MACHINE LEARNING ALGORITHMS FOR TELEGRAM SPAM FILTERING

Authors

  • Abubakar Hassan
    Department of Computer Engineering, University of Maiduguri
  • Yusuf Ayuba
    Department of Computer Engineering, University of Maiduguri
  • Mohammed Aji Wajiro
    Directorate of Information and Communication Centre, Ramat Polytechnic Maiduguri
  • Muhammad Zaharadeen Ahmad
    Department of Computer Engineering, University of Maiduguri

Keywords:

Extreme Gradient Boosting, Light Gradient Boosting Machine, CatBoost, Support Vector Machine, K-Nearest Neighbour

Abstract

With unprecedented usage of social media applications to interact in virtual communities, bad entities can now use these platforms to spread their malicious activities such as spam, hate speech, and even phishing to a very large population. Especially, Telegram is suitable for these kinds of activities because it is a new cloud-messenger that is highly popular among bloggers and media around the world, established by Pavel Durov in 2013. As a result, it is necessary for social media platforms to develop algorithms to filter these malicious contents. This paper employs Machine learning algorithms to filter spam messages in Telegram. Dataset obtained from Kaggle was used for the experiments in this paper. Five machine learning models were applied, namely, Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), CatBoosting, Support Vector Machine (SVM) and K-Nearest Neighbours (KNN). Experimental results showed that SVM outperforms other machine learning models used for the study with a classification accuracy of 94%. This is an indication that SVM is a promising algorithm for Spam filtering in Telegram if adopted.

Dimensions

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Published

05-12-2024

How to Cite

MACHINE LEARNING ALGORITHMS FOR TELEGRAM SPAM FILTERING . (2024). FUDMA JOURNAL OF SCIENCES, 8(6), 170-176. https://doi.org/10.33003/fjs-2024-0806-2799

Issue

Vol. 8 No. 6 (2024): FUDMA Journal of Sciences - Vol. 8 No. 6

Section

Research Articles
Copyright & Licensing

How to Cite

MACHINE LEARNING ALGORITHMS FOR TELEGRAM SPAM FILTERING . (2024). FUDMA JOURNAL OF SCIENCES, 8(6), 170-176. https://doi.org/10.33003/fjs-2024-0806-2799

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