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Authors: Ahmad Homsi ; Joyce Al Nemri ; Nisma Naimat ; Hamzeh Abdul Kareem ; Mustafa Al-Fayoumi and Mohammad Abu Snober

Affiliation: Department of Computer Science, Princess Sumaya University for Technology, Khalil Al-Saket Street, Amman, Jordan

Keyword(s): Twitter, ML, Detecting Fake Accounts, Spearman's Correlation, PCA, J48, Random Forest, KNN, Naive Bayes.

Abstract: Internet Communities are affluent in Fake Accounts. Fake accounts are used to spread spam, give false reviews for products, publish fake news, and even interfere in political campaigns. In business, fake accounts could do massive damage like waste money, damage reputation, legal problems, and many other things. The number of fake accounts is increasing dramatically by the enormous growth of the online social network; thus, such accounts must be detected. In recent years, researchers have been trying to develop and enhance machine learning (ML) algorithms to detect fake accounts efficiently and effectively. This paper applies four Machine Learning algorithms (J48, Random Forest, Naive Bayes, and KNN) and two reduction techniques (PCA, and Correlation) on a MIB Twitter Dataset. Our results provide a detailed comparison among those algorithms. We prove that combining Correlation along with the Random Forest algorithm gave better results of about 98.6%.

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Paper citation in several formats:
Homsi, A.; Al Nemri, J.; Naimat, N.; Abdul Kareem, H.; Al-Fayoumi, M. and Abu Snober, M. (2021). Detecting Twitter Fake Accounts using Machine Learning and Data Reduction Techniques. In Proceedings of the 10th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-521-0; ISSN 2184-285X, SciTePress, pages 88-95. DOI: 10.5220/0010604300880095

@conference{data21,
author={Ahmad Homsi. and Joyce {Al Nemri}. and Nisma Naimat. and Hamzeh {Abdul Kareem}. and Mustafa Al{-}Fayoumi. and Mohammad {Abu Snober}.},
title={Detecting Twitter Fake Accounts using Machine Learning and Data Reduction Techniques},
booktitle={Proceedings of the 10th International Conference on Data Science, Technology and Applications - DATA},
year={2021},
pages={88-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010604300880095},
isbn={978-989-758-521-0},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Data Science, Technology and Applications - DATA
TI - Detecting Twitter Fake Accounts using Machine Learning and Data Reduction Techniques
SN - 978-989-758-521-0
IS - 2184-285X
AU - Homsi, A.
AU - Al Nemri, J.
AU - Naimat, N.
AU - Abdul Kareem, H.
AU - Al-Fayoumi, M.
AU - Abu Snober, M.
PY - 2021
SP - 88
EP - 95
DO - 10.5220/0010604300880095
PB - SciTePress