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Journal articles on the topic 'Fake user'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Ferrari, Elisabetta. "Sincerely Fake: Exploring User-Generated Political Fakes and Networked Publics." Social Media + Society 6, no. 4 (2020): 205630512096382. http://dx.doi.org/10.1177/2056305120963824.

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This article investigates user-generated political satire, focusing in particular on one genre: fake political accounts. Such fakes, created as social media profiles, satirize politicians or political organizations by impersonating them. Through interviews with a sample of Italian fake accounts creators, I explore how the fakes navigate their fakeness vis-à-vis the affordances of social network sites and their publics. First, I map how the publics of the fake accounts react to the satire along two axes: one referring to the public’s understanding of the satire and the other to the uses that th

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Ms., Dilna e. p1 Ms. Maneesha Manoj2 Ms. Jiji c. j3 Ms. Jeena c. j. 4. Ms. Hrudhya k. p5. "FAKE FACE IDENTIFICATION." International Journal of Advances in Engineering & Scientific Research, ISSN: 2349 –3607 (Online) , ISSN: 2349 –4824 (Print) Vol.4,, Issue 1, Jan-2017, (2017): pp 40–48. https://doi.org/10.5281/zenodo.242479.

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<strong>Abstract: </strong> <strong>Objective-</strong> Automatic face recognition is now widely used in applications ranging from de-duplication of identity to authentication of mobile payment. This popularity of face recognition has raised concerns about face spoof attacks (also known as biometric sensor presentation attacks), where a photo or video of an authorized person’s face could be used to gain access to facilities or services. While a number of face spoof detection techniques have been proposed, their generalization ability has not been adequately addressed. We propose an efficient a

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Ms., Dilna e. p1, Maneesha Manoj2 Ms., Jiji c. j3 Ms., Jeena c. j. Ms., and Hrudhya k. p5 Ms. "FAKE FACE IDENTIFICATION." International Journal of Advances in Engineering & Scientific Research 4, no. 1 (2017): 40–48. https://doi.org/10.5281/zenodo.10774726.

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<strong>Abstract: </strong>   <strong>Objective-</strong> Automatic face recognition is now widely used in applications ranging from de-duplication of identity to authentication of mobile payment. This popularity of face recognition has raised concerns about face spoof attacks (also known as biometric sensor presentation attacks), where a photo or video of an authorized person’s face could be used to gain access to facilities or services. While a number of face spoof detection techniques have been proposed, their generalization ability has not been adequately addressed. We propose a

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M B, Ranjan. "Detection of Face Swapped Deep Fake Videos." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47643.

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ABSTRACT The Deepfake Detector is a novel application designed to enhance digital media integrity by identifying manipulated videos. This project centers on the creation and deployment of an advanced system that continuously analyzes uploaded video content to detect deepfakes. The system employs a deep learning model trained on real and fake video data, utilizing facial recognition and temporal analysis techniques. If a video is determined to be manipulated, the system informs the user with a confidence score and visual indicators, mitigating potential risks associated with deceptive media. Th

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M, Kaviarasan, and Marrynal S. Eastaff Mrs. "SPAMMER DETECTION AND FAKE USER IDENTIFICATION ON SOCIAL NETWORKS." Volume 8 Issue 10 8, no. 10 (2021): 16–18. https://doi.org/10.5281/zenodo.5675115.

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Person to person communication locales draw in large number of clients all throughout the planet. The clients' collaborations with these social locales, for example, Twitter and Facebook have an enormous effect and at times bothersome repercussions for day to day existence. The noticeable long range interpersonal communication locales have transformed into an objective stage for the spammers to scatter a gigantic measure of unimportant and malicious data. Twitter, for instance, has become quite possibly the most excessively utilized foundation ever and hence permits an absurd measure of sp

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Kumar, R. Rakesh. "SPAMMER DETECTION AND FAKE USER IDENTIFICATION ON SOCIAL NETWORKS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31639.

