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

arXiv: https://arxiv.org/abs/2301.06815 · HTML: https://mlsanity.com/paper/2301.06815/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 59 references that are in the map.
- First posted on arXiv 17 Jan 2023.

## Builds on (most cited first, 18 of 18)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [Photo Aesthetics Ranking Network with Attributes and Content Adaptation](https://mlsanity.com/paper/1606.01621/) (2016)
- [SPICE: Semantic Pseudo-labeling for Image Clustering](https://mlsanity.com/paper/2103.09382/) (2021)
- [Detecting and Understanding Harmful Memes: A Survey](https://mlsanity.com/paper/2205.04274/) (2022)
- [Sentiment of Emojis](https://mlsanity.com/paper/1509.07761/) (2015)
- [On Explaining Multimodal Hateful Meme Detection Models](https://mlsanity.com/paper/2204.01734/) (2022)
- [What Twitter Profile and Posted Images Reveal About Depression and Anxiety](https://mlsanity.com/paper/1904.02670/) (2019)
- [Learning Functional Causal Models with Generative Neural Networks](https://mlsanity.com/paper/1709.05321/) (2017)
- [From Pixels to Sentiment: Fine-tuning CNNs for Visual Sentiment Prediction](https://mlsanity.com/paper/1604.03489/) (2016)
- [How to Become Instagram Famous: Post Popularity Prediction with Dual-Attention](https://mlsanity.com/paper/1809.09314/) (2018)
- [Intrinsic Image Popularity Assessment](https://mlsanity.com/paper/1907.01985/) (2019)
- [Are We All in a Truman Show? Spotting Instagram Crowdturfing through Self-Training](https://mlsanity.com/paper/2206.12904/) (2022)
- [How Impersonators Exploit Instagram to Generate Fake Engagement?](https://mlsanity.com/paper/2002.07173/) (2020)

## Built on by (most cited first, 2 of 2)

- [Social Honeypot for Humans: Luring People through Self-managed Instagram Pages](https://mlsanity.com/paper/2303.17946/) (2023)
- [Computational Studies in Influencer Marketing: A Systematic Literature Review](https://mlsanity.com/paper/2506.14602/) (2025)

## 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.