The Influence of AI-Driven Technologies on Social Media Marketing Strategies

Authors

Nang Thiri San.

Faculty of Information Science, University of Information Technology Yangon, Myanmar (Myanmar)

Hlaing Htake Khaung Tin

Faculty of Information Science, University of Information Technology Yangon, Myanmar (Myanmar)

Article Information

DOI: 10.51584/IJRIAS.2026.11060097

Subject Category: Computer Science

Volume/Issue: 11/6 | Page No: 1189-1197

Publication Timeline

Submitted: 2026-05-17

Accepted: 2026-05-22

Published: 2026-06-24

Abstract

The advent of artificial intelligence (AI) has transformed the way companies adopt social media marketing and enabled them to be more efficient, personalized in their actions, and interesting to the customers. The study also examines ethical and operational concerns associated with AI technologies, including algorithmic bias, content authenticity, data privacy, and implementation challenges. The paper below focuses on the impact of emerging technologies like machine learning, natural language processing, and predictive analytics on creating and executing marketing campaigns on social media. The objective of the present study is to examine the role of these technologies in marketing strategies and how it enhances social media marketing with respect to content creation, customer targeting, sentiment analysis, and enhancement of the performance of various campaigns. To conduct research, a mixed-method approach will be employed, combining both quantitative and qualitative elements, and the impact of technologies on key performance indicators like engagement rate, conversion rate, and customer acquisition cost will be evaluated through statistical analysis of empirical data. In addition to this, potential problems related to the use of AI technologies will be highlighted and considered. Overall, this research should show that technologies can be extremely helpful for social media marketing when used properly but at the same time present some challenges that need to be overcome.

Keywords

Artificial Intelligence (AI), Social Media Marketing, Machine Learning, Digital Marketing Strategy, Marketing Performance Analytics.

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References

1. M.-H. Huang and R. T. Rust, “Artificial Intelligence in Service,” Journal of Service Research, vol. 21, no. 2, pp. 155–172, 2018. [Google Scholar] [Crossref]

2. A. Nagpal, A. K. Singh, and P. Sharma, “Machine Learning in Marketing: A Review of Current Trends and Applications,” International Journal of Intelligent Systems and Applications in Engineering, vol. 10, no. 3, pp. 1–10, 2022. [Google Scholar] [Crossref]

3. S. Agarwal, “AI-Augmented Social Media Marketing: Enhancing Customer Engagement Through Intelligent Systems,” Journal of Digital Marketing, vol. 5, no. 1, pp. 45–60, 2021. [Google Scholar] [Crossref]

4. J. Uford and E. Akpan, “Artificial Intelligence in Social Media Marketing: Applications and Implications,” International Journal of Engineering Research & Technology, vol. 9, no. 6, pp. 120–126, 2020. [Google Scholar] [Crossref]

5. L. A. Tirtayani, I. M. S. Indrawan, and G. R. Dantes, “The Role of Machine Learning in Social Media Marketing: A Scoping Review,” Procedia Computer Science, vol. 216, pp. 102–109, 2023. [Google Scholar] [Crossref]

6. S. Starcevic, “The Impact of Artificial Intelligence on Social Media Marketing,” ResearchGate Preprint, 2024. [Google Scholar] [Crossref]

7. V. Jain and S. Kumar, “Artificial Intelligence in Marketing: A Systematic Literature Review,” Journal of Marketing Analytics, vol. 10, no. 4, pp. 1–15, 2022. [Google Scholar] [Crossref]

8. P. Chintalapati and S. Pandey, “Artificial Intelligence in Marketing: A Review,” Journal of Business Research, vol. 124, pp. 321–331, 2021. [Google Scholar] [Crossref]

9. Aung NW, Paing AK, Aung YM, Tin HHK. “AI-Driven Strategies for Automated Stock Trading: Opportunities and Risks”, Indian Journal of Science and Research, 6(2), 92-100, 2026. [Google Scholar] [Crossref]

10. Su, T.H., Mon, M.Y.P. and Tin, H.H.K., “Comparative Study of AI-Driven Decision Intelligence and Traditional Image Processing Based Decision Systems”, Indian Journal of Science and Research, 6(2), 101-107, 2026. [Google Scholar] [Crossref]

11. K. Crawford, Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press, 2021. [Google Scholar] [Crossref]

12. Y. K. Dwivedi, L. Hughes, E. Ismagilova et al., “Artificial Intelligence (AI): Multidisciplinary Perspectives on Emerging Challenges, Opportunities, and Agenda for Research, Practice and Policy,” International Journal of Information Management, vol. 57, 2021. [Google Scholar] [Crossref]

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