Examination of Social Media Algorithms’ Ability to Know User Preferences

Algorithms are used by social media platforms to gather information on users to better suggest updated content to maximize the time spent on their online platforms. The information gathered from the users (browsing history, search history, engaged posts, engaged channels, etc.) is analyzed by these algorithms and used to predict what content and channels to best offer the users for further engagement. Information such as liking patterns, following tendencies, content engagement behavior and more are fed to these algorithms to personalize the user’s experience. The goal of this paper is to compare the effectiveness of the algorithms’ ability to profile and induce users, in this case employed by Instagram, Twitter, and YouTube by tracking several variables, including interaction time and interaction rates with posts, channels, likes, and follows. The results of this study show the performance of each platform algorithm over weeks of observation and how they stack in terms of engagement effectiveness.