IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v12y2020i11p187-d437202.html
   My bibliography  Save this article

A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter

Author

Listed:
  • Amgad Muneer

    (Department of Computer and Information Sciences, Universiti Teknologi PETRONAS, Seri Iskandar 32160, Malaysia)

  • Suliman Mohamed Fati

    (Information Systems Department, College of Computer and Information Sciences, Prince Sultan University, Riyadh 11586, Saudi Arabia)

Abstract

The advent of social media, particularly Twitter, raises many issues due to a misunderstanding regarding the concept of freedom of speech. One of these issues is cyberbullying, which is a critical global issue that affects both individual victims and societies. Many attempts have been introduced in the literature to intervene in, prevent, or mitigate cyberbullying; however, because these attempts rely on the victims’ interactions, they are not practical. Therefore, detection of cyberbullying without the involvement of the victims is necessary. In this study, we attempted to explore this issue by compiling a global dataset of 37,373 unique tweets from Twitter. Moreover, seven machine learning classifiers were used, namely, Logistic Regression (LR), Light Gradient Boosting Machine (LGBM), Stochastic Gradient Descent (SGD), Random Forest (RF), AdaBoost (ADB), Naive Bayes (NB), and Support Vector Machine (SVM). Each of these algorithms was evaluated using accuracy, precision, recall, and F1 score as the performance metrics to determine the classifiers’ recognition rates applied to the global dataset. The experimental results show the superiority of LR, which achieved a median accuracy of around 90.57%. Among the classifiers, logistic regression achieved the best F1 score (0.928), SGD achieved the best precision (0.968), and SVM achieved the best recall (1.00).

Suggested Citation

  • Amgad Muneer & Suliman Mohamed Fati, 2020. "A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter," Future Internet, MDPI, vol. 12(11), pages 1-20, October.
  • Handle: RePEc:gam:jftint:v:12:y:2020:i:11:p:187-:d:437202
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/12/11/187/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1999-5903/12/11/187/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Maher Maalouf, 2011. "Logistic regression in data analysis: an overview," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 3(3), pages 281-299.
    2. Cynthia Van Hee & Gilles Jacobs & Chris Emmery & Bart Desmet & Els Lefever & Ben Verhoeven & Guy De Pauw & Walter Daelemans & Véronique Hoste, 2018. "Automatic detection of cyberbullying in social media text," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-22, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ebrahim A. A. Ghaleb & P. D. D. Dominic & Suliman Mohamed Fati & Amgad Muneer & Rao Faizan Ali, 2021. "The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees," Sustainability, MDPI, vol. 13(15), pages 1-33, July.
    2. José Manuel Ortiz-Marcos & María Tomé-Fernández & Christian Fernández-Leyva, 2021. "Cyberbullying Analysis in Intercultural Educational Environments Using Binary Logistic Regressions," Future Internet, MDPI, vol. 13(1), pages 1-15, January.
    3. Suliman Mohamed Fati & Amgad Muneer & Ayed Alwadain & Abdullateef O. Balogun, 2023. "Cyberbullying Detection on Twitter Using Deep Learning-Based Attention Mechanisms and Continuous Bag of Words Feature Extraction," Mathematics, MDPI, vol. 11(16), pages 1-21, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Silvia Gabrielli & Silvia Rizzi & Sara Carbone & Enrico Maria Piras, 2021. "School Interventions for Bullying–Cyberbullying Prevention in Adolescents: Insights from the UPRIGHT and CREEP Projects," IJERPH, MDPI, vol. 18(21), pages 1-13, November.
    2. Okoli Jude Emeka & Haslinda Nahazanan & Bahareh Kalantar & Zailani Khuzaimah & Ojogbane Success Sani, 2021. "Evaluation of the Effect of Hydroseeded Vegetation for Slope Reinforcement," Land, MDPI, vol. 10(10), pages 1-23, September.
    3. Jinyu Huang & Zhaohao Zhong & Haoyuan Zhang & Liping Li, 2021. "Cyberbullying in Social Media and Online Games among Chinese College Students and Its Associated Factors," IJERPH, MDPI, vol. 18(9), pages 1-12, April.
    4. Sheunesu Brandon Shamuyarira & Trust Tawanda & Elias Munapo, 2023. "Truck Fuel Consumption Prediction Using Logistic Regression and Artificial Neural Networks," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 14(1), pages 1-17, January.
    5. Carlos Carrasco-Farré, 2022. "The fingerprints of misinformation: how deceptive content differs from reliable sources in terms of cognitive effort and appeal to emotions," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-18, December.
    6. Laura R. Persky & Janet L. Walsh & Ken Pinnock, 2023. "Creating Positive Workplace Culture To Reduce Workplace Bullying," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 17(1), pages 43-53.
    7. Sumita, Kazuto & Nakazawa, Katsuyoshi & Kawase, Akihiro, 2021. "Long-term care facilities and migration of elderly households in an aged society: Empirical analysis based on micro data," Journal of Housing Economics, Elsevier, vol. 53(C).
    8. Shuaa A. Aljasir & Maisoon O. Alsebaei, 2022. "Cyberbullying and cybervictimization on digital media platforms: the role of demographic variables and parental mediation strategies," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.
    9. Xieling Chen & Di Zou & Haoran Xie & Gary Cheng, 2021. "A Topic-Based Bibliometric Review of Computers in Human Behavior: Contributors, Collaborations, and Research Topics," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
    10. Daniel Falla & Rosario Ortega-Ruiz & Eva M. Romera, 2021. "Mechanisms of Moral Disengagement in the Transition from Cybergossip to Cyberaggression: A Longitudinal Study," IJERPH, MDPI, vol. 18(3), pages 1-12, January.
    11. Carmen Patino-Alonso & Marta Gómez-Sánchez & Leticia Gómez-Sánchez & Benigna Sánchez Salgado & Emiliano Rodríguez-Sánchez & Luis García-Ortiz & Manuel A. Gómez-Marcos, 2022. "Predictive Ability of Machine-Learning Methods for Vitamin D Deficiency Prediction by Anthropometric Parameters," Mathematics, MDPI, vol. 10(4), pages 1-16, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:12:y:2020:i:11:p:187-:d:437202. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.