Profile Imposter Detection On Instagram Using XGboost And SVM Algorithm
- DOI
- 10.2991/978-94-6463-471-6_141How to use a DOI?
- Keywords
- XGBoost; SVM; profile imposter detection; Instagram
- Abstract
In today's world, everyone relies heavily on social media. The vast majority of individuals nowadays regularly use social media as their primary means of communication. Social networking site membership is growing exponentially every day, and many users are chatting with people across the world regardless of time or place. This offers another vector for attack, such as fabricated information. Our study focuses on determining if an Instagram account is real or fake. In order to determine whether newly provided account information is from a legitimate user or an imposter, an algorithm will be trained using historical data on both types of accounts. To identify fake profiles, we employed machine learning methods like XGBoost Algorithm and SVM. The given dataset is pre-processed using multiple different Python tools, and the resulting results are compared to build a realistic method. For the purpose of identifying fake profiles, we compare the results of the classification algorithms XGBoost and SVM Algorithm.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Shrija Madhu AU - Neeli Gowri Sreelakshmi AU - Gandham Roshitha Madhuri AU - Panapana Shanmukha AU - Dulla Venkata Rajesh AU - Yendluri Venkata Sai Bhanu PY - 2024 DA - 2024/07/30 TI - Profile Imposter Detection On Instagram Using XGboost And SVM Algorithm BT - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024) PB - Atlantis Press SP - 1453 EP - 1461 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-471-6_141 DO - 10.2991/978-94-6463-471-6_141 ID - Madhu2024 ER -