  With 1.3 billion users, Instagram (IG) has also become a business tool. IG
influencer marketing, expected to generate $33.25 billion in 2022, encourages
companies and influencers to create trending content. Various methods have been
proposed for predicting a post's popularity, i.e., how much engagement (e.g.,
Likes) it will generate. However, these methods are limited: first, they focus
on forecasting the likes, ignoring the number of comments, which became crucial
in 2021. Secondly, studies often use biased or limited data. Third, researchers
focused on Deep Learning models to increase predictive performance, which are
difficult to interpret. As a result, end-users can only estimate engagement
after a post is created, which is inefficient and expensive. A better approach
is to generate a post based on what people and IG like, e.g., by following
guidelines.
  In this work, we uncover part of the underlying mechanisms driving IG
engagement. To achieve this goal, we rely on statistical analysis and
interpretable models rather than Deep Learning (black-box) approaches. We
conduct extensive experiments using a worldwide dataset of 10 million posts
created by 34K global influencers in nine different categories. With our simple
yet powerful algorithms, we can predict engagement up to 94% of F1-Score,
making us comparable and even superior to Deep Learning-based method.
Furthermore, we propose a novel unsupervised algorithm for finding highly
engaging topics on IG. Thanks to our interpretable approaches, we conclude by
outlining guidelines for creating successful posts.
