Enhancing User Trust: A Novel Hybrid Model to Detect Fake Profiles in Online Social Networks

Authors

  • Rohini Bhosale Department of Computer Engineering, Ramrao Adik Institute of Technology, D Y Patil Deemed to be University, Nerul, India Department of Computer Engineering, Pillai HOC College of Engineering &Technology, University of Mumbai
  • Vanita Mane Department of Computer Engineering, Ramrao Adik Institute of Technology, D Y Patil Deemed to be University, Nerul, India

Keywords:

Fake Profile Detection, Machine Learning, Deep Learning, Social Media Security, Natural Language Processing, Hybrid Model

Abstract

The widespread problem of counterfeit profiles in digital social networks has prompted substantial research endeavors to enhance user security and confidence. This paper focuses on profile matching on social networks. It thoroughly examines machine learning (ML) and deep learning (DL) strategies for identifying fake profiles, specifically emphasizing profile matching on social media platforms. Using a dataset from Twitter, our research involves doing a comparative examination of various machine learning models such as Naïve Bayes, Random Forest, AdaBoost, Support Vector Machines (SVM), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and a new hybrid model combining LSTM and GRU. The results indicate the efficacy of these methods, with the hybrid model surpassing others with an accuracy rate of 98.7%, along with notable precision, recall, and F1-Score measures. This study not only enhances strategies for detecting false profiles but also emphasizes the potential of hybrid deep learning models in safeguarding online social networks. The research highlights the crucial importance of Natural Language Processing (NLP) in analyzing textual material, revealing linguistic patterns that aid in identifying fraudulent profiles. We utilize a Twitter dataset to capture the real-time actions of users, acknowledging the distinct characteristics and patterns of this medium. The paper explores the importance of ML and DL, while also comparing their performances using different methods. The results of our study demonstrate that the hybrid model exhibits higher accuracy compared to other models, and it achieves a delicate equilibrium between precision and recall. This highlights its potential as a sophisticated tool for detecting fake profiles. The hybrid model’s interpretability and ability to be adapted across various social media platforms offer potential areas for future investigation. This research adds to the academic discussion on cybersecurity and has real-world applications for enhancing the dependability and security of online social interactions.

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Published

29.01.2024

How to Cite

Bhosale, R. ., & Mane, V. . (2024). Enhancing User Trust: A Novel Hybrid Model to Detect Fake Profiles in Online Social Networks. International Journal of Intelligent Systems and Applications in Engineering, 12(13s), 542 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4620

Issue

Vol. 12 No. 13s (2024)

Section

Research Article

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