AI-Powered Personalization Mechanisms and Their Role in Enhancing Social Media User Experience
Keywords:
AI personalization, recommender systems, social media, user engagement, filter bubble, secondary data analysis, ethicsAbstract
Personalization features powered by artificial intelligence, such as recommendation systems and the content ranking tools, are important parts of the today's social media platforms. These features help manage big amounts of content, show which users what is most relevant to them, and influence how they experience the platform. This paper offers a more information’s analysis of existing research, industry reports, and regulatory studies to understand how AI-based personalization affects user interaction and their overall experience on social media. The paper has four main parts: first, it explains what personalization mechanisms are and their role in finding content, directing user attention, and supporting the platform's financial model. Second, it reviews recent studies on how personalization impacts user involvement, long-term use, and attention patterns. Third, it looks at the challenges that come with higher engagement, such as creating echo chambers, harming privacy and negatively affecting mental health. Fourth, it suggests a research-based, ethical way to create personalization systems that consider user experience, content variety, and safety. The findings show that AI personalization tends to boost temporary activity and time spent on the platform, but might reduce the variety of details users see and raise concerns about privacy and well-being. The paper ends with advice for researchers and platform creators and includes the list of datasets and sources for further study.
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