Science University Research Symposium (SURS)
Publication Date
Fall 11-2025
College
College of Sciences & Mathematics
Department
Math and Computer Science, Department of
SURS Faculty Advisor
Dr. Ryan Fox
Presentation Type
Poster Presentation
Abstract
The majority of the world's over one billion active users depend on large-scale machine learning based social media platforms to personalize content through various methods of curation. Recommendation systems use many types of machine learning model including collaborative filtering, content-based filtering and deep learning to find what a specific user is likely to be interested in, and therefore increase user engagement. As beneficial as this is to the user experience, it has also generated a significant amount of concern about both bias in recommendations and the dissemination of false information on the web, as well as issues related to the digital well-being of users. This project will examine the computer science aspects of the development of recommendation systems (the process by which they are developed), explore some of the potential social impacts that may occur from the deployment of recommendation systems, and discuss how socially responsible algorithms can help create a better, healthier Internet.
Recommended Citation
Mallory-Smothers, Bethaney A., "“The Role of Machine Learning in Social Media Content Moderation”" (2025). Science University Research Symposium (SURS). 333.
https://repository.belmont.edu/surs/333