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The project titled "Spammer Detection and Fake User Identification on Social Networks" aims to explore and implement a novel approach to concealing information within digital images while preserving their visual integrity. With the exponential growth of social media platforms like Instagram, the issue of spam and fake user accounts has become increasingly prevalent, posing significant challenges to the integrity and user experience of these platforms. In this project, a comprehensive approach to address the problem of spammer detection and fake user identification on Instagram is proposed. Lev

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Li, Jun, Wentao Jiang, Jianyi Zhang, Yanhua Shao, and Wei Zhu. "Fake User Detection Based on Multi-Model Joint Representation." Information 15, no. 5 (2024): 266. http://dx.doi.org/10.3390/info15050266.

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The existing deep learning-based detection of fake information focuses on the transient detection of news itself. Compared to user category profile mining and detection, transient detection is prone to higher misjudgment rates due to the limitations of insufficient temporal information, posing new challenges to social public opinion monitoring tasks such as fake user detection. This paper proposes a multimodal aggregation portrait model (MAPM) based on multi-model joint representation for social media platforms. It constructs a deep learning-based multimodal fake user detection framework by an

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Nila, Udhaya, Abalin Luther, and Aathi Vignesh. "Block Chain in Fake Product Identification System Using QR Code." International Journal on Cybernetics & Informatics 10, no. 2 (2021): 73–80. http://dx.doi.org/10.5121/ijci.2021.100209.

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Every popular brand has fake manufacturers selling a counterfeited item at cheaper rates. Mostly counterfeiting medicine are selling to customer. For these factors combined with block chain technology can lead to an efficient comprehensive approach to reduce counterfeiting. Pharmaceutical organizations face many challenges regarding counterfeit medicines. Detecting fault medicines so that it will save public life. To discover the consciousness of the fake medication issue which requires an expanding security level for the appropriation of lawful pharmaceutical items. Manufacturing to user can

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Meghana,, T., J. V. B. S. Prem Sai, K. Deekshitha,, and A. Gnanesh Kumar. "Spammer Detection and Fake User Identification on Social Media." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42769.

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This paper presents about the detection of spammers and fake user accounts by using a machine learning model which is logistic regression using binary classification through a flask-based web application. The data set which is used for the training of the Machel learning model consist of 576 user profile characterized by 11 attributes which is presence of profile pic, username length, length of the full name of the user, user profile description length, presence of external URL’s, number of words in full name, is user name equals to full name, is user name public or private, number of follower

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Shukla, Dheeraj. "Deep Fake Face Detection Using Deep Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50976.

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Artificial Intelligence, deepfake technology, Generative Adversarial Networks GAN, Detection System, Detection Accuracy, User accessibility, Digital content verification. Abstract: In recent years, the rise of deepfake technology has raised significant concerns. regarding the authenticity of digital content. Deepfakes, which are synthetic media created using advanced artificial intelligence techniques, can mislead viewers and pose risks to personal privacy, public trust, and social discourse. The proposed system focuses on developing a Generative Adversarial Network (GAN)- based deepfake detec

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Gomathy, Dr C. K. "The Spammer Detection and Fake User Identification on Social Networks." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 87–92. http://dx.doi.org/10.22214/ijraset.2021.38760.

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Abstract: The Twitter has fleetly come an online source for acquiring real- time his/ her information about druggies. Twitter is an Online Social Network (OSN) where druggies can partake anything and everything, similar as news, opinions, and indeed their moods. Several arguments can be held over different motifs, similar as politics, Perticular affairs, and important events. When a stoner tweets commodity, it's incontinently conveyed to her followers, allowing them to unfold the entered information at a much broader position. With the elaboration of OSNs, the need to study and dissect druggie

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Kristo, Radion Purba, Asirvatham David, and Kumar Murugesan Raja. "Classification of instagram fake users using supervised machine learning algorithms." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 2763–72. https://doi.org/10.11591/ijece.v10i3.pp2763-2772.

