Predicting Engagement with Sponsored Content Across Account Types on Instagram

In this work we explore how users interact with sponsored content on social media platforms by collecting and analyzing a large-scale dataset of sponsored Instagram posts. To maintain transparency, we favor robust statistical analysis and explainable models over deep learning techniques. Our pipeline categorizes Instagram accounts along multiple dimensions, including audience size and entity type. We complement this with semantic features extracted from post captions and hashtags, and use these elements to train regression models that forecast engagement. We validate our approach on a dataset comprising over 15M Instagram posts authored by over 700K accounts featuring sponsored content. Our analysis shows that per-post engagement is partly predictable and highly optimized when accounts are segmented by entity type or audience tier. Our models achieve competitive predictive power, while remaining fully transparent about which features drive engagement.