Fake Profile Detection on Social Networking Websites Using Machine Learning

A. Ramesh Babu, Fahimuddin Shaik, K. Devi Sri, M. Keerthi, S. Karuna Sree, K. Nandini

2025

Abstract

This project will address the difficult Addressing the issue of detecting fake profiles on social media platforms to an interactive and engaging approach using and Streamlit based user-friendly application built This new tool empowers users to seamlessly upload their datasets, preprocess the data, and evaluate a number of support vector machine learning models, specifically Support Vector Machines (SVM), Random Forest Classifiers, and Neural Networks. SVMs are well-known as, one class of machine learning tools, used for in this work, we focus on the aforementioned work which yields expressed structures, examples that behave well in high- dimensional spaces and also hold resilience to overfitting. Random Forests are recognized for their ability to manage complex interactions in data through the use of ensemble methods while Neural Networks employ sophisticated techniques to learn from complex patterns. Random Forest Classifier is one of them that gives a considerably higher performance compared to other models when it comes to predicting fake profiles. Random Forests work organizing the predictions from many decisional stumps, improving their capacity of handling high dimensional data and lifting subtile structures that can be missed by simpler models. This hybrid class of method has the advantage of being able to use the features of all the participant models, reducing the likelihood of overfitting while enhancing the model's ability to adapt effectively, making it especially effective in the diverse and dynamic landscape of social media data, where interactions and patterns can be highly complex and varied. By combining intuitive design with detailed performance analysis, the application not only addresses the immediate challenge of fake profile detection but also encourages continuous improvement and adaptation.

Paper Citation

in Harvard Style

Babu A., Shaik F., Sri K., Keerthi M., Sree S. and Nandini K. (2025). Fake Profile Detection on Social Networking Websites Using Machine Learning. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 21-26. DOI: 10.5220/0013876200004919

in Bibtex Style

@conference{icrdicct`2525,
author={A. Babu and Fahimuddin Shaik and K. Sri and M. Keerthi and S. Sree and K. Nandini},
title={Fake Profile Detection on Social Networking Websites Using Machine Learning},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={21-26},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013876200004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Fake Profile Detection on Social Networking Websites Using Machine Learning
SN - 978-989-758-777-1
AU - Babu A.
AU - Shaik F.
AU - Sri K.
AU - Keerthi M.
AU - Sree S.
AU - Nandini K.
PY - 2025
SP - 21
EP - 26
DO - 10.5220/0013876200004919
PB - SciTePress