Name
Title | ||
|---|---|---|
Machine Learning Techniques for Brand-Influencer Matchmaking on the Instagram Social Network. |
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, itu0027s 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. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Social and Information Networks | Journal |
Volume | Citations | PageRank |
abs/1901.05949 | 1 | 0.38 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Taylor Sweet | 1 | 1 | 0.72 |
| Austin Rothwell | 2 | 1 | 0.38 |
| Xuan Luo | 3 | 30 | 10.84 |