Abstract

Analyzing social media data has become increasingly important for TV channels as it offers valuable insights into audience preferences, engagement, and sentiment. It provides the feedback needed to align content with audience tastes, fostering greater viewer satisfaction and loyalty. In this paper, we propose a media analytics framework for analyzing audience engagement with posts made by TV channels on their social media platforms. We combine traditional statistical modeling, natural language processing tools, and explainable machine learning to predict engagement and derive feature importance. Our results on two platforms (Facebook and Instagram) for a major TV channel in Chile confirm the predictive capabilities of machine learning: Extreme Gradient Boosting achieved the best performance on Facebook, with a Mean Absolute Percentage Error (MAPE) of 25.14%, while the lowest MAPE for Instagram (16.56%) was obtained using Support Vector Regression. Furthermore, explainable machine learning techniques unveil interesting conclusions for decision-making, such as the importance of mentions of TV personalities, which can be automated using Named Entity Recognition.