Machine Learning Techniques for Brand-Influencer Matchmaking on the Instagram Social Network

TL;DR

This paper presents a machine learning-based system for matching brands with suitable influencers on Instagram by predicting partnership success through content similarity analysis.

Contribution

It introduces a novel algorithm that leverages modern machine learning techniques to effectively predict compatible brand-influencer pairings based on content similarity.

Findings

The algorithm accurately predicts successful brand-influencer matches.

Content similarity is a strong indicator of partnership potential.

The system improves the efficiency of influencer marketing strategies.

Abstract

The social media revolution has changed the way that brands interact with consumers. Instead of spending their advertising budget on interstate billboards, more and more companies are choosing to partner with so-called Internet "influencers" --- individuals who have gained a loyal following on online platforms for the high quality of the content they post. Unfortunately, it's not always easy for small brands to find the right influencer: someone who aligns with their corporate image and has not yet grown in popularity to the point of unaffordability. In this paper we sought to develop a system for brand-influencer matchmaking, harnessing the power and flexibility of modern machine learning techniques. The result is an algorithm that can predict the most fruitful brand-influencer partnerships based on the similarity of the content they post.