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On Instagram, the number of followers is a common success indicator. Hence, followers selling services become a huge part of the market. Influencers become bombarded with fake followers and this causes a business owner to pay more than they should for a brand endorsement. Identifying fake followers becomes important to determine the authenticity of an influencer. This research aims to identify fake users' behavior and proposes supervised machine learning models to classify authentic and fake users. The dataset contains fake users bought from various sources, and authentic users. There are

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Masood, Faiza, Ghana Ammad, Ahmad Almogren, et al. "Spammer Detection and Fake User Identification on Social Networks." IEEE Access 7 (2019): 68140–52. http://dx.doi.org/10.1109/access.2019.2918196.

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Ferrari, Elisabetta. "Fake accounts, real activism: Political faking and user-generated satire as activist intervention." New Media & Society 20, no. 6 (2017): 2208–23. http://dx.doi.org/10.1177/1461444817731918.

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In this article, I explore user-generated political satire in Italy by focusing on fake political accounts. By fake accounts, I refer to humorous social media accounts that satirize a politician or a political organization through impersonation. I investigate political faking and user-generated satire as an activist intervention. Through in-depth interviews, I explore the motivations and the relationship with Italian politics of a sample of fake account creators. The results show that most of the satirists interviewed here consider satire as a form of activism and even those who do not, still

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Shirsat, Abhijeet R., Angel F. González, and Judith J. May. "Proposing a model of social media user interaction with fake news." Journal of Information, Communication and Ethics in Society 20, no. 1 (2021): 134–49. http://dx.doi.org/10.1108/jices-10-2020-0104.

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Purpose This study aims to understand the allure and danger of fake news in social media environments and propose a theoretical model of the phenomenon. Design/methodology/approach This qualitative research study used the uses and gratifications theory (UGT) approach to analyze how and why people used social media during the 2016 US presidential election. Findings The thematic analysis revealed people were gratified after using social media to connect with friends and family and to gather and share information and after using it as a vehicle of expression. Participants found a significant numb

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Sun, Ling, Yuan Rao, Yuqian Lan, Bingcan Xia, and Yangyang Li. "HG-SL: Jointly Learning of Global and Local User Spreading Behavior for Fake News Early Detection." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 5248–56. http://dx.doi.org/10.1609/aaai.v37i4.25655.

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Recently, fake news forgery technology has become more and more sophisticated, and even the profiles of participants may be faked, which challenges the robustness and effectiveness of traditional detection methods involving text or user identity. Most propagation-only approaches mainly rely on neural networks to learn the diffusion pattern of individual news, which is insufficient to describe the differences in news spread ability, and also ignores the valuable global connections of news and users, limiting the performance of detection. Therefore, we propose a joint learning model named HG-SL,

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Fu, Lifang, and Shuai Liu. "Multimodal Fake News Detection Incorporating External Knowledge and User Interaction Feature." Advances in Multimedia 2023 (July 3, 2023): 1–10. http://dx.doi.org/10.1155/2023/8836476.

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With the development of online social media, the number of various news has exploded. While social media provides an information platform for news release and dissemination, it also makes fake news proliferate, which may cause potential social risks. How to detect fake news quickly and accurately is a difficult task. The multimodal fusion fake news detection model is the current research focus and development trend. However, in terms of content, most existing methods lack the mining of background knowledge hidden in the news content and ignore the connection between background knowledge and ex

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Albahar, Marwan. "A hybrid model for fake news detection: Leveraging news content and user comments in fake news." IET Information Security 15, no. 2 (2021): 169–77. http://dx.doi.org/10.1049/ise2.12021.

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Purba, Kristo Radion, David Asirvatham, and Raja Kumar Murugesan. "Classification of instagram fake users using supervised machine learning algorithms." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 2763. http://dx.doi.org/10.11591/ijece.v10i3.pp2763-2772.

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On Instagram, the number of followers is a common success indicator. Hence, followers selling services become a huge part of the market. Influencers become bombarded with fake followers and this causes a business owner to pay more than they should for a brand endorsement. Identifying fake followers becomes important to determine the authenticity of an influencer. This research aims to identify fake users' behavior, and proposes supervised machine learning models to classify authentic and fake users. The dataset contains fake users bought from various sources, and authentic users. There are 17

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Mohamad Roshdi, Mohamad Uzair, Sue Lyn Ong, and Tak Jie Chan. "“Share or Not”, The Relationship Between User Motivations and Fake News Sharing about Political Issues in Malaysia." Journal of Communication, Language and Culture 5, no. 1 (2025): 19–36. https://doi.org/10.33093/jclc.2025.5.1.2.

