Clickbait Prediction Through Feature Extraction and Feature Selection by Examining Attributes, Social Influence, ConTent, and Engagement

Authors

  • Nv Balaji
  • S.S. Senthil Priya

Keywords:

N\A

Abstract

This research introduces a comprehensive method for predicting clickbait through the development of a unique feature extraction algorithm. The algorithm integrates various types of features, including Content Features (both text and media), Engagement & Behavior Features, Social Influence & Virality Features, and Content Attributes to accurately identify and classify clickbait content. Clickbait typically consists of sensationalized headlines designed to generate clicks, often undermining user trust. By focusing on aspects such as hyperbolic language, engagement patterns (e.g., likes, shares, comments), and social dynamics, the model offers a detailed understanding of how clickbait spreads and attracts user attention. The system’s integration of diverse feature types results in a highly effective machine learning model, optimized for predicting clickbait in digital content. This research aims to improve the accuracy of clickbait detection and foster a more reliable online media environment by examining the relationship between content features and user interaction.

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References

Chen, Y., Wang, X., & Zhang, L. (2023). "User Engagement and Clickbait: Analyzing Interactions on Social Media." Journal of Social Media Studies, 12(1), 45-67.

Kumar, R., & Gupta, S. (2024). "Social Influence and Content Virality: The Role of Clickbait in Online Sharing." International Journal of Communication Research, 15(2), 102-120.

Li, J., Zhou, Y., & Cheng, T. (2023). "Understanding the Dynamics of Clickbait Dissemination: The Impact of Posting Time and Source Credibility." Digital Journalism, 11(3), 234-250.

Patel, R., Sharma, A., & Desai, M. (2023). "Detecting Clickbait: Linguistic Features and Patterns in Online Content." Journal of Information Technology Research, 16(4), 78-95.

Zhang, H., Xu, Y., & Liu, J. (2024). "The Role of Content Structure in Clickbait Classification: A Deep Learning Approach." Computers in Human Behavior, 145, 107-119.

Nguyen, M., & Tran, D. (2022). "Clickbait Detection using Multi-modal Approaches: Combining Text and Images." IEEE Transactions on Knowledge and Data Engineering, 34(7), 3401-3412.

Torres, P., & Hidalgo, M. (2022). "Engagement Metrics and Clickbait: Evaluating User Interaction with Online News." Journal of Data Science and Analytics, 14(3), 189-205.

Oliveira, J., & Costa, P. (2022). "The Spread of Clickbait Content Across Social Networks: A Case Study on Virality." Social Network Analysis and Mining, 12(4), 112-126.

Singh, R., & Kaur, S. (2023). "Impact of Hyperbolic Language in Clickbait Detection: An Experimental Study." Journal of Artificial Intelligence Research, 19(2), 123-145.

Al-Mahmoud, M., & Hassan, F. (2022). "Sentiment and Emotional Analysis of Clickbait Headlines: A Deep Learning Perspective." ACM Transactions on Information Systems (TOIS), 40(5), 12-25.

Lee, H., & Kim, D. (2023). "Deep Learning for Clickbait Detection: Integrating Text, Image, and User Behavior." IEEE Access, 11, 45567-45578.

Gupta, N., & Roy, S. (2022). "Analyzing the Role of Influencers in Clickbait Propagation." Social Computing and Behavioral Modeling, 19(3), 301-315.

Farid, A., & Ahmed, M. (2023). "A Comparative Study of Machine Learning Algorithms for Clickbait Classification." Journal of Information and Computational Science, 25(6), 563-580.

Rahman, A., & Hasan, R. (2022). "The Influence of Hashtags in Clickbait: A Feature-Based Analysis." Journal of Digital Marketing and Analytics, 9(4), 201-218.

Chan, L., & Lin, Y. (2023). "Feature Extraction for Clickbait Detection Using Multi-dimensional Analysis." Journal of Computational Linguistics, 17(5), 432-450

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Published

2025-05-15

How to Cite

1.
Balaji N, Priya SS. Clickbait Prediction Through Feature Extraction and Feature Selection by Examining Attributes, Social Influence, ConTent, and Engagement. J Neonatal Surg [Internet]. 2025May15 [cited 2025Sep.21];14(24S):273-84. Available from: https://jneonatalsurg.com/index.php/jns/article/view/5925

Issue

Vol. 14 No. 24S (2025): Journal of Neonatal Surgery

Section

Original Article

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