Intelligent algorithm selection for efficient update predictions in social media feeds

Abstract Efficiently synchronizing data with social media feeds while minimizing unnecessary requests presents a significant challenge in various fields. This paper investigates prediction algorithms for determining the optimal update intervals for Facebook and Twitter (now X) feeds, focusing on metrics such as delay (the time between a post’s publication and its retrieval) and requests per post. Variations in update intervals result in different algorithms producing varying results, making the selection of the most suitable algorithm for each feed crucial yet time-intensive. To address this, we propose three strategies for algorithm selection: baseline (applying a single algorithm to all feeds), optimum (identifying the best algorithm for each individual feed), and classification (selecting algorithms through a classification process based on each feed’s unique update patterns and context). Our strategies leverage various prediction algorithms, including static and adaptive algorithms, and inhomogeneous Poisson processes. We evaluate these strategies using real-world data from Facebook and Twitter, thoroughly assessing their performance in terms of delay and request efficiency. The findings demonstrate that the strategy Optimum effectively identifies the best algorithms for each feed, ensuring the highest prediction quality, though at a considerable computational cost. On the other hand, the strategy Classification offers superior runtime performance required to select algorithms. This research highlights the trade-offs between delay and request efficiency and presents a comprehensive solution for optimizing update predictions in social media feeds.