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This research investigates the psychological motivations underlying the sharing of fake news on social media concerning political issues in Malaysia. Despite the growing concern about fake news on social media platforms, gaps in psychological research and the relationship between social media use and fake news sharing remain unattended within the Malaysian context. The study aims to identify the primary motivations driving social media users to share fake news on social media platforms concerning Malaysian political issues and explores potential gender differences. Using a quantitative researc

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Ge, Xiaoyi, Mingshu Zhang, Xu An Wang, Jia Liu, and Bin Wei. "Emotion-Drive Interpretable Fake News Detection." International Journal of Data Warehousing and Mining 18, no. 1 (2022): 1–17. http://dx.doi.org/10.4018/ijdwm.314585.

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Fake news has brought significant challenges to the healthy development of social media. Although current fake news detection methods are advanced, many models directly utilize unselected user comments and do not consider the emotional connection between news content and user comments. The authors propose an emotion-driven explainable fake news detection model (EDI) to solve this problem. The model can select valuable user comments by using sentiment value, obtain the emotional correlation representation between news content and user comments by using collaborative annotation, and obtain the w

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Mengutaycı, Ümmügülsüm, and Selma Ayşe Özel. "Supervised Machine Learning Based Fake Profile Detection Using User Ratings and Reviews in Recommender Systems." Journal of Advanced Research in Natural and Applied Sciences 11, no. 2 (2025): 144–55. https://doi.org/10.28979/jarnas.1657419.

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Recommendation systems produce content based on user's interests and aim to increase user satisfaction. In this way, the system keeps the user constantly active. Therefore, the reliability and robustness of these systems are essential. However, in recent years, with the influence of popular culture, recommendation systems have been struggling with fake users to highlight a particular product more or, conversely, to reduce the popularity of the product. Fake accounts mimic real user data and provide misleading information to the systems. This affects the accuracy of recommendation algorithms. T

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Gayke, P. S., Snehal Kardile, Nutan Dongare, Shweta Pathare, and Pallavi Sakat. "Spammer Detection and Fake User Identification in E-Commerce Site." International Journal of Computer Sciences and Engineering 9, no. 7 (2021): 22–25. http://dx.doi.org/10.26438/ijcse/v9i7.2225.

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Samman, Meyassara, Abeer Tashkandi, Maha Alsharif, Heba Ashi, and Lina Bahanan. "User Insights into Fake Snap-on Veneers: Perceptions and Experiences." Clinical, Cosmetic and Investigational Dentistry Volume 16 (October 2024): 419–29. http://dx.doi.org/10.2147/ccide.s489013.

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Suryawanshi, Jagjeet, Saifulla Md Abdul, Rajendra Prasad Lal, Amarajyothi Aramanda, Nazrul Hoque, and Nooraini Yusoff. "Enhanced Recommender Systems with the Removal of Fake User Profiles." Procedia Computer Science 235 (2024): 347–60. http://dx.doi.org/10.1016/j.procs.2024.04.035.

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Sivasankari, S., and Dr G. Vadivu. "Credibility Verification of Social Media Users for Detecting Fake News." Webology 18, Special Issue 03 (2021): 274–81. http://dx.doi.org/10.14704/web/v18si03/web18040.

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Fake news contains wrong information’s and mostly it spreads through social media. This is mostly done to impose some ideas and is implemented with reasons. These news containing false claims, may end up with viralized. The role of coordinated users in social media is high as they try to give fake reviews to promote or remove some YouTube videos. These coordinated users also can promote unworthy products for sale. With respect to politics, they can even change the scenario by giving negative votes. This paper implemented to verify the user credibility in social media based on their similarity

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Petit, John, Cong Li, Barbara Millet, Khudejah Ali, and Ruoyu Sun. "Can We Stop the Spread of False Information on Vaccination? How Online Comments on Vaccination News Affect Readers’ Credibility Assessments and Sharing Behaviors." Science Communication 43, no. 4 (2021): 407–34. http://dx.doi.org/10.1177/10755470211009887.

