Social Networking Websites

Fake Profile Detection

Fake profile detection is a critical task in maintaining the authenticity and safety of online communities. With the rising predominance of web-based entertainment stages, the presence of phony profiles has turned into a worry. This abstract explores are various techniques used to detect fake profiles including analysing user behaviour checking for inconsistencies in profile information, and employing machine learning algorithms. The detection process involves analysing patterns and anomalies in user data to identify suspicious activity. AI calculations assume a huge part in this cycle by gaining from marked datasets of veritable and counterfeit profiles. These calculations can investigate highlights, for example, profile pictures, posting conduct, network associations, and commitment examples to make expectations on the realness of a profile. However, it's important to acknowledge that no detection method is foolproof. Fake profile creators constantly evolve their strategies to evade detection. Therefore, continuous monitoring, user feedback, and updates to detection algorithms are necessary to stay ahead of these malicious actors. The ongoing efforts to detect and combat fake profiles contribute to creating a safer and more trustworthy online environment. By leveraging various techniques and advancements in machine learning, platforms strive to maintain the integrity of their user base and protect their users from potential scams and fraudulent activities.One example of a machine learning algorithm used for fake profile detection is the Random Forest algorithm. Irregular Backwoods is a well known troupe learning technique that joins various choice trees to make expectations. With regards to counterfeit profile recognition, the Irregular Backwoods calculation can be prepared on a dataset that incorporates both certified and counterfeit profiles. The calculation gains from different elements like profile data, posting conduct, network associations, and commitment designs. I trust this theoretical furnishes you with a succinct outline of phony profile location!

A Fake Profile Detection Model Using Multistage Stacked Ensemble Classification

Fake profile identification on social media platforms is essential for preserving a reliable online community. Previous studies have primarily used conventional classifiers for fake account identification on social networking sites, neglecting feature selection and class balancing to enhance performance. This study introduces a novel multistage stacked ensemble classification model to enhance fake profile detection accuracy, especially in imbalanced datasets. The model comprises three phases: feature selection, base learning, and meta-learning for classification. The novelty of the work lies in utilizing chi-squared feature-class association-based feature selection, combining stacked ensemble and cost-sensitive learning. The research findings indicate that the proposed model significantly enhances fake profile detection efficiency. Employing cost-sensitive learning enhances accuracy on the Facebook, Instagram, and Twitter spam datasets with 95%, 98.20%, and 81% precision, outperforming conventional and advanced classifiers. It is demonstrated that the proposed model has the potential to enhance the security and reliability of online social networks, compared with existing models.

Automatic Identification of Fake Profiles in Online Social Networks

In the modern era, the social life of individuals has shifted towards becoming connected with online informal communities. These communities have revolutionized the way we pursue our social life, making it easier to connect with them and keep up with their updates. However, with the rapid growth of these communities, there has been a rise in issues such as false profiles and online bullying. There is currently no practical solution to address these issues. To address this issue, we have developed a system with which the programmed identification of false profiles is feasible and efficient. This system uses order methods such as support vector machine, nave bayes and decision trees to classify the profiles into false or true classes. As this is a software-based recognition method, it can be easily connected to online informal organizations that have a large number of profiles whose profiles cannot be physically inspected. In today’s world, users are bombarded with unnecessary information during surfing, which are posted by false users. Research has shown that 20% to 40% of profiles in social networks like Facebook are fake profiles. This detection of fake profiles leads to solution using frameworks. Keywords: Online Social Network, Fake Profiles, Classification, Natural Language Processing.

Fake profile detection in Twitter

Fake profile detection in Twitter

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

<span lang="EN-US">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.</span>

Survey on Fake Profile Detection in Social Media

Survey on Fake Profile Detection in Social Media

Machine learning for False Information Detection in Social Internet of Things

By capitalizing on object relationships and local navigability, the social internet of things (SIoT) is one of the burgeoning paradigms that could solve the technical challenges of conventional IoT. Because of this paradigm's capacity to combine conventional IoT with social media, it is possible to create smart objects and services with greater utility than those created using conventional IoT infrastructures. In recent years, scholars have become interested in SIoT, leading to a plethora of works examining various mechanisms for providing services and technologies within this context. In this vein, we present a comprehensive review of recent research covering important aspects of SIoT. In this research, we give a detailed justification for the function of several machine learning paradigms and provide a practical application of it to unexamined concerns relating to erroneous data and other social IoT. First, we give a classification of false news detection approaches and an analysis of these techniques. Second, the potential uses for detecting fake news are examined at length, including how it might be applied to the areas of fake profile detection, traffic management, bullying detection, etc . We also suggested a detailed review of the possibilities of machine learning algorithms for detecting bogus news and intervening in social networks. In our paper, we introduce categories of fake news detection methods providing a comparison between these methods. After that, the promising applications for false news detection are extensively discussed in terms of fake account detection, bot detection, bullying detection, and the security and privacy of SIoT. After all, A thorough discussion of the potential of machine learning approaches for fake news detection and interventions in SIoT networks along with the state-of-the-art challenges, opportunities, and future search prospects. This article seeks for aiding the readers and researchers in explaining the motive and role of the different machine learning paradigms to offer them a comprehensive realization of so far unexplored issues related to false information and other scenarios of SIoT networks.

Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?

In the last decade, Social Networks (SNs) have deeply changed many aspects of society, and one of the most widespread behaviours is the sharing of pictures. However, malicious users often exploit shared pictures to create fake profiles, leading to the growth of cybercrime. Thus, keeping in mind this scenario, authorship attribution and verification through image watermarking techniques are becoming more and more important. In this paper, we firstly investigate how thirteen of the most popular SNs treat uploaded pictures in order to identify a possible implementation of image watermarking techniques by respective SNs. Second, we test the robustness of several image watermarking algorithms on these thirteen SNs. Finally, we verify whether a method based on the Photo-Response Non-Uniformity (PRNU) technique, which is usually used in digital forensic or image forgery detection activities, can be successfully used as a watermarking approach for authorship attribution and verification of pictures on SNs. The proposed method is sufficiently robust, in spite of the fact that pictures are often downgraded during the process of uploading to the SNs. Moreover, in comparison to conventional watermarking methods the proposed method can successfully pass through different SNs, solving related problems such as profile linking and fake profile detection. The results of our analysis on a real dataset of 8400 pictures show that the proposed method is more effective than other watermarking techniques and can help to address serious questions about privacy and security on SNs. Moreover, the proposed method paves the way for the definition of multi-factor online authentication mechanisms based on robust digital features.

Detection of Fake Profiles on Twitter Using Hybrid SVM Algorithm

Establishing and management of social relationships among huge amount of users has been provided by the emerging communication medium called online social networks (OSNs). The attackers have attracted because of the rapid increasing of OSNs and the large amount of its subscriber’s personal data. Then they pretend to spread malicious activities, share false news and even stolen personal data. Twitter is one of the biggest networking platforms of micro blogging social networks in which daily more than half a billion tweets are posted most of that are malware activities. Analyze, who are encouraging threats in social networks is need to classify the social networks profiles of the users. Traditionally, there are different classification methods for detecting the fake profiles on the social networks that needed to improve their accuracy rate of classification. Thus machine learning algorithms are focused in this paper. Therefore detection of fake profiles on twitter using hybrid Support Vector Machine (SVM) algorithm is proposed in this paper. The machine learning based hybrid SVM algorithm is used in this for classification of fake and genuine profiles of Twitter accounts and applied the dimension reduction techniques, feature selection and bots. Less number of features is used in the proposed hybrid SVM algorithm and 98% of the accounts are correctly classified with proposed algorithm.

Fake Profile Detection on Social Networking Websites: A Comprehensive Review

This article aims to summarize the recent advancement in the fake account detection methodology on social networking websites. Over the past decade, social networking websites have received huge attention from users all around the world. As a result, popular websites such as Facebook, Twitter, LinkedIn, Instagram, and others saw an unexpected rise in registered users. However, researchers claim that all registered accounts are not real; many of them are fake and created for specific purposes. The primary purpose of fake accounts is to spread spam content, rumor, and other unauthentic messages on the platform. Hence, it is needed to filter out the fake accounts, but it has many challenges. In the past few years, researchers applied many advanced technologies to identify fake accounts. In the survey presented in this article, we summarize the recent development of fake account detection technologies. We discuss the challenges and limitations of the existing models in brief. The survey may help future researchers to identify the gaps in the current literature and develop a generalized framework for fake profile detection on social networking websites.

Author profiling on bi-lingual tweets

The task of author profiling aims to distinguish the author’s profile traits from a given content. It has got potential applications in marketing, forensic analysis, fake profile detection, etc. In recent years, the usage of bi-lingual text has raised due to the global reach of social media tools as people prefer to use language that expresses their true feelings during online conversations and assessments. It has likewise impacted the use of bi-lingual (English and Roman-Urdu) text in the sub-continent (Pakistan, India, and Bangladesh) over social media. To develop and evaluate methods for bi-lingual author profiling, benchmark corpora are needed. The majority of previous efforts have focused on developing mono-lingual author profiling corpora for English and other languages. To fulfill this gap, this study aims to explore the problem of author profiling on bi-lingual data and presents a benchmark corpus of bi-lingual (English and Roman-Urdu) tweets. Our proposed corpus contains 339 author profiles and each profile is annotated with six different traits including age, gender, education level, province, language, and political party. As a secondary contribution, a range of deep learning methods, CNN, LSTM, Bi-LSTM, and GRU, are applied and compared on the three different bi-lingual corpora for age and gender identification, including our proposed corpus. Our extensive experimentation showed that the best results for both gender identification task (Accuracy = 0.882, F1-Measure = 0.839) and age identification (Accuracy = 0.735, F1-Measure = 0.739) are obtained using Bi-LSTM deep learning method. Our proposed bi-lingual tweets corpus is free and publicly available for research purposes.