A Data-Driven Approach: Assessing the Relevance of AI Algorithms in Tailoring Personalised Content for Social Media Users

Ingrid Georgeta Apostol, Mihai Prelipcean, Elena Bostanica and Maria-Cristiana Munthiu
Additional contact information
Ingrid Georgeta Apostol: Bucharest University of Economic Studies, Bucharest, Romania
Mihai Prelipcean: Bucharest University of Economic Studies, Bucharest, Romania
Elena Bostanica: Bucharest University of Economic Studies, Bucharest, Romania
Maria-Cristiana Munthiu: Bucharest University of Economic Studies, Bucharest, Romania

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ECONOMICS AND SOCIAL SCIENCES, 2024, vol. 6, issue 1, 994-1003

Abstract: In the rapidly evolving landscape of social media platforms, the delivery of personalised content has become crucial to engage users and foster meaningful interactions. Artificial Intelligence (AI) algorithms offer promising solutions to this challenge by leveraging vast amounts of user data to tailor content recommendations to individual preferences. This research presents a comprehensive quantitative analysis aimed at evaluating the effectiveness of AI algorithms in personalising content for social media users. The key findings will provide valuable insights into the effectiveness of various AI algorithms in delivering personalised content across different social media contexts. We aim to see if AI-driven personalisation significantly enhances user engagement, with tailored content receiving higher interaction rates compared to non-personalised content. Furthermore, this article study if exists factors that influence the success of AI-based personalisation efforts, including user demographics, content characteristics, and platform-specific features. The analysis highlights the importance of considering these factors when designing and implementing AI-driven content personalisation strategies. The current state of the scientific literature reveals a growing interest in the use of AI for content personalisation in social media. While previous studies have highlighted the potential benefits of AI-driven personalisation, there remains a need for empirical evidence to quantify its effectiveness and understand its impact on user engagement and satisfaction. The research questions from the questionnaire focus on quantifying the impact of personalised content on user engagement, content relevance, and user satisfaction. Overall, this study contributes to advancing our understanding of the role of AI in content personalisation and its impact on user experiences in social media environments. Through quantitative analysis, we provide empirical evidence to support the adoption of AI-powered personalisation techniques, ultimately leading to more engaging and satisfying user experiences on social media platforms.

Keywords: AI; content; Social Media; consumers; experience. (search for similar items in EconPapers)
JEL-codes: M30 M31 (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.icess.ase.ro/a-data-driven-approach-as ... -social-media-users/ (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rom:conase:v:6:y:2024:i:1:p:994-1003

Access Statistics for this article

More articles in PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON ECONOMICS AND SOCIAL SCIENCES from Bucharest University of Economic Studies, Romania Contact information at EDIRC.
Bibliographic data for series maintained by Zamfir Andreea ().