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This study used a 2 (type of news: fake vs. real) × 2 (presence of negative user comments: yes vs. no) × 2 (presence of positive user comments: yes vs. no) between-subjects experimental design to examine the differences in perceived news credibility and sharing intention between fake news and real news on vaccination. Fake news was found to generate a lower level of perceived credibility than real news, which subsequently decreased news sharing intention. Furthermore, negative user comments significantly lowered perceived news credibility, and this was especially true for real news. However, t

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Oyeniyi, Samuel A., and Joseph A. Ojeniyi. "DEVELOPMENT OF A CONCEPTUAL FRAMEWORK AND A MEASUREMENT MODEL FOR THE DETECTION OF FAKE NEWS." International Journal of Innovative Research in Advanced Engineering 8, no. 7 (2021): 138–47. http://dx.doi.org/10.26562/ijirae.2021.v0807.001.

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Fake news has been there since before the advent of the Internet. It has had an immense impact on our modern society. Detecting fake news is an important step. Although there are various ways and methods in which fake news can be detected and solved. In this research paper we discuss the various conceptual frameworks and how they affect fake news. It further shows the development of the conceptual framework and the measurement model used; showing which of the frameworks fake news is most likely to surface through. The objective of the research is to design a conceptual framework for fake news

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Moradzadeh, Sam, and Yubo Kou. ""Wow another fake game from YouTube ad": Unpacking Fake Games Through a Mixed-Methods Investigation." Proceedings of the ACM on Human-Computer Interaction 8, CHI PLAY (2024): 1–36. http://dx.doi.org/10.1145/3677115.

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Mobile games have become highly popular and profitable. While much work has been done to understand deceptive patterns of games and some unethical practices they apply, little is known about fake games, an emergent phenomenon in mobile gaming. To answer this question, we conducted two studies: a walkthrough method to characterize fake games, and a thematic analysis of user reviews to gain understanding from the user perspective. We found five types of misalignments that render a game fake and identified four primary facets of player experience with fake games. These misalignments act as realiz

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Lareki, Arkaitz, URDIN JON ALTUNA, and Juan Ignacio Martínez-de-Morentin. "Fake digital identity and cyberbullying." Media, Culture & Society 45, no. 2 (2022): 338–53. https://doi.org/10.1177/01634437221126081.

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This study aims to determine whether or not there is an association between creating fake user accounts and engaging in behaviors deemed to constitute cyberbullying. A quantitative research methodology was used with a clear descriptive and interpretative intent. The sample comprised 1989 adolescents aged between 10 and 17 years from five regions in Southern Europe, who completed an online questionnaire. The results reveal that adolescents aged 16years were the ones who engaged most in cyberbullying actions. Those who created false profiles tended to engage in more behaviors linked to cyberbull

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Rohini Bhosale, Vanita Mane. "A Hybrid Model for Detecting Fake Profiles in Online Social Networks: Enhancing User Trust." Journal of Information Systems Engineering and Management 10, no. 10s (2025): 170–85. https://doi.org/10.52783/jisem.v10i10s.1364.

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The multiplication of fake profiles in online social systems (OSNs) has developed as a basic challenge, debilitating client believe and security. This paper presents a novel cross breed show planned to distinguish fake profiles in OSNs by combining progressed machine learning methods to improve discovery precision and keep up client believe. Our approach coordinating both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models, known for their quality in preparing successive information, into a crossover show that leverages the qualities of each method for more compelling location.

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N, RAMYA. "Liveness Detector for Face Recognition System Fake Vs Real." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47812.

