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Research Article · Open access · Published 5 December 2024

Artificial Intelligence and Mathematical Modelling-Based Techniques to Improve Social Media Marketing

Kiran Chaudhary1, Mohammad Naquibur Rahman2, Nabeela Hasan3, Mansaf Alam4 & Aakash Punit5

Journal of Computer Science, Volume 21, Issue 1, pp. 78–87 · doi.org/10.3844/jcssp.2025.78.87

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Abstract

The Businessperson is using social media to do marketing of their product. Almost all people use social media. Most people relate to social media. Social media marketing is beneficial in making the product more popular and easy ways to introduce the product to everyone in economical ways. In this study, we introduce the concept of machine learning and mathematical models to enhance social media marketing for the product. We have used the concept of data analytics in this study to analyze social media data and based on the outcome of the analytics result, we developed strategies for marketing the product. The Bayesian Regularization (BR) Model has achieved the highest accuracy of 93% and the Root mean squire error is as low as 1.2 approximately in decision tree regression (DTR). The Area Under The Curve (AUC) of the model is .57. The model will be beneficial to improving social media marketing. The business personnel will benefit from marketing products on social media networks.

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Open access. This article is licensed under a Creative Commons Attribution 4.0 International Licence (CC BY 4.0), as printed on the article, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source. No permission is needed.

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Chaudhary, K., Rahman, M. N., Hasan, N., Alam, M. & Punit, A. (2025). Artificial Intelligence and Mathematical Modelling-Based Techniques to Improve Social Media Marketing. Journal of Computer Science, 21(1), 78–87. https://doi.org/10.3844/jcssp.2025.78.87

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Volume 21, Issue 1
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Artificial Intelligence and Mathematical Modelling-Based Techniques to Improve Social Media MarketingJournal of Computer Science · Volume 21, Issue 1 · pp. 78–87 · CC BY 4.0Download