Optimizing Broadcast Scheduling in Social Media: A Nonlinear Integer Programming Approach

This paper addresses the challenge of optimizing broadcast scheduling on social media platforms to enhance user engagement while mitigating information overload. I formulate the scheduling problem as a nonlinear integer programming model that captures the interplay among content creators’ posting strategies, follower behavior, and competing posts. the approach casts the broadcast planning task as a variant of the nonlinear knapsack problem, subject to constraints on the number of posts. To tackle the problem’s complexity, I employ a greedy allocation algorithm that iteratively improves scheduling decisions. I validate the model using X data by comparing several heuristic methods—uniform, peak, graveyard, and smart—against the proposed strategy. Experimental results demonstrate that the smart scheduling method, which targets periods of lower competition, yields significantly higher attention potential while reducing post frequency. These findings offer valuable insights for social media advertisers aiming to optimize content dissemination. Overall, the comprehensive analysis confirms improved performance.