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Abstract - facial recognition systems are increasingly deployed for identity verification and security, but they remain vulnerable to spoofing attacks using photographs, videos, or 3D masks. To address these challenges, a liveness detection mechanism is critical to distinguish between real, live human faces and spoofed or fake inputs. This paper presents a liveness detection framework integrated with a facial recognition system, utilizing techniques such as eye-blink detection, facial micro- movements, texture analysis, and 3D depth estimation. The proposed system aims to enhance the security

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Purification, Sourav, Jinoh Kim, Jonghyun Kim, and Sang-Yoon Chang. "Fake Base Station Detection and Link Routing Defense." Electronics 13, no. 17 (2024): 3474. http://dx.doi.org/10.3390/electronics13173474.

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Fake base stations comprise a critical security issue in mobile networking. A fake base station exploits vulnerabilities in the broadcast message announcing a base station’s presence, which is called SIB1 in 4G LTE and 5G NR, to get user equipment to connect to the fake base station. Once connected, the fake base station can deprive the user of connectivity and access to the Internet/cloud. We discovered that a fake base station can disable the victim user equipment’s connectivity for an indefinite period of time, which we validated using our threat prototype against current 4G/5G practices. W

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SWAMY, Mr K. K., B. MEGHANA REDDY, K. SAI CHAITANYA, M. HARINI, and T. MEHER PRANEETH. "DETECTING UNAUTHORIZED OR FRAUD PROFILES USING ARTIFICIAL NEURAL NETWORKS." YMER Digital 21, no. 05 (2022): 404–11. http://dx.doi.org/10.37896/ymer21.05/43.

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In moment's digital age, the ever- adding reliance on computer technology has left the average citizen vulnerable to crimes similar as data breaches and possible identity theft. These attacks can do without notice and frequently without announcement to the victims of a data breach. At this time, there's little provocation for social networks to ameliorate their data security. These breaches frequently target social media networks similar as Face book, Twitter, Instagram and many other platforms. They can also target banks and other fiscal institutions. Vicious users’ produce fake accounts to p

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Ruffin, Margie, Haeseung Seo, Aiping Xiong, and Gang Wang. "Does It Matter Who Said It? Exploring the Impact of Deepfake-Enabled Profiles on User Perception towards Disinformation." Proceedings of the International AAAI Conference on Web and Social Media 18 (May 28, 2024): 1328–41. http://dx.doi.org/10.1609/icwsm.v18i1.31392.

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Recently, deepfake techniques have been adopted by real-world adversaries to fabricate believable personas (posing as experts or insiders) in disinformation campaigns to promote false narratives and deceive the public. In this paper, we investigate how fake personas influence the user perception of the disinformation shared by such accounts. Using Twitter as an exemplary platform, we conduct a user study (N=417) where participants read tweets of fake news with (and without) the presence of the tweet authors' profiles. Our study examines and compares three types of fake profiles: deepfake profi

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Pandit, Pankaj. "Fake News Detector." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33362.

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The proliferation of fake news in digital media presents a significant challenge to information integrity. This research explores the application of machine learning, specifically logistic regression, for automated fake news detection using a dataset sourced from Kaggle. Text preprocessing techniques, including tokenization, stemming, and TF-IDF vectorization, were applied to extract features from news articles. A logistic regression model was trained on the processed data to classify articles as real or fake. The model achieved high accuracy rates of 98.68% on the training set and 97.67% on t

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Nikitha, Beri, and Smt. A.Kalpana. "The Use of Artificial Neural Networks to Identify The Fake Profiles." Journal of Engineering Sciences 16, no. 04 (2025): 193–97. https://doi.org/10.36893/jes.2025.v16i04.032.

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The proliferation of fake profiles on social media and online platforms has become a growing concern, impacting user trust and the integrity of online interactions. These profiles are often used for malicious activities, such as spreading misinformation, performing fraudulent transactions, and engaging in cyberattacks. Traditional methods for detecting fake profiles rely on rule-based systems and manual verification, which are time-consuming and often ineffective. This paper explores the use of Artificial Neural Networks (ANNs) as an automated and scalable solution for identifying fake profile

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Dong, Luo, Nur Haniz Mohd Nor, Ge Bai Kai, Azian Muhamad Adzmi, and Siti Syuhada Abdul Rais. "Fake News Sharing Among Weibo Users in China." Jurnal Komunikasi: Malaysian Journal of Communication 39, no. 4 (2023): 284–305. http://dx.doi.org/10.17576/jkmjc-2023-3904-15.

