Twitter Spam Accounts Detection Using Machine Learning Models

Shikah J. Alsunaidi; Rawan Talal Alraddadi; Hamoud Aljamaan

doi:10.1109/CICN56167.2022.10008339

Twitter Spam Accounts Detection Using Machine Learning Models

Shikah J. Alsunaidi, Rawan Talal Alraddadi, Hamoud Aljamaan

Department of Information and Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

The amount of spam accounts on Twitter has recently surged, which has attracted researchers' interest in seeking strategies to mitigate this problem. This paper reviews recent studies in the literature that tackled the Twitter spam accounts problem based on machine learning (ML). It then introduces an empirical study to test several ML models on a publicly access dataset. The model types were individual, ensemble, and majority voting models. It found that the ensemble ML models, and majority voting ML models can improve the prediction accuracy of Twitter spam accounts detection compared to the individual ML models. We concluded that the Random Forest model is the best for Twitter spam accounts detection using an imbalanced dataset.

Original language	English
Title of host publication	Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	525-531
Number of pages	7
ISBN (Electronic)	9781665487719
DOIs	https://doi.org/10.1109/CICN56167.2022.10008339
State	Published - 2022
Event	14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022 - Al-Khobar, Saudi Arabia Duration: 4 Dec 2022 → 6 Dec 2022

Publication series

Name	Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

Conference

Conference	14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
Country/Territory	Saudi Arabia
City	Al-Khobar
Period	4/12/22 → 6/12/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

Twitter
ensemble learning
machine learning
social media
spam account

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition

Access to Document

10.1109/CICN56167.2022.10008339

Cite this

Alsunaidi, S. J., Alraddadi, R. T., & Aljamaan, H. (2022). Twitter Spam Accounts Detection Using Machine Learning Models. In Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022 (pp. 525-531). (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CICN56167.2022.10008339

Alsunaidi, Shikah J. ; Alraddadi, Rawan Talal ; Aljamaan, Hamoud. / Twitter Spam Accounts Detection Using Machine Learning Models. Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 525-531 (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022).

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title = "Twitter Spam Accounts Detection Using Machine Learning Models",

abstract = "The amount of spam accounts on Twitter has recently surged, which has attracted researchers' interest in seeking strategies to mitigate this problem. This paper reviews recent studies in the literature that tackled the Twitter spam accounts problem based on machine learning (ML). It then introduces an empirical study to test several ML models on a publicly access dataset. The model types were individual, ensemble, and majority voting models. It found that the ensemble ML models, and majority voting ML models can improve the prediction accuracy of Twitter spam accounts detection compared to the individual ML models. We concluded that the Random Forest model is the best for Twitter spam accounts detection using an imbalanced dataset.",

keywords = "Twitter, ensemble learning, machine learning, social media, spam account",

author = "Alsunaidi, \{Shikah J.\} and Alraddadi, \{Rawan Talal\} and Hamoud Aljamaan",

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series = "Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022",

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Alsunaidi, SJ, Alraddadi, RT & Aljamaan, H 2022, Twitter Spam Accounts Detection Using Machine Learning Models. in Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022, Institute of Electrical and Electronics Engineers Inc., pp. 525-531, 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022, Al-Khobar, Saudi Arabia, 4/12/22. https://doi.org/10.1109/CICN56167.2022.10008339

Twitter Spam Accounts Detection Using Machine Learning Models. / Alsunaidi, Shikah J.; Alraddadi, Rawan Talal; Aljamaan, Hamoud.
Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 525-531 (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Alraddadi, Rawan Talal

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N2 - The amount of spam accounts on Twitter has recently surged, which has attracted researchers' interest in seeking strategies to mitigate this problem. This paper reviews recent studies in the literature that tackled the Twitter spam accounts problem based on machine learning (ML). It then introduces an empirical study to test several ML models on a publicly access dataset. The model types were individual, ensemble, and majority voting models. It found that the ensemble ML models, and majority voting ML models can improve the prediction accuracy of Twitter spam accounts detection compared to the individual ML models. We concluded that the Random Forest model is the best for Twitter spam accounts detection using an imbalanced dataset.

AB - The amount of spam accounts on Twitter has recently surged, which has attracted researchers' interest in seeking strategies to mitigate this problem. This paper reviews recent studies in the literature that tackled the Twitter spam accounts problem based on machine learning (ML). It then introduces an empirical study to test several ML models on a publicly access dataset. The model types were individual, ensemble, and majority voting models. It found that the ensemble ML models, and majority voting ML models can improve the prediction accuracy of Twitter spam accounts detection compared to the individual ML models. We concluded that the Random Forest model is the best for Twitter spam accounts detection using an imbalanced dataset.

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KW - social media

KW - spam account

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Alsunaidi SJ, Alraddadi RT, Aljamaan H. Twitter Spam Accounts Detection Using Machine Learning Models. In Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 525-531. (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022). doi: 10.1109/CICN56167.2022.10008339

Twitter Spam Accounts Detection Using Machine Learning Models

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

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Cite this