Machine Learning Models to Identify and Classify Clickbait Headlines Accurately

https://doi.org/10.48185/jaai.v6i1.1148

Authors

  • Waleed Alanazi College of Computer Science and Engineering, University of Ha’il, Hail, 11800, Saudi Arabia
  • Abdulghani B. Alshaibani College of Computer Science and Engineering, University of Ha’il, Hail, 11800, Saudi Arabia
  • Badiea Abdulkarem Mohammed Al-Shaibani College of Computer Science and Engineering, University of Ha’il, Hail, 11800, Saudi Arabia
  • Zeyad Ghaleb Al-Mekhlafi College of Computer Science and Engineering, University of Ha’il, Hail, 11800, Saudi Arabia
  • Abdulraheem B. Alshaibani College of Computer Science and Engineering, University of Ha’il, Hail, 11800, Saudi Arabia
  • Anwar B. Alshaibani College of Computer Science and Engineering, University of Ha’il, Hail, 11800, Saudi Arabia

Keywords:

Machine learning, Clickbait headlines, classification, identification, news credibility

Abstract

One potential research problem related to clickbait data could be to investigate the impact of clickbait headlines on news consumption and perception of news credibility. The objective of using Machine Learning (ML) models to analyze clickbait data in this work is to determine an accurate model for identifying and classifying clickbait headlines, understand the features that make them successful, evaluate the model's performance in real-world scenarios, and compare the performance of different ML models to select the best one for clickbait classification. By achieving these objectives, the research could provide valuable insights into the mechanisms behind clickbait and the effectiveness of ML models in detecting and mitigating its impact. This research could inform the development of more effective algorithms and tools for combating clickbait and improving news literacy. The suggested methodology for detecting clickbait using machine learning involves collecting a large amount of clickbait and non-clickbait headlines, pre-processing and cleaning the data, identifying and extracting relevant features, selecting an appropriate ML algorithm, training and evaluating the model, making necessary adjustments, and deploying the final model in a production environment to detect clickbait in real-world data. The specific steps and details may vary depending on the task complexity and data availability.

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Author Biography

Badiea Abdulkarem Mohammed Al-Shaibani, College of Computer Science and Engineering, University of Ha’il, Hail, 11800, Saudi Arabia

Badiea Abdulkarem Mohammed received his BSc in Computer Science from Babylon University, Iraq in 2002, M.Tech in Computer Science from University of Hyderabad, India in 2007 and PhD from Universiti Sains Malaysia, Malaysia in 2018. He is currently an Assistant Professor in the College of Computer Science and Engineering at University of Hail, KSA. He is permanently Assistant Professor at Hodeidah University, Yemen. His research focuses on Wireless Networks, Mobile Networks, Vehicle networks, WSN, Cybersecurity, and Image Processing. He is an IEEE member, Member, IAENG member, and ASR member. In his research area, he has published many papers in reputed journals and conferences.

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Published

2025-04-15

How to Cite

Alanazi, W., Alshaibani, A. B., Al-Shaibani, B. A. M., Al-Mekhlafi, Z. G., Alshaibani, A. B., & Alshaibani, A. B. (2025). Machine Learning Models to Identify and Classify Clickbait Headlines Accurately. Journal of Applied Artificial Intelligence, 6(1), 1–17. https://doi.org/10.48185/jaai.v6i1.1148

Issue

Vol. 6 No. 1 (2025): 2025 (1) In Press

Section

Articles
Copyright & Licensing

Copyright (c) 2025 Badiea Abdulkarem Mohammed Al-Shaibani, Waleed Alanazi, Abdulghani B. Alshaibani, Zeyad Ghaleb Al-Mekhlafi, Abdulraheem B. Alshaibani, Anwar B. Alshaibani

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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