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In an era where social media's impact on public discourse is increasingly pronounced, this study probes into the spread of fake news among Weibo users in China, a significant issue given the platform's massive user base and China's unique media environment. Adopting a quantitative research approach, the study primarily investigates how situational motivation and information-seeking behaviours influence the sharing of fake news. Utilising regression analysis, a method pivotal for understanding the relationship between various independent variables and the sharing of misinformation, the research

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Chiu, Ming Ming, Chong Hyun Park, Hyelim Lee, Yu Won Oh, and Jeong-Nam Kim. "Election Fraud and Misinformation on Twitter: Author, Cluster, and Message Antecedents." Media and Communication 10, no. 2 (2022): 66–80. http://dx.doi.org/10.17645/mac.v10i2.5168.

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This study determined the antecedents of diffusion scope (total audience), speed (number of adopters/time), and shape (broadcast vs. person-to-person transmission) for true vs. fake news about a falsely claimed stolen 2020 US Presidential election across clusters of users that responded to one another’s tweets (“user clusters”). We examined 31,128 tweets with links to fake vs. true news by 20,179 users to identify 1,069 user clusters via clustering analysis. We tested whether attributes of authors (experience, followers, following, total tweets), time (date), or tweets (link to fake [vs. true]

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Lahire, Mayuri. "Survey on Comprehensive Study of Fake Reviews and Reviewers Detection using machine learning techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 769–75. http://dx.doi.org/10.22214/ijraset.2022.40743.

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Abstract: Individuals and businesses are increasingly using opinionated social media, such as product evaluations, to make decisions. People, however, try to game the system for profit or fame by opinion spamming (e.g., creating bogus reviews) to promote or demote certain specific items. Such bogus reviews should be identified in order for reviews to reflect real user experiences and opinions. Most of the consumers are influenced by the online reviews on the product and it plays a crucial role in finalizing purchase decisions in the market. But fake reviewers or spammers misused and take advan

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Alim, Al Ayub Ahmed, Aljarbouh Ayman, Kumar Donepudi Praveen, and Suh Choi Myung. "Detecting Fake News using Machine Learning: A Systematic Literature Review." Psychology And Education 58, no. 1 (2021): 1932–39. https://doi.org/10.5281/zenodo.4494366.

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Internet is one of the important inventions and a large number of persons are its users. These persons use this for different purposes. There are different social media platforms that are accessible to these users. Any user can make a post or spread the news through these online platforms. These platforms do not verify the users or their posts. So some of the users try to spread fake news through these platforms. This fake news can be propaganda against an individual, society, organization, or political party. A human being is unable to detect all this fake news. So there is a need for machine

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Melià-Seguí, Joan, Eugene Bart, Rui Zhang, and Oliver Brdiczka. "An empirical approach for fake user detection in location-based social networks." Journal of Ambient Intelligence and Smart Environments 9, no. 6 (2017): 643–57. http://dx.doi.org/10.3233/ais-170464.

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Deepika, A. "Using Machine Learning for Social Network Spammer Detection & Fake User Identification." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (2024): 3202–6. http://dx.doi.org/10.22214/ijraset.2024.59610.

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Abstract: In this research, various methods for Twitter spam detection are identified, and these methods are categorized into multiple categories to offer a taxonomy. For categorization, we have found four ways to report spammers which can be useful in spotting phony user IDs. The following criteria can be used to identify spammer

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Negi, Pallavi, and Monica Bedi. "Examining the Effect of Fake News Awareness on Social Media Users’ News Sharing Behavior." Informing Science: The International Journal of an Emerging Transdiscipline 27 (2024): 014. https://doi.org/10.28945/5397.

