mlsanityData to 7 Oct 2026

Follow Us and Become Famous! Insights and Guidelines From Instagram Engagement Mechanisms

arXiv 2301.06815 ↗First posted 17 Jan 2023cs.SI cs.CY cs.IRcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 18 of its 59 references that are in the map. First posted on arXiv 17 Jan 2023.

Builds on

18 of 59 references.

References · 18

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6 more
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1.

2 papers.