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Aim/Purpose: Despite the widespread presence of fake news on the internet, many individuals continue to share information without verifying its accuracy. In response, this study examined two types of news-sharing behaviors, Unverified News Sharing and Authenticating News Before Sharing, and their influence on the spread of fake news on social media. Fake news awareness was also incorporated into the paper as a moderating factor. Background: The proposed conceptual model illustrates how an individual’s general approach to news sharing can predict the behavior of fake news sharing. The model was

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Sineglazov, Victor, and Kyrylo Bylym. "Twitter Fake News Detection Using Graph Neural Networks." Electronics and Control Systems 4, no. 78 (2023): 26–33. http://dx.doi.org/10.18372/1990-5548.78.18259.

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This article is devoted to the intellectual processing of text information for the purpose of detecting rail news. To solve the given task, the use of deep graph neural networks is proposed. Fake news detection based on user preferences is augmented with deeper graph neural network topologies, including Hierarchical Graph Pooling with Structure Learning, to improve the graph convolution operation and capture richer contextual relationships in news graphs. The paper presents the possibilities of extending the framework of fake news detection based on user preferences using deep graph neural net

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Zhang, Amy, Aaron Brookhouse, Daniel Hammer, Francesca Spezzano, and Liljana Babinkostova. "Predicting the Influence of Fake and Real News Spreaders (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 13107–8. http://dx.doi.org/10.1609/aaai.v36i11.21690.

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We study the problem of predicting the influence of a user in spreading fake (or real) news on social media. We propose a new model to address this problem which takes into account both user and tweet characteristics. We show that our model achieves an F1 score of 0.853, resp. 0.931, at predicting the influence of fake, resp. real, news spreaders, and outperforms existing baselines. We also investigate important features at predicting the influence of real vs. fake news spreaders.

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Deokate, Prof B. J. "Detecting Fake News Using Social Media Platforms." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 1115–20. http://dx.doi.org/10.22214/ijraset.2021.38561.

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Abstract: Fake news detection is an interesting topic for computer scientists and social science. The recent growth of the online social media fake news has great impact to the society. There is a huge information from disparate sources among various users around the world. Social media platforms like Facebook, WhatsApp and Twitter are one of the most popular applications that are able to deliver appealing data in timely manner. Developing a technique that can detect fake news from these platforms is becoming a necessary and challenging task. This project proposes a machine learning method whi

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Manu, Vasudevan Unni Jeevananda S. Jacob Joseph Kalapurackal Saba Fatma. "Enhancing authenticity and trust in social media: an automated approach for detecting fake profiles." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 1 (2024): 292–300. https://doi.org/10.11591/ijeecs.v35.i1.pp292-300.

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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 usi

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Fahmy, Sara G., Khaled M. Abdelgaber, Omar H. Karam, and Doaa S. Elzanfaly. "Modeling the Influence of Fake Accounts on User Behavior and Information Diffusion in Online Social Networks." Informatics 10, no. 1 (2023): 27. http://dx.doi.org/10.3390/informatics10010027.

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The mechanisms of information diffusion in Online Social Networks (OSNs) have been studied extensively from various perspectives with some focus on identifying and modeling the role of heterogeneous nodes. However, none of these studies have considered the influence of fake accounts on human accounts and how this will affect the rumor diffusion process. This paper aims to present a new information diffusion model that characterizes the role of bots in the rumor diffusion process in OSNs. The proposed SIhIbR model extends the classical SIR model by introducing two types of infected users with d

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SAI SREE, M. SYMALA, K.TEJASWI, K.SREEJA, and K. SNEHA REDDY. "USE ARTIFICIAL NEURAL NETWORKS TO IDENTIFY FAKE PROFILES X_0005." Journal of Engineering Sciences 15, no. 10 (2024): 103–9. http://dx.doi.org/10.36893/jes.2024.v15i10.013.

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we use machine learning, namely an artificial neural network to determine what are the chances that Facebook friend request is authentic or not. We also outline the classes and libraries involved. Furthermore, we discuss the sigmoid function and how the weights are determined and used. Finally, we consider the parameters of the social network page which are utmost important in the provided solution. The other dangers of personal data being obtained for fraudulent purposes is the presence of bots and fake profiles. Bots are programs that can gather information about the user without